Analytical Hierarchy Process, Earned Value and other Project Management Themes – Fourth Edition by Ricardo Viana Vargas

ARTICLES

■ Using the Analytic Hierarchy Process (AHP) to Select and Prioritize Projects in a Portfolio ■ Blockchain is Changing How Companies Can Engage with Customers ■ Unleashing the Power of Blockchain in the Enterprise ■ How BlockChain Will Change Construction ■ Response to the Crisis: Build Perpetual Capabilities ■ Using Crises as a Catalist for Transformation ■ Identifying and Recovering Troubled Projects: How To Rescue Your Project From Its Failure ■ To Drive Innovation, You must Understand your Ecosystem ■ Transformation is Personal ■ Transforming Traditional Business Doesn’t Have to Be an Elusive Goal ■ Ten Questions to Help You Turn Strategy into Reality ■ The Innovator’s Mindset: Radical Can-Do ■ Great Strategies Need Great Delivery: The 10 Principles of Implementation Excellence ■ Sustainability Indicators to Assess Infrastructure Projects: Sector Disclosure to Interlock the GRI ■ The Sustainability Marker to Support the Project Selection Process: The Unops Case ■ Applying Neural Networks and Analogous Estimating to Determine the Project Budget ■ Modelling Line of Balance Schedules With Start-Finish Relationships ■ Determining the Mathematical ROI of a Project Management Implementation ■ Adopting the Quadratic Mean Process to Quantify the Qualitative Risk Analysis ■ Urgency: A Critical Factor in Project Planning ■ Avoiding Mistakes during the Team Acquisition: Assign the Right People to Their Right Functions using MBTI© ■ Using Earned Value Management Indexes as a Team Development Factor and a Compensation Tool ■ Earned Value Probabilistic Forecasting Using Monte Carlo Simulation ■ Earned Value Analysis in the Control of Projects: Success or Failure? ■ A New Approach to PMBOK® GUIDE 2000 ■ Managing an ERP Implementation Project using Basic Software Tools and Web Based Scheduling Control NEW

NEW

ABOUT THE AUTHOR Passionate about transforming ideas into action, Ricardo Vargas is a chief advocate in the project economy. Specializing in implementing innovative global initiatives, capital projects and product development, Ricardo has directed dozens of projects across industries and continents, managing more than $20 billion in global initiatives over the past 25 years.

More than two decades ago, he founded Macrosolutions, a global consulting firm with international operations in energy, infrastructure, IT, oil and finance. Ricardo holds a Ph.D. in Civil Engineering from Federal Fluminense University in Brazil and an undergraduate degree in Chemical Engineering, a master’s degree in Industrial Engineering from Federal University of Minas Gerais in Brazil.

FOURTH EDITION

Ricardo shares his expertise with millions of professionals around the globe through his “5 Minutes Podcast,” which he’s hosted since 2007. He has written 15 books on project management, risk and crisis management, and transformation, which have been translated into six languages and sold more than half a million copies. His influence on the sector was affirmed when he became the first Latin American to be elected Chairman of PMI.

A COMPENDIUM OF TECHNICAL ARTICLES

NEW

ANALYTICAL HIERARCHY PROCESS, BLOCKCHAIN, TRANSFORMATION, SUSTAINABILITY, NEURAL NETWORKS AND OTHER PROJECT MANAGEMENT THEMES A COMPENDIUM OF TECHNICAL ARTICLES Technical articles from 1999 to 2021 that will help understanding transformation, blockchain and sustainability in a project management context 4th edition

@rvvargas ricardo-vargas.com

ANALYTICAL HIERARCHY PROCESS, BLOCKCHAIN, TRANSFORMATION, SUSTAINABILITY, NEURAL NETWORKS AND OTHER PROJECT MANAGEMENT THEMES A COMPENDIUM OF TECHNICAL ARTICLES

Printed in the United States of America by CreateSpace. Please feel free to send comments on typographical, formatting, or other errors to info@ricardo-vargas.com.

This book is available to download at http://rvarg.as/compendium4

The Author Passionate about transforming ideas into action, Ricardo Vargas is a chief advocate in the project economy. Specializing in implementing innovative global initiatives, capital projects and product development, Ricardo has directed dozens of projects across industries and continents, managing more than $20 billion in global initiatives over the past 25 years. Ricardo shares his expertise and passion with millions of professionals around the globe through his “5 Minutes Podcast,” which he has hosted since 2007. He has written 15 books on project management, risk and crisis management and transformation, which have been translated into seven languages and sold more than half a million copies. His influence on the sector was affirmed when he became the first Latin American to be elected Chairman of the Project Management Institute. More than two decades ago, he founded Macrosolutions, a global consulting firm with international operations in energy, infrastructure, IT, oil and finance. His work as a venture capitalist and entrepreneur in artificial intelligence, blockchain, big data, chatbots and machine learning resulted in tools and products that have revolutionized how users bring agility and agile management into project management software. Between 2016 and 2020, Ricardo directed the Brightline Initiative, a Project Management Institute think tank that brings together other global leading organizations into a coalition that is dedicated to helping executives bridge the expensive, unproductive gap between strategic design and delivery. Ricardo developed and presented a Coursera Massive Open Online Course on “Bridging the Gap Between Strategy Design and Delivery,” viewed by more than 18,000 students around the globe. Under his leadership, the coalition launched the Brightline Transformation Compass: pioneering a human-centric approach to transformation.

Prior to his work with Brightline, Ricardo directed the United Nations Office for Project Services (UNOPS) Infrastructure and Project Management Group. Based in Copenhagen, Denmark and with operations in more than 120 countries, Ricardo and his team led more than 1,000 projects and $1.2 billion in refugee camp, rural road, maternity clinic and housing efforts and managed the sustainability program, responsible for the first Global Reporting Initiative (GRI) report in the United Nations’ system. Following his time at the UN, he collaborated to document the lives of the Zaatari refugee camp in Jordan and create the treatment for the documentary, “Zaatari: Memories of the Labyrinth,” a German-Brazilian production that is currently in several stream services globally. The film showcases the tragedy of war and the men, women and children seeking a different life. Ricardo started his career with two partners founding AeC in Brazil, where he was responsible for the services and training areas. A company with three initial employees grew and diversified the business from traditional software sales into outsourcing, project management, call center and IT services, becoming one of the leading technology groups in the country. When Ricardo decided to leave to make his work international, the AeC Group had 1,500 employees. T Today, Ricardo continues to enjoy the progress and success of his two former partners, who are now leading the current 40,000 employees. A dual citizen of Brazil and Portugal, Ricardo holds a Ph.D. in Civil Engineering from Federal Fluminense University in Brazil and an undergraduate degree in Chemical Engineering and a master’s degree in Industrial Engineering from Federal University of Minas Gerais in Brazil. He has received accreditation and key credentials in project management software and methodologies; Ricardo has also earned accolades ranging from the PMI Distinguished Award to the IPMA Achievement Award to the AACE Total Quality Management Award for his leadership globally. When he’s not traveling the world to share his passion for strategic transformation, Ricardo enjoys gardening and cooking with his wife and daughters in their sunny place in Portugal.

• www.ricardo-vargas.com • Instagram: @rvvargas • Twitter: @rvvargas • Linkedin: http://www.linkedin.com/in/ricardovargas

Introduction In 2014 I decided to create a collection of technical articles to share the main ideas and studies I have made on the field and keep track of some of my work’s evolution. Now I am releasing the fourth edition, including10 new articles I published in 2019 and 2020 during my time at Brightline Initiative. Time flies, and to remember, one of the classical articles was about the new approach to the PMBOK® Guide. For many new project managers, the article may be perceived as an old article. Still, the article’s concepts were a major driving force to the changes introduced in the third edition of the PMBOK® Guide that now shows the data flow diagram in Chapter 3. Another topic that I cover in several articles is Earned Value Management, the central issue of my master thesis. I also discuss human resource aspects and the Monte Carlo simulation applied to project management. A few years ago, I started studying Qualitative Risk Analysis, Sustainability in the project environment (that became the topic of my PhD) and Return over Investment of Project Offices and Analytical Hierarchy Process. One of the most intriguing articles was about Neural Networks. When I presented it in 2015, nobody thought about what would happen with Artificial Intelligence and Machine Learning. The process I introduced using Microsoft Excel and the Palisade Neural Tools is today present in most cloud-based solutions and, for sure, one of the hot topics for the upcoming future. Several of the most recent articles cover my work at Brightline Initiative in topics related to strategy implementation. Written in partnership with Edivandro Conforto, former Brightline Initiative Head of Strategic Research, they discuss the key elements to be in place to get things done.

I also published three articles on Blockchain in partnership with Don Tapscott, author of Blockchain Revolution and chairman of the Blockchain Research Institute. These articles were featured at Harvard Business Review and MIT Sloan Management Review. I hope you enjoy the fourth edition. Remember that the online version is free and it is available at http://rvarg.as/ compendium4 For sure, I intend to keep this book updated with new articles in the future. Enjoy your reading. Ricardo

Table of Contents 1. Using the Analytic Hierarchy Process (AHP) to Select and Prioritize Projects in a Portfolio.........................................................................................................................1 2. Blockchain is Changing How Companies Can Engage with Customers.................25 3. Unleashing the Power of Blockchain in the Enterprise..........................................31 4. How BlockChain Will Change Construction............................................................39 5. Response to the Crisis: Build Perpetual Capabilities.............................................43 6. Using Crises as a Catalist for Transformation........................................................49 7. Identifying and Recovering Troubled Projects: How To Rescue Your Project From Its Failure.....................................................................................................................57 8. To Drive Innovation, You must Understand your Ecosystem...................................71 9. Transformation is Personal.....................................................................................79 10. Transforming Traditional Business Doesn’t Have to Be an Elusive Goal.............85 11. Ten Questions to Help You Turn Strategy into Reality...........................................91 12. The Innovator’s Mindset: Radical Can-Do.............................................................97 13. Sustainability Indicators to Assess Infrastructure Projects................................115 14. The Sustainability Marker to Support the Project Selection Process: The Unops Case.........................................................................................................147 15. Applying Neural Networks and Analogous Estimating to Determine the Project Budget........................................................................................................................159 16. Modelling Line of Balance Schedules With Start-Finish Relationships.............187 17. Determining the Mathematical Roi of a Project Management Implementation.205

18. Adopting the Quadratic Mean Process to Quantify the Qualitative Risk Analysis...225 19. Urgency: A Critical Factor in Project Planning........................................................237 20. Avoiding Mistakes during the Team Acquisition: Assign the Right People to Their Right Functions using MBTI© ........................................................................................249 21. Using Earned Value Management Indexes as a Team Development Factor and a Compensation Tool........................................................................................................263 22. Earned Value Probabilistic Forecasting Using Monte Carlo Simulation.................277 23. Earned Value Analysis in the Control of Projects: Success or Failure?..................295 24. A New Approach to PMBOK® GUIDE 2000...............................................................307 25. Managing an ERP Implementation Project using Basic Software Tools and Web Based Scheduling Control.............................................................................................319

USING THE ANALYTIC HIERARCHY PROCESS (AHP) TO SELECT AND PRIORITIZE PROJECTS IN A PORTFOLIO

Accepted for publication at PMI Global Congress 2010 – North America Washington - DC – USA – 2010 Russian Project Management Conference (Русская конференция Управление проектами) Moscow – Russia - 2010

Using the Analytic Hierarchy Process (AHP) to Select and Prioritize Projects in a Portfolio

Abstract Related Podcasts

♫

Demystifying the Portfolio Management – Part 1/3: The Strategy’s Side http://rvarg.as/1j

♫ Demystifying the Portfolio Management – Part 2/3: The Criteria http://rvarg.as/1o

♫ Demystifying the Portfolio

Management – Part 3/3: A comparison and Decision http://rvarg.as/1s

♫ Why is so complex to be

Simple and Effective during the Project Selection? http://rvarg.as/6x

♫ Why is it So Hard to Define Clear Criteria for Selecting Projects? http://rvarg.as/6c

The objective of this paper is to present, discuss and apply the principles and techniques of the Analytic Hierarchy Process (AHP) in the prioritization and selection of projects in a portfolio. AHP is one of the main mathematical models currently available to support the decision theory. When looking into how organizations decide over which projects to execute, we can notice a constant desire to have clear, objective and mathematical criteria (HAAS & MEIXNER, 2005). However, decision making is, in its totality, a cognitive and mental process derived from the most possible adequate selection based on tangible and intangible criteria (SAATY, 2009), which are arbitrarily chosen by those who make the decisions. This paper also discusses the importance and some possible criteria for prioritizing projects, and by using a fictitious project prioritization example, it demonstrates AHP in a step-by-step manner, where the resulting priorities are shown and the possible inconsistencies are determined.

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The Importance of Project Selection and Prioritization One of the main challenges that organizations face today resides in their ability to choose the most correct and consistent alternatives in such a way that strategic alignment is maintained. Given any specific situation, making the right decisions is probably one of the most difficult challenges for science and technology (TRIANTAPHYLLOU, 2002). When we consider the ever changing dynamics of the current environment like we have never seen before, making the right choices based on adequate and aligned objectives constitutes a critical factor, even for organizational survival. Basically the prioritization of projects in a portfolio is nothing more than an ordering scheme based on a benefit cost relationship of each project. Projects with higher benefits, when compared to their costs, will have a higher priority. It’s important to observe that a benefit cost relationship does not necessarily mean the use of exclusive financial criteria like the widely known benefit cost ratio, but instead a broader concept of the reaped benefits from executing the project and their related efforts. Since organizations belong to a complex and varying context, often times even chaotic, the challenge of the aforementioned definition resides exactly in determining what are costs and benefits to any given organization. POSSIBLE DEFINITIONS FOR LOW COSTS

POSSIBLE DEFINITIONS FOR HIGH BENEFITS

Cheaper

More profitable

Less resource needs

Greater return of investment

Easier to be executed

Increase in the number of customers

Less complex

Increase in competitiveness

Less internal resistance

Improvements for the society

Less bureaucratic

Increase in Market Share

Less risks (threats)

Executives and shareholders happier

When analyzing the above table, one can observe that the different dimensions demonstrate how complex it is to come up with an exact translation for the meaning of low cost and high benefits. That is the reason why a unique criterion or translation is not viable enough to determine which project(s) should or should not be executed. Thus it is necessary to employ a multi-criteria analysis (TRIANTAPHYLLOU, 2002) which allows for decisions while taking into consideration the different dimensions and organizational needs altogether.

Using the Analytic Hierarchy Process (AHP) to Select and Prioritize Projects in a Portfolio

PMI’s Standard for Portfolio Management (PMI, 2008) says that the scope of a project portfolio must stem from the strategic objectives of the organization. These objectives must be aligned with the business scenario which in turn may be different for each organization. Consequently, there is no perfect model that covers the right criteria to be used for any type of organization when prioritizing and selecting its projects. The criteria to be used by the organization should be based on the values and preferences of its decision makers.

Current Criteria used in the Prioritization of Projects Although decisions are based on values and preferences of the decision makers, a set of criteria or specific objectives can be used while prioritizing projects and determining the real meaning of an optimal relationship between benefits and costs.

The main criteria groups are: Financial – A group of criteria with the objective of capturing the financial benefits of projects. They are directly associated with costs, productivity and profit measures. A few examples are:

• Return on Investment (ROI) – It is the percentage of the profit margin of the project. It allows comparing the financial return of projects with different investments and profits.

• Profit (currency) – The value (in currency) of the financial profit gained by the project. A project may have a smaller ROI but its nominal profit can be bigger.

• Net Present Value (NPV) – It is the difference between the project benefits and costs taking into consideration that all incomes and expenses are converted to be realized in the current date. In order to do so, it is necessary to bring all future values to the current date by using a given interest rate. That allows the assessment and comparison between projects which have future incomes and expenses from different time periods.

• Payback – It is the number of time in periods necessary to recover all of the original project investments.

• Financial Benefit / Cost Rate – It is the ratio between the present value of

the benefits and the present value of the costs. The higher the ratio, the more viable is the project under the perspective of benefit/costs.

Strategic – A group of criteria directly associated with the strategic objectives of the organization. The strategic criteria/objectives are determined by methods used to cascade corporate strategy like the Balanced Scorecard. They differ from the financial criteria because strategic criteria are specific for any organization. Organizations with different strategies will certainly have different prioritization criteria. Some examples may be to increase the capacity to compete in inter-

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national markets, to use eco-friendly practices, to optimize internal processes, to cut expenses in comparison with benchmarking competitors, to improve the reputation of products and services, etc. Risks (Threats) – It determines the level of risk tolerance that an organization accepts to execute a project. The threat-based risk assessment criteria can also incorporate the assessment of opportunities (HILSON, 2003). However, often times the assessment of opportunities that a project can yield are already covered and taken care of by the strategic criteria. Another equally possible perspective for this criterion entangles the organizational risk of not undertaking the project. Urgency – It determines the urgency level of the project. Projects considered to be urgent require immediate decision and action, and so they have a higher priority than projects that are not urgent. Stakeholder commitment – A group of criteria that aims to assess the level of stakeholder commitment towards the project. The higher the commitment to the project, the higher priority the project receives. Commitment may be assessed in a broad manner where all stakeholders are considered as a unique group, or it can be decomposed into different stakeholder groups, like for example:

• Customer commitment • Community commitment • Organizational commitment • Regulatory bodies • Project team commitment • Project manager commitment Technical Knowledge – It assesses the technical knowledge necessary to execute the project. The more technical knowledge readily available, the easier will it be to execute any given project and, consequently, it will cause the project to use fewer resources. It is important to note that, if it is necessary to establish criteria or objectives related to the learning and growth process, these criteria need to be associated with the organization’s strategic criteria, and not with any technical knowledge.

Analytic Hierarchy Process The multi-criteria programming made through the use of the Analytic Hierarchy Process is a technique for decision making in complex environments where many variables or criteria are considered in the prioritization and selection of alternatives or projects.

Using the Analytic Hierarchy Process (AHP) to Select and Prioritize Projects in a Portfolio

AHP was developed in the 70’s by Thomas L. Saaty and has been since then extensively studied, being currently used in decision making for complex scenarios, where people work together to make decisions when human perceptions, judgments and consequences have a long term repercussion (BHUSHAN & RAI, 2004). The application of AHP begins with a problem being decomposed into a hierarchy of criteria so as to be more easily analyzed and compared in an independent manner (Figure 1). After this logical hierarchy is constructed, the decision makers can systematically assess the alternatives by making pair-wise comparisons for each of the chosen criteria. This comparison may use concrete data from the alternatives or human judgments as a way to input subjacent information (SAATY, 2008). Goal

Criterion 01

Criterion 02

Alternative 01

Criterion 03

Alternative 02

Criterion 04

Alternative 03

Figure 1 – Example of a hierarchy of criteria/objectives

AHP transforms the comparisons, which are most of the times empirical, into numeric values that are further processed and compared. The weight of each factor allows the assessment of each one of the elements inside the defined hierarchy. This capability of converting empirical data into mathematical models is the main distinctive contribution of the AHP technique when contrasted to other comparing techniques. After all comparisons have been made, and the relative weights between each one of the criteria to be evaluated have been established, the numerical probability of each alternative is calculated. This probability determines the likelihood that the alternative has to fulfill the expected goal. The higher the probability, the better chances the alternative has to satisfy the final goal of the portfolio. The mathematical calculation involved in the AHP process may at first seem simple, but when dealing with more complex cases, the analyses and calculations become deeper and more exhaustive.

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The Comparison Scale (Saaty scale) The comparison between two elements using AHP can be done in different ways (TRIANTAPHYLLOU & MANN, 1995). However, the relative importance scale between two alternatives suggested by Saaty (SAATY, 2005) is the most widely used. Attributing values that vary from 1 to 9, the scale determines the relative importance of an alternative when compared to another alternative, as we can see in Table 1. SCALE

NUMERICAL RATING

RECIPROCAL

Extremely Preferred

1/9

Very strong to extremely

1/8

Very strongly preferred

1/7

Strongly to very strongly

1/6

Strongly preferred

1/5

Moderately to strongly

Moderately preferred

1/3

Equally to moderately

Equally preferred

Table 1 – Saaty’s Scale of Relative Importance (SAATY, 2005)

It is common to always use odd numbers from the table to make sure there is a reasonable distinction among the measurement points. The use of even numbers should only be adopted if there is a need for negotiation between the evaluators. When a natural consensus cannot be reached, it raises the need to determine a middle point as the negotiated solution (compromise) (SAATY, 1980). The comparison matrix is constructed from the Saaty scale (Table 2). Criteria 1

Criteria 2

Criteria 1

Numerical Rating

Criteria 2

1/Numerical Rating (Reciprocal)

Table 2 – Comparison Matrix (presuming that Criterion 1 dominates over Criterion 2)

An Example of the Application of AHP in a Portfolio In order to serve as an example of the AHP calculations for a prioritization of projects, the development of a fictitious decision model for the ACME Organization has been chosen. As the example is further developed, the concepts, terms and approaches to AHP will be discussed and analyzed. The first step to build the AHP model lies in the determination of the criteria that will be used. As already mentioned, each organization develops and structures

Using the Analytic Hierarchy Process (AHP) to Select and Prioritize Projects in a Portfolio

its own set of criteria, which in turn must be aligned to the strategic objectives of the organization. For our fictitious ACME organization, we will assume that a study has been made together with the Finance, Strategy Planning and Project Management areas on the criteria to be used. The following set of 12 (twelve) criteria has been accepted and grouped into 4 (four) categories, as shown on the hierarchy depicted in Figure 2.

Figure 2 – Hierarchy of Criteria for the fictitious ACME organization

Determining the Comparison Matrix, the Priority Vector (Eigen) and the Inconsistency After the hierarchy has been established, the criteria must be evaluated in pairs so as to determine the relative importance between them and their relative weight to the global goal. The evaluation begins by determining the relative weight of the initial criteria groups (Figure 3). Table 3 shows the relative weight data between the criteria that have been determined by ACME’s decision makers.

Figure 3 – ACME’s initial group of criteria/objectives STAKEHOLDERS C

FINANCIAL

STRATEGIC

OTHER CRITERIA

Stakeholders C

1/5

1/9

Financial

Table 3 – Comparison Matrix for ACME’s Group of Criteria

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STAKEHOLDERS C

FINANCIAL

STRATEGIC

OTHER CRITERIA

Strategic

Other Criteria

1/5

Table 3 – Comparison Matrix for ACME’s Group of Criteria

In order to interpret and give relative weights to each criterion, it is necessary to normalize the previous comparison matrix. The normalization is made by dividing each table value by the total the total column value (Table 4). STAKEHOLDERS C

FINANCIAL

STRATEGIC

OTHER CRITERIA

Stakeholders C

1/5

1/9

Financial

Strategic

Other Criteria

1/5

16.00

2.40

2.31

12.00

Total (Sum)

Results Stakeholders C

1/16 = 0.063

0.083

0.048

0.083

Financial

5/16 = 0,313

0.417

0.433

0.417

Strategic

9/16 = 0.563

0.417

0.433

0.417

Other Criteria

1/16 = 0.063

0.083

0.087

0.083

Table 4 – Comparison Matrix for ACME’s Group of Criteria after Normalization

The contribution of each criterion to the organizational goal is determined by calculations made using the priority vector (or Eigenvector). The Eigenvector shows the relative weights between each criterion it is obtained in an approximate manner by calculating the arithmetic average of all criteria, as depicted on Exhibit 10. We can observe that the sum of all values from the vector is always equal to one (1). The exact calculation of the Eigenvector is determined only on specific cases. This approximation is applied most of the times in order to simplify the calculation process, since the difference between the exact value and the approximate value is less than 10% (KOSTLAN, 1991). EIGENVECTOR (CALCULATION)

EIGENVECTOR

Stakeholders C

[0.063+0.083+0.048+0.083]/4 =0.0693

0.0693 (6,93%)

Financial

[0.313+0.417+0.433+0.417]/4 =0.3946

0.3946 (39,46%)

Strategic

[0.563+0.417+0.433+0.417]/4 =0.4571

0.4571 (45,71%)

Other Criteria

[0.063+0.083+0.087+0.083]/4 = 0.0789

0.0789 (7,89%)

Table 5 – Eigenvector Calculation (ACME)

For comparison purposes, a mathematical software application has been used to calculate the exact value for the Eigenvector through the use of potential matri-

Using the Analytic Hierarchy Process (AHP) to Select and Prioritize Projects in a Portfolio

ces. The results are shown on Table 6. APPROXIMATE EIGEN VECTOR

EXACT EIGEN VECTOR

DIFFERENCE (%)

Stakeholders C 0.0693 (6,93%)

0.0684 (6,84%)

0,0009 (1,32%)

Financial

0.3946 (39,46%)

0.3927 (39,27%)

0,0019 (0,48%)

Strategic

0.4571 (45,71%)

0.4604 (46,04%)

0,0033 (0,72%)

Other Criteria

0.0789 (7,89%)

0.0785 (7,85%)

0,0004 (0,51%)

Table 6 – Comparative Results for the Calculation of the Eigenvector – Approximate and Exact

It can be observed that the approximate and exact values are very close to each other, so the calculation of the exact vector requires a mathematical effort that can be exempted (KOSTLAN, 1991). The values found in the Eigenvector have a direct physical meaning in AHP. They determine the participation or weight of that criterion relative to the total result of the goal. For example, in our ACME organization, the strategic criteria have a weight of 46.04% (exact calculation of the Eigenvector) relative to the total goal. A positive evaluation on this factor contributes approximately 7 (seven) times more than a positive evaluation on the Stakeholder Commitment criterion (weight 6.84%). The next step is to look for any data inconsistencies. The objective is to capture enough information to determine whether the decision makers have been consistent in their choices (TEKNOMO, 2006). For example, if the decision makers affirm that the strategic criteria are more important than the financial criteria and that the financial criteria are more important than the stakeholder commitment criteria, it would be inconsistent to affirm that the stakeholder commitment criteria are more important than the strategic criteria (if A>B and B>C it would be inconsistent to say that A<C). The Eigenvector values used from this moment on will be based on the exact values, and not on the approximate values, because the exact values have been calculated and are thus available. 1

The inconsistency index is based on Maximum Eigenvalue, which is calculated by summing the product of each element in the Eigenvector (Table 5) by the respective column total of the original comparison matrix (Table 4). Table 7 demonstrates the calculation of Maximum Eigenvalue (λMax)1 .

Eigenvector

0.0684

0.3927

0.4604

0.0785

Total (Sum)

16.00

2.40

2.31

12.00

Maximum Eigenvalue (λMax)

[(0.0684 x 16.00)+(0.3927 x 2.40)+(0.4604 x 2.31) + (0.0785 x 12.00)] = 4.04 Table 7 – Calculation of Maximum Eigenvalue

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(𝝀𝝀𝑴𝑴𝑴𝑴𝑴𝑴 ) (𝝀𝝀𝑴𝑴𝑴𝑴𝑴𝑴)) (𝝀𝝀 (𝝀𝝀𝑴𝑴𝑴𝑴𝑴𝑴 𝑴𝑴𝑴𝑴𝑴𝑴 )

The calculation of the Consistency Index (SAATY, 2005) is given by the following Table 7 – Calculation of Maximum Eigenvalue formula Table 77 –– Calculation Calculation of of Maximum Maximum Eigenvalue Eigenvalue Table Table 7 – Calculation of Maximum Eigenvalue

𝜆𝜆!"# − 𝑛𝑛 − 𝑛𝑛 𝑛𝑛 𝐶𝐶𝐶𝐶 = 𝜆𝜆𝜆𝜆!"# !"# − 𝐶𝐶𝐶𝐶 = = 𝜆𝜆!"# 𝑛𝑛 −− 1 𝑛𝑛 𝐶𝐶𝐶𝐶 𝐶𝐶𝐶𝐶 = 𝑛𝑛 𝑛𝑛 − −1 1 𝑛𝑛 − 1 Where CI is the Consistency Index and n is the number of evaluated criteria. Where CI CI is is the the Consistency Consistency Index Index and and n n is is the the number number of of evaluated evaluated criteria. criteria. Where where CI is the Consistency Index andnumber n is the number of evaluated criteria. Where CI is the Consistency Index and n is the of evaluated criteria. For our ACME organization, the Consistency Index (CI) is For our our ACME ACME organization, organization, the the Consistency Consistency Index Index (CI) (CI) is is For For our ACME organization, the Consistency Index For our ACME organization, the Consistency Index (CI) is (CI) is 𝜆𝜆!"# − 𝑛𝑛 4.04 − 4 − 𝑛𝑛 𝑛𝑛 = 4.04 4.04 − − 4 = 0.0143 𝐶𝐶𝐶𝐶 = 𝜆𝜆𝜆𝜆!"# !"# − 4= = 4.04 = 0.0143 0.0143 𝐶𝐶𝐶𝐶 = = 𝜆𝜆!"# 𝑛𝑛 −− 1 𝑛𝑛 = 4 −−1 4 𝐶𝐶𝐶𝐶 0.0143 𝐶𝐶𝐶𝐶 = 𝑛𝑛 𝑛𝑛 − −1 1 = 4 4− −1 1 = 𝑛𝑛 − 1 4−1 In order to verify whether the Consistency Index (CI) is adequate, Saaty (SAATY, 2005) In order order to to verify verify whether whether the the Consistency Consistency Index Index (CI) (CI) is is adequate, adequate, Saaty Saaty (SAATY, (SAATY, 2005) 2005) In In order to verify the Consistency Index (CI) which is(CI)adequate, Saaty suggests what haswhether been called Consistency Rate (CR), is determined by the2005) ratio In order to verify whether the Consistency Index is adequate, Saaty(SAATY, (SAATY, suggests what what has has been been called called Consistency Consistency Rate Rate (CR), (CR), which which is is determined determined by by the the ratio ratio suggests suggests the what hassuggests been Index called Consistency RateConsistency (CR), whichIndex isRate determined by the ratio between Consistency thebeen Random (RI). Thewhich matrix be 2005) whatand has called Consistency (CR), iswill between the the Consistency Consistency Index Index and and the the Random Random Consistency Consistency Index Index (RI). (RI). The The matrix matrix will will be be between between the Consistency Index and the Consistency The matrix determined the ratio between the Consistency and the(RI). Random Consis-will be considered consistent ifby the resulting ratioRandom is less than 10%.IndexIndex considered consistent consistent ifif the the resulting resulting ratio ratio is is less less than than 10%. 10%. considered tency Indexif (RI). matrixratio will be consistent if the resulting ratio is considered consistent the The resulting is considered less than 10%. The calculation of the Consistency Index (SAATY, 2005) is given by the following formula lessof than The calculation calculation of the10%. Consistency Index Index (SAATY, (SAATY, 2005) 2005) is is given given by by the the following following formula formula The the Consistency The calculation of the Consistency Index (SAATY, 2005) is given by the following formula 𝐶𝐶𝐶𝐶 𝐶𝐶𝐶𝐶 < 0.1 ~ 10% 𝐶𝐶𝐶𝐶 = 𝐶𝐶𝐶𝐶 𝐶𝐶𝐶𝐶 < 𝐶𝐶𝐶𝐶 = = 𝑅𝑅𝑅𝑅 < 0.1 ~ 10% 0.1 ~ 10% 𝐶𝐶𝐶𝐶 𝐶𝐶𝐶𝐶 = 𝑅𝑅𝑅𝑅 𝑅𝑅𝑅𝑅 < 0.1 ~ 10% 𝑅𝑅𝑅𝑅 The RI value and based on theofnumber of evaluated as shown The RI value is fixed and is is fixed based onisthe number evaluated criteria, criteria, as shown on Table 8. The RI RI value value is is fixed fixed and and is is based based on on the the number number of of evaluated evaluated criteria, criteria, as as shown shown on on Table Table 8. 8. The The RI value is 8. onfixed Tableand 8. is based on the number of evaluated criteria, as shown on Table N 1 2 3 4 5 6 7 8 9 10 N 1 3 5 6 7 9 10 N 1 22 3 44 5 6 7 88 9 10 RI 0 0 0.58 0.9 1.12 1.24 1.32 1.41 1.45 1.49 N 1 2 3 4 5 6 7 8 9 10 RI 0 0.58 0.9 1.12 1.24 1.32 1.41 1.45 1.49 RI 0 00 1 0.58 0.9 1.24 1.32 1.45 1.49 N 0 2 30.9 4 1.12 6 (RI) (SAATY, 7 8 1.41 9 10 RI 0 1.12 5 Indices 1.24 1.32 1.45 1.49 Table 80.58 – Table of Random Consistency 2005)1.41 Table Table of Random Random Consistency Consistency Indices (RI) (SAATY, 2005) 0 888 ––– Table 0 1.12 Indices 1.24(RI) 1.32 2005) 1.41 1.45 Table of Table Table of0.58 Random0.9 Consistency Indices (RI) (SAATY, (SAATY, 2005) organization, the for the initial group Table 8 – Consistency Table of RandomRate Consistency Indices (RI) criteria (SAATY, 2005)

1.49

For our ACME is For our our ACME ACME organization, organization, the the Consistency Consistency Rate Rate for for the the initial initial criteria criteria group group is is For For our ACMEFor organization, the Consistency Rate for the initial criteria groupgroup is our ACME organization, the Consistency Rate for the initial criteria is 0.0143 0.0143 = 0.0159 = 1,59% 𝐶𝐶𝐶𝐶 = 0.0143 𝐶𝐶𝐶𝐶 = = 0.0143 = 0.0159 0.0159 = = 1,59% 1,59% 0.9 = 𝐶𝐶𝐶𝐶 𝐶𝐶𝐶𝐶 = 0.9 = 1,59% 0.9 = 0.0159 0.9 Since its value is less than 10%, the matrix can be considered to be consistent. Since its its value value is less less than 10%, 10%, the matrix can matrix be considered considered to be be consistent. consistent. Since its value is lessthe than 10%, can the can be considered to be consistent. Since is than the matrix can be to Since its value is less than 10%, matrix be considered to be consistent. The priority criteria results for the first level can be seen in Figure 4. The priority criteria results the first can be seen in 4. Figure 4. The priority priority criteria criteria results for the the firstfor level can level be seen seen in Figure Figure The results for first level can be in 4. The priority criteria results for the first level can be seen in Figure 4.

Figure 4 – Results of the Comparison Matrix for ACME’s Criteria Group, demonstrating the contribution of ACME’s each criterion the goal defined for the Figure 4 – Results of the Comparison Matrix for Criteriato Group, demonstrating the organization contribution of each criterion to the Figure 44 –– Results Results of of the the Comparison Comparison Matrix Matrix for for ACME’s ACME’s Criteria Criteria Group, Group, demonstrating demonstrating the the contribution contribution of of each criterion criterion to the the Figure goal defined for the organization Figure 4 – ResultsBy of the Comparison Matrix4for ACME’s Criteria Group, demonstrating the contribution of each each criterion to to the looking at Figure and the Eigenvector values, it is evident that the Strategic goal defined for for the organization organization goal goal defined defined for the the organization

a contribution of 46.04% to the goal, whereas the Com-Criteria By looking atCriteria Figurehave 4 and the Eigenvector values, it is evident thatStakeholder the Strategic By looking looking at at Figure Figure criteria and contributes the Eigenvector Eigenvector values, it is is evident evident that that the the Strategic Strategic Criteria Criteria By 44 and and the values, it with 6.84% to the goal. By looking atmitment Figure of 4 the Eigenvector values, it is evident that Commitment the Strategic Criteria have a contribution 46.04% to the goal, whereas the Stakeholder criteria have aa contribution contribution of of 46.04% 46.04% to to the the goal, goal, whereas whereas the the Stakeholder Stakeholder Commitment Commitment criteria criteria have have a contribution of to 46.04% to the goal, whereas the Stakeholder Commitment criteria contributes with 6.84% the goal. contributes with with 6.84% 6.84% to to the the goal. goal. contributes contributes with 6.84% to the goal.

Using the Analytic Hierarchy Process (AHP) to Select and Prioritize Projects in a Portfolio

Other Calculations Involving the Chosen Criteria Just like it was done with the initial criteria group for the ACME organization, it is necessary to evaluate the criteria’s relative weights for the second level of the hierarchy (Figure 5). This process is executed just like the step to evaluate the first level of the hierarchy (Criteria Group) as it was shown above.

Figure 5 – Hierarchy of criteria for the fictitious ACME organization highlighting the second hierarchy level

The following tables show the comparison matrices for the criteria with the pairwise comparisons already taken by the decision makers. STAKEHOLDERS COMMITMENT CRITERIA TEAM COMMITMENT

ORGANIZATIONAL COMMITMENT

PROJECT MANAGER COMMITMENT

1/5

1/3

1/9

Team Commitment Organizational Commitment Project Manager Commitment

Table 9 – Comparison Matrix – Stakeholder Commitment Criteria FINANCIAL CRITERIA RETURN OF INVESTMENT

PROFIT (US$)

NET PRESENT VALUE

Return of Investment

1/5

Profit (US$)

Net Present Value

Table 10 – Comparison Matrix – Financial Criteria

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STRATEGIC CRITERIA COMPETE IN INTERNATIONAL MARKETS

INTERNAL PROCESSES

REPUTATION

Internal Processes

1/7

1/5

Reputation

1/3

Compete in Intern. Markets

Table 11 – Comparison Matrix – Strategic Criteria

OTHER CRITERIA

Lower Risks for the Organization Urgency Internal Technical Knowledge

LOWER RISKS FOR THE ORGANIZATION

URGENCY

INTERNAL TECHNICAL KNOWLEDGE

1/3

1/5

1/7

Table 12 – Comparison Matrix – Other Criteria

The following charts demonstrate the priority results for the sub-criteria for each one of the criteria groups2 and their respective inconsistency indices. We can observe that none of the criterion demonstrates any inconsistency above tolerable limits.

Figure 6 – Priority results for the Stakeholder Commitment Criteria

Figure 7 – Priority results for the Financial Criteria

Figure 8 – Priority results for the Strategic Criteria

Figure 9 – Priority results for the Other Criteria

The data have been simulated and calculated using ExpertChoice 11.5 for Windows, available at www.expertchoice.com 2

Using the Analytic Hierarchy Process (AHP) to Select and Prioritize Projects in a Portfolio

The global priority for each criterion is determined by the result of the multiplication of each priority on the first level by its respective priority on the second level. The results are shown on the hierarchy depicted on Figure 10. We can also see that the sum of the weights of all twelve (12) factors is equal to 1.

Figure 10 – Hierarchy of criteria for the fictitious ACME organization with global priorities for each criterion

Evaluating Candidate Projects for the Portfolio After having structured the tree and established the priority criteria, it is now possible to determine how each one of the candidate projects fits the chosen criteria. In the same manner that the criteria prioritization has been made, the candidate projects are pair-wisely compared considering every established criteria. For our ACME organization, six (6) different projects have been identified and must then be prioritized. The fictitious projects are:

• Move to a new office facility • New ERP system • Opening of an office in China • Development of a new Product aiming at the International Market • IT infrastructure Outsourcing • New local Marketing Campaign In order to apply AHP, the decision makers from ACME organization have compared six projects taking into consideration every one of the twelve established criteria. The results are shown in the following twelve tables.

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TEAM COMMITMENT NEW OFFICE

ERP IMPLEM.

CHINESE OFFICE

INTERN. PRODUCT

IT OUTSOURC.

LOCAL CAMPAIGN

1/3

ERP Implementation

1/5

1/7

1/3

Chinese Office

1/3

IT Outsourcing

1/9

1/7

1/5

1/3

New Local Campaign

1/7

1/3

1/5

New Office

International Product

Table 13 – Projects Comparison Matrix for the Team Commitment Criterion ORGANIZATIONAL COMMITMENT NEW OFFICE

ERP IMPLEM.

CHINESE OFFICE

INTERN. PRODUCT

IT OUTSOURC.

LOCAL CAMPAIGN

1/9

1/5

1/3

1/9

1/7

1/3

Chinese Office

International Product

1/3

IT Outsourcing

1/5

1/7

1/9

1/3

New Local Campaign

1/3

1/7

New Office ERP Implementation

Table 14 – Projects Comparison Matrix for the Organization Commitment Criterion

PROJECT MANAGER COMMITMENT NEW OFFICE

ERP IMPLEM.

CHINESE OFFICE

INTERN. PRODUCT

IT OUTSOURC.

LOCAL CAMPAIGN

1/3

1/7

1/9

1/7

1/3

Chinese Office

International Product

IT Outsourcing

1/5

1/3

1/7

1/5

New Local Campaign

1/3

1/7

1/9

New Office ERP Implementation

Table 15 – Projects Comparison Matrix for the Project Manager Commitment Criterion RETURN ON INVESTMENT NEW OFFICE

ERP IMPLEM.

CHINESE OFFICE

INTERN. PRODUCT

IT OUTSOURC.

LOCAL CAMPAIGN

New Office

1/3

1/7

1/9

1/3

ERP Implementation

1/9

1/3

Chinese Office

1/3

International Product

IT Outsourcing

1/7

1/3

New Local Campaign

1/5

Table 16 – Projects Comparison Matrix for the Return On Investment Criterion

Using the Analytic Hierarchy Process (AHP) to Select and Prioritize Projects in a Portfolio PROFIT (US$) NEW OFFICE

ERP IMPLEM.

CHINESE OFFICE

INTERN. PRODUCT

IT OUTSOURC.

LOCAL CAMPAIGN

New Office

1/7

1/9

1/5

1/3

ERP Implementation

1/7

1/9

1/3

1/5

Chinese Office

1/3

International Product

IT Outsourcing

1/7

1/9

1/3

New Local Campaign

1/5

Table 17 – Projects Comparison Matrix for the Profit (US$) Criterion NET PRESENT VALUE NEW OFFICE

ERP IMPLEM.

CHINESE OFFICE

INTERN. PRODUCT

IT OUTSOURC.

LOCAL CAMPAIGN

New Office

1/3

1/5

1/7

1/3

ERP Implementation

1/5

1/7

1/3

Chinese Office

1/3

International Product

IT Outsourcing

1/5

1/3

New Local Campaign

1/3

1/7

Table 18 – Projects Comparison Matrix for the Net Present Value Criterion IMPROVES ABILITY TO COMPETE IN INTERNATIONAL MARKETS NEW OFFICE

ERP IMPLEM.

CHINESE OFFICE

INTERN. PRODUCT

IT OUTSOURC.

LOCAL CAMPAIGN

1/9

1/7

1/3

1/9

1/3

Chinese Office

International Product

IT Outsourcing

1/5

1/9

New Local Campaign

1/5

1/3

1/9

1/3

New Office ERP Implementation

Table 19 – Projects Comparison Matrix for the Ability to Compete in International Markets Criterion IMPROVES INTERNAL PROCESSES NEW OFFICE

ERP IMPLEM.

CHINESE OFFICE

INTERN. PRODUCT

IT OUTSOURC.

LOCAL CAMPAIGN

New Office

1/5

ERP Implementation

Chinese Office

1/3

1/7

International Product

1/5

1/7

1/3

1/7

IT Outsourcing New Local Campaign

Table 20 – Projects Comparison Matrix for the Improves Internal Processes Criterion

ricardo-vargas.com IMPROVES REPUTATION NEW OFFICE

ERP IMPLEM.

CHINESE OFFICE

INTERN. PRODUCT

IT OUTSOURC.

LOCAL CAMPAIGN

New Office

1/3

1/7

1/5

1/7

ERP Implementation

1/9

1/5

1/7

Chinese Office

International Product

1/3

1/5

1/7

1/9

IT Outsourcing New Local Campaign

Table 21 – Projects Comparison Matrix for the Improves Reputation Criterion LOWER RISKS (THREATS) FOR THE ORGANIZATION NEW OFFICE

ERP IMPLEM.

CHINESE OFFICE

INTERN. PRODUCT

IT OUTSOURC.

LOCAL CAMPAIGN

ERP Implementation

1/5

1/7

Chinese Office

1/7

1/5

1/3

1/9

International Product

1/3

1/7

IT Outsourcing

1/5

1/3

1/5

1/9

New Office

New Local Campaign

Table 22 – Projects Comparison Matrix for the Lower Risks (Threats) for the Organization Criterion URGENCY NEW OFFICE

ERP IMPLEM.

CHINESE OFFICE

INTERN. PRODUCT

IT OUTSOURC.

LOCAL CAMPAIGN

New Office

1/3

1/5

1/7

ERP Implementation

1/7

1/9

Chinese Office

1/3

International Product

1/3

1/5

1/7

1/3

1/7

IT Outsourcing New Local Campaign

Table 23 – Projects Comparison Matrix for the Urgency Criterion INTERNAL TECHNICAL KNOWLEDGE NEW OFFICE

ERP IMPLEM.

CHINESE OFFICE

INTERN. PRODUCT

IT OUTSOURC.

LOCAL CAMPAIGN

ERP Implementation

1/9

1/3

1/5

1/9

Chinese Office

1/9

International Product

1/9

1/3

1/9

IT Outsourcing

1/9

New Local Campaign

1/3

New Office

Table 24 – Projects Comparison Matrix for the Internal Technical Knowledge Criterion

Using the Analytic Hierarchy Process (AHP) to Select and Prioritize Projects in a Portfolio

After calculating all priorities and inconsistency indices, it is possible to determine the relative weight of each project for each criterion, as we can see in the following twelve (12) charts (one chart for each criterion).

Figure 11 – Priority results for the projects according to the Team Commitment Criterion

Figure 12 – Priority results for the projects according to the Organization Commitment Criterion

Figure 13 – Priority results for the projects according to the Project Manager Commitment Criterion

Figure 14 – Priority results for the projects according to the Return On Investment Criterion

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Figure 15 – Priority results for the projects according to the Profit (US$) Criterion

Figure 16 – Priority results for the projects according to the Net Present Value Criterion

Figure 17 – Priority results for the projects according to the Ability to compete in International Markets Criterion

Figure 18 – Priority results for the projects according to the Improves Internal Processes Criterion

Figure 19 –Priority results for the projects according to the Improves Reputation Criterion

Using the Analytic Hierarchy Process (AHP) to Select and Prioritize Projects in a Portfolio

Figure 20 – Priority results for the projects according to the Lower Organizational Risks (Threats) Criterion

Figure 21 – Priority results for the projects according to the Urgency Criterion

Figure 22 – Priority results for the projects according to the Internal Technical Knowledge Criterion

The crossing product of all project evaluations using all criteria determines the final priority for each project in relation to the desired goal. The mechanism for calculating the final priority is to sum the products of the multiplication of each criterion’s priority weight by its alternative weight. In order to exemplify this process, Table 25 shows the calculation process for the alternative “Move to a New Office” PRIORITY WEIGHT

ALTERNATIVE WEIGHT

PRODUCT

Team Commitment

0,0122

0,2968

0,0036

Organizational Commitment

0,0048

0,0993

0,0005

Project Manager Commitment

0,0514

0,1586

0,0082

Return on Investment (ROI)

0,0357

0,0296

0,0011

Profit (US$)

0,1785

0,0315

0,0056

Net Present Value

0,1785

0,0366

0,0065

CRITERIA

Table 25 – Final priority evaluation of the Project Move to a New Office according to the established and weighted criteria for the ACME Organization

ricardo-vargas.com PRIORITY WEIGHT

ALTERNATIVE WEIGHT

PRODUCT

Improves Ability to Compete in International Markets

0,2988

0,1033

0,0309

Improves Internal Processes

0,0331

0,1903

0,0063

Improves Reputation

0,1284

0,0421

0,0054

Lower Risks (Threats) for the Organization

0,0219

0,2994

0,0066

Urgency

0,0056

0,0553

0,0003

Internal Technical Knowledge

0,0510

0,4796

0,0243

CRITERIA

Results

0,0992

Table 25 – Final priority evaluation of the Project Move to a New Office according to the established and weighted criteria for the ACME Organization

The same process should be repeated for the other five (5) projects. The final results for all projects are shown in Figure 23.

Figure 23 – Final priority results for the for ACME’s portfolio of projects

Figure 23 shows that the project with the highest level of adherence to the defined goal is “Development of a New Product for the International Market”. It contributes with 34.39% (0.3439). In order to better illustrate the importance of the difference between the weights and priorities of each project, this project contributes with about three (3) times more to the goal than the New Local Marketing Campaign project, which contributes with only 13.1% (0.131) to the global goal.

Conclusion AHP has been attracting the interest of many researchers mainly due to the mathematical features of the method and the fact that data entry is fairly simple to be produced (TRIANTAPHYLLOU & MANN, 1995). Its simplicity is characterized by the pair-wise comparison of the alternatives according to specific criteria (VARGAS, 1990). Its application to select projects for the portfolio allows the decision makers to have a specific and mathematical decision support tool. This tool not only sup-

Using the Analytic Hierarchy Process (AHP) to Select and Prioritize Projects in a Portfolio

ports and qualifies the decisions, but also enables the decision makers to justify their choices, as well as simulate possible results. The use of AHP also presumes the utilization of a software application tailored specifically to perform the mathematical calculations. In this paper, the intention has been to show the main calculations performed during the analysis, enabling project managers to have an adequate understanding of the technique, as well as the complexity involved to make the calculations by hand (in case software applications can’t be used). Another important aspect is the quality of the evaluations made by the decision makers (COYLE, 2004). For a decision to be the most adequate possible, it must be consistent and coherent with organizational results. We saw that the coherence of the results can be calculated by the inconsistency index. However, the inconsistency index allows only the evaluation of the consistency and regularity of the opinions from the decision makers, and not whether these opinions are the most adequate for a specific organizational context. Finally, it is important to emphasize that decision making presumes a broader and more complex understanding of the context than the use of any specific technique. It predicates that a decision about a portfolio is a fruit of negotiation, human aspects and strategic analysis, where methods like AHP favor and guide the execution of the work, but they cannot and must not be used as a universal criteria.

References BHUSHAN, N. & RAI, K. (2004). Strategic Decision Making: Applying the Analytic Hierarchy Process. New York: Springer. COYLE, G. (2004). The Analytic Hierarchy Process. New York: Pearson Educational. HAAS, R. & MEIXNER, O. (2005). An Illustrated Guide To Analytic Hierarchy Process. Vienna: University of Natural Resources and Applied Life Sciences. KOSTLAN, E. (1991). Statistical Complexity of Dominant Eigenvector Calculation. Hawaii: Journal of Complexity Volume 7, Issue 4, December 1991, Pages 371-379. Available at http://www.developmentserver.com/randompolynomials/scdec/paper.pdf HILLSON, D. (2002). Use a Risk Breakdown Structure (RBS) to Understand Your Risks. San Antonio: PMI Global Congress and Symposium. PMI (2008). The Standard for Portfolio Management: Second Edition. Newtown Square: Project Management Institute.

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SAATY, T. L (1980). The Analytic Hierarchy Process. New York: McGraw-Hill International. SAATY, T. L. (2005). Theory and Applications of the Analytic Network Process: Decision Making with Benefits, Opportunities, Costs, and Risks. Pittsburgh: RWS Publications. SAATY, T. L. (2008). Relative Measurement and its Generalization in Decision Making: Why Pairwise Comparisons are Central in Mathematics for the Measurement of Intangible Factors - The Analytic Hierarchy/Network Process. Madrid: Review of the Royal Spanish Academy of Sciences, Series A, Mathematics. Available at http:// www.rac.es/ficheros/doc/00576.PDF SAATY, T. L. (2009). Extending the Measurement of Tangibles to Intangibles. International Journal of Information Technology & Decision Making, Vol. 8, No. 1, pp. 7-27, 2009. Available at SSRN: http://ssrn.com/abstract=1483438 TEKNOMO, K. (2006). Analytic Hierarchy Process (AHP) Tutorial. Available at http:// people.revoledu.com/kardi/tutorial/ahp/ TRIANTAPHYLLOU, E. & MANN S. H. (1995). Using The Analytic Hierarchy Process For Decision Making in Engineering Applications: Some Challenges. International Journal of Industrial Engineering: Applications and Practice, Vol. 2, No. 1, pp. 35-44, 1995. Available at http://www.csc.lsu.edu/trianta/Journal_PAPERS1/AHPapls1.pdf TRIANTAPHYLLOU, E. (2002). Multi-Criteria Decision Making Methods: A comparative Study. New York: Springer. VARGAS, L.G. (1990). An Overview of the Analytic Hierarchy Process and its Applications. European Journal of Operational Research, 48, 2-8.

BLOCKCHAIN IS CHANGING HOW COMPANIES CAN ENGAGE WITH CUSTOMERS

Ricardo Viana Vargas Executive Director – Brightline Initiative Don Tapscott Chairman - Blockchain Research Institute

Published in Harvard Business Review Cambridge - USA - 2021

Blockchain is Changing How Companies Can Engage with Customers

Disruptive technologies like blockchain and artificial intelligence are powerful catalysts for change — they can change not just how a company works, but what it’s possible for it to do. Consider the case of Interac Corp., Canada’s largest digital payments company, which partnered with an energy company to change consumer habits through a blockchain-powered incentive program that saved the energy company money and created more efficiency throughout the system. This case offers a few lessons for leaders: 1) tech like blockchain can transform sectors, 2) collaboration is an efficient way to experiment, 3) enterprise-wide engagement builds momentum for innovation, and 4) social purpose is a powerful driver of change

Changing how an organization conducts its core business — such as launching new services or reorganizing for greater efficiency — is really hard. During the first era of the internet, we saw the tremendous strain transformation placed on companies, industries, and entire economies. Today, employees often don’t want change because they’ve grown accustomed to certain norms. Executives often don’t want change, because they’re weary of the costs and risks associated with embracing innovation. Yet, despite the obstacles, transformation is important for any company looking to survive and thrive in this era of digital disruption. While market forces may compel companies to transform themselves, disruptive technologies like blockchain and artificial intelligence provide a powerful catalyst for change. Just as leading organizations integrated the web into the fabric of their businesses during the internet era, modern companies need to adapt with today’s emerging technologies in mind. We’re both particularly interested in blockchain, and in understanding its opportunities. Don is the co-founder and executive chairman of the Blockchain Research Institute, a global think tank with more than 100 member organizations committed to exploring blockchain strategy and reimagining enterprise architecture. Ricardo is the executive director of the Brightline Initiative — a founding member of the Blockchain Research Institute — where he works with global decision makers to understand emerging technologies like blockchain. Interac, another founding (and, for the record, dues paying) member of the Blockchain Research Institute, is Canada’s largest digital payments company. It has rapidly adopted emerging technologies to expand into new industries and spearhead digital transformation. Interac was founded almost 40 years ago as a nonprofit association responsible for running Canada’s interbank network. But in early 2018, as the revolution in digital banking accelerated, it made a strategic

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shift to for-profit status, freeing the company to innovate and invest in new ways. Specifically, its embrace of blockchain — which allows assets to be exchanged with real-time settlement as certain conditions are met — is helping Interac to transform entire industries, and itself in the process.

Sparking an Energy Revolution Formed in 1985, Interac’s network is today used by more than 16 million Canadians for everything from consumer payments to inter-bank transfers. Competition for market share is fierce. To expand its value proposition, Interac has started to diversify its business practices, looking for opportunities to step into an emerging market and lead industry transformations. It recognized one such opportunity in the energy sector, where the growing popularity of electric vehicles (EVs) is creating a new surge problem. In normal times, peak demand for electric vehicle chargers is typically between 5 p.m. and 7 p.m., says Oscar Roque, associate vice president of innovation, research, and emerging solutions at Interac. Commuters in Toronto, for example, get home from work around the same time and all set their cars to charge, putting a significant strain on grid capacity. As EV ownership grows, peak demand from energy-hungry EV chargers could outstrip supply, forcing utilities to invest in upgrading capacity — at significant cost. Distributing the same load over time would solve the problem, but utility companies lacked a means to incentivize consumers to change behavior. That’s where Interac came in. Working with Alectra Utilities and IBM, the company designed a blockchain-based solution for optimizing energy consumption. The pilot invited customers — all of whom were chosen because they owned solar panels, electric home batteries, or electric vehicles — to download an app that offered real-time monetary incentives for making energy-efficient choices. The app addressed both the supply and demand sides of the surge problem. First, it incentivized customers to schedule charging at off-peak hours — sometime in the middle of the night — by rewarding those consumers with digital tokens called “sparks” that they could convert into Canadian dollars through Interac’s network. Second, in times of peak energy demand, Alectra could also harness its customers’ “virtual power plant” capabilities, and pay residential homeowners for feeding power into the grid directly from their rooftop solar installations and lithium-ion storage batteries. For both processes, the blockchain infrastructure created an auditable trail of verifiable data for each kilowatt of energy saved or delivered and every token awarded. It also ensured that homeowners could redeem their tokens.

Blockchain is Changing How Companies Can Engage with Customers

The concept is a game-changer for both Interac and Alectra — and one that illustrates how blockchains enable powerful incentive structures that promote collaborative behaviors. “We are going through a generational change, and the utility sector is being fundamentally disrupted,” says Neetika Sathe, vice president of Alectra’s Green Energy and Technology Centre. “Consumers are no longer passive consumer of electricity,” says Sathe. “They want to be prosumers — producing, selling, and buying electricity according to their preferences.” The success of the Alectra program is evidence that real-time monetary incentives are potent drivers of changes in consumer behavior. According to Roque, “It’s like a loyalty program with social and environmental benefits.”

Leadership and Engagement for Enterprise Transformation Enterprise transformation is not just about modernizing an organization’s technology and tools (though the Interac partnership with Alectra did require that, too). It requires profound organizational commitment to changing how work gets done — how teams collaborate, how a company relates to its customers, how it innovates. At Interac, that meant building new teams, creating new opportunities for internal collaboration, and fostering a culture comfortable with the tumultuous environment of emerging tech. For example, to drive its blockchain projects forward, Interac built a lean internal team that focuses on emerging technologies and solutions, and rapidly prototypes and tests new ideas and business models. “We concentrate on research, strategy, and new product development,” says Roque, who leads the team. It also has clearly defined limits — when it comes to building the technology components, it sources external consultants and vendors to help. Successful transformations involve building a movement that aligns inside-out and outside-in approaches — a movement led by committed senior leaders inside the organization, and authored and driven by large numbers of employees, including the management and frontline team members with a stake in the organization’s success. “When you open up the process, you quickly find you’ve got a few dozen people who are passionate and committed to the journey,” said Roque. “That means you have a few dozen people that are advocates for the success of the project.” In other words, invest in getting people engaged in the journey, and they will all become champions when it comes to project execution and delivery.

Key Takeaways for Enterprise Leaders Industries are facing disruption, spurred on by new digital technologies like

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blockchain. For company executives who see the necessity of transformation, there are a few key insights we can draw from companies like Interac and Alectra.

Blockchain has the power to transform whole business ecosystems. Interac’s work with blockchain to create platforms and applications that incentivize certain behaviors weren’t confined to energy — it also started new pilots in the health and wellness sector. Partnering with the Heart & Stroke Foundation, Interac developed a program in which participants are rewarded with vouchers to redeem at major Canadian stores when health milestones are met or maintained. Using blockchain, business ecosystems can embed incentive mechanisms within themselves to promote more sustainable behaviors.

Collaboration is an efficient way to test emerging technologies. Combining complementary capabilities from multiple organizations saves time and other resources. While the internal innovation team at Interac drives business strategy and architecture, it leverages blockchain expertise and capabilities from leaders like IBM and stakeholders like Alectra. By collaborating on an industry level, companies can also split the costs and reduce the risks of innovation, while remaining party to the insights and benefits.

Enterprise-wide engagement creates momentum for innovation. With the world of digital payments evolving at a breakneck pace, Roque recognized that he had to play an active, positive role in Interac’s transformation. Topdown leadership alone, however, was not enough. The ability of team members to self-select for strategic change projects helped to keep them focused on their performance as a team.

Social purpose is a powerful driver of change. Roque argued that youthful generations are driving a fundamental shift in how companies think about value creation and progress. “People, planet, and profit are our guiding principles,” he said. “Everything that we do and think about must be driven by these three angles.” As the modern generation becomes more concerned with issues like climate change, market forces will start to select and favor more sustainable companies. Building modern business ecosystems with incentive structures that promote sustainability will ensure long-term market success.

UNLEASHING THE POWER OF BLOCKCHAIN IN THE ENTERPRISE BLOCKCHAIN IS EMERGING AS A FOUNDATIONAL TECHNOLOGY, BUT THERE IS STILL GROUNDWORK TO BE DONE ON STANDARDS DEVELOPMENT AND THE GOVERNANCE OF CHANGE — AND DOING THAT WORK IS CRITICAL TO ENTERPRISE STRATEGY.

Ricardo Viana Vargas Executive Director – Brightline Initiative Don Tapscott Chairman - Blockchain Research Institute

Published in MIT Sloan Management Review Cambridge - USA - 2019

Unleashing the Power of Blockchain in the Enterprise

Too many of us take for granted how well the internet worked for business in the early 1990s. By the time the National Science Foundation lifted commercial restrictions on internet use, Tim Berners-Lee had already launched the World Wide Web, and Marc Andreessen had developed the Mosaic web browser.1 Andreessen’s startup Netscape took about a minute to raise $2.7 billion, which was jaw-dropping at the time for an initial public offering.2 Thus began the dot-com boom, 30 years in the making. In contrast, blockchain technology was born commercial, in the sense that it was minting scarce units of digital value from the beginning with cryptocurrency. As of this writing, the market cap of the Bitcoin blockchain hovers around $180 billion. In 2018, blockchain-based startup Block.one raised a record-breaking amount through its initial coin offering (ICO), whereas corporate cracks at ICOs such as Overstock’s tZERO and KodakCoin have largely fallen flat.3 Blockchain will work much better for enterprise strategy implementation when enterprise leaders work together on blockchain — specifically on blockchain governance, meaning the stewardship of this distributed ledger technology through its phases of development. They must reach consensus on several types of issues — such as systems integration and scalability — but two in particular are critical to adoption: standards and the governance of change. Enterprise executives exploring blockchain can’t leave these solely to the technologists; these issues require industry expertise from stakeholders around the world

The Hard Work of Creating Open Standards Developing standards requires industry or ecosystem collaboration, and this work is not intuitive for executives. Consider FedEx: As part of its strategy, the company has joined the Blockchain in Transport Alliance (BiTA), and Dale Chrystie, FedEx’s business fellow and blockchain strategist, was recently elected chair of its standards council.4 BiTA is a member-driven standards organization unique among consortia in its focus on the “digitization of freight and transport.”5 In a phone interview with the Blockchain Research Institute, Chrystie used the term coopetition to describe what BiTA members do: cooperating on technical standards but competing on everything else — applications, products, and services.6

Without cooperation, the technology will stall. Professor Marek Laskowski, cofounder of the blockchain.lab at York University in Toronto, Canada, agrees. He has written on the limitations of the enterprise mindset when it comes to shared semantics, and it starts with the default assumption that companies ought not

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share their data except through web services and APIs.7 But with blockchain, we’re not talking about communicating between our respective data silos through APIs. We’re talking about bulldozing some boundaries of the organization and sharing an unprecedented amount of data about the movement of assets on a distributed ledger across global supply chains. For this, companies need to agree on which data is most important and how to define it in the universal language of code, agnostic of industry or geolocation. If we want the ability to deploy smart contracts to govern assets and minimize paperwork, then we must agree on the types of data and data formats these distributed applications must access or contain. The challenge is sizeable, as Dale Chrystie puts it: “We’re all standing on this side of the canyon, looking through our telescopes to the other side, and seeing the promised land, the pot of gold. We need to design a bridge to the other side. It will take a global village to build it.”

The Need for a Process to Manage Change Scientists at MIT Sloan are looking to understand the economic design choices encoded in software by the creators at the protocol level, relative to their stated goals (for example, “solving for trust”) and the economic choices of the people actually using it. Creators are witnessing unexpected user behaviors that, unlike a corporate or city charter and bylaws, become difficult to address without robust governance. A well-known example of governance in action was the hack of the decentralized autonomous organization known as The DAO, a distributed application launched on the Ethereum blockchain in May 2016.8 The DAO operated as an investment fund that gave each token holder a say in the allocation of its assets. In June 2016, an attacker exploited a bug in The DAO’s code and siphoned off about $50 million from the organization’s holdings.9 Ethereum governance kicked in, giving stakeholders 28 days to debate, vote on, and execute a plan of counteraction. By an 89% majority vote, stakeholders decided to reverse the funds by altering the Ethereum blockchain, thereby undoing The DAO. But few were happy about fixing an application-level problem with a protocol-level solution. So, we need to think about the different layers of blockchain governance — protocol, application, and ecosystem. Enterprise stakeholders need a mechanism for agreeing on which changes need to be made, when, how, and by whom. Do we need on-chain rules — that is, rules coded directly into the software protocols and running autonomously — or off-chain rules implemented by members of a governance body like the Ethereum Foundation, or some combination of the

Unleashing the Power of Blockchain in the Enterprise

two? According to the Coalition of Automated Legal Applications, the operations of a blockchain system are defined not solely by these on-chain and off-chain rules but also by the underlying layers of internet infrastructure. Here we see some features of a decentralized system, but they are often centrally controlled or administered by an internet service provider, government authority, or other intermediary.10 Getting these parties to the table may be worthwhile, too.

A Reluctance to Participate By our count, only 20 of the Fortune 500 corporations are considering governance issues through their participation in two of the largest blockchain consortia, Enterprise Ethereum Alliance (EEA) and Hyperledger. (See “Fortune 500 Companies Participating in Blockchain Consortia.”) Why is there not greater involvement? “The problem with blockchain is that it’s boring. It’s not fast. It’s not the only database. It’s not the best database,” said Chrystie of FedEx. “But where authenticity is important, where legitimacy matters, it’s a game changer.”11 At its core, blockchain technology provides transparency for proving claims of identity, origin, custody, and ownership in a variety of applications. That information is critical for, say, ingredients used in pharmaceuticals and precision parts used in military aircraft. It may become critical to more types of marketing claims and product labels, particularly those involving health, environmental impact, fair trade, and ethical sourcing. But it requires everyone in an ecosystem to participate end to end. Walmart, for instance, has asked all its leafy green suppliers to participate in IBM’s Food Trust, which uses blockchain technology.12 To those enterprise executives interested in joining a blockchain consortium but reluctant to do so because of antitrust concerns, Chrystie was emphatic: As with trade and industry association gatherings, BiTA begins every meeting with the reading of an antitrust statement, which proscribes any discussion of pricing, products, or customers. Blockchain governance is the unfinished business of industry. It is a prerequisite to designing and delivering strategies that depend on blockchain’s ability to provide single, shared, and immutable source of the truth. Those who claim their seat at the table and contribute to data standards and the rules of change will be laying the groundwork and preparing their organizations to build on it. There’s still time to be in the room where it happens, and executives need to get there.

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Unleashing the Power of Blockchain in the Enterprise

References 1 NATIONAL SCIENCE FOUNDATION, “Fact Sheet: A Brief History of NSF and the Internet,” nsf.gov, Aug. 13, 2003, accessed March 9, 2019. 2 S. GREENSTEIN, “Commercialization of the Internet: The Interaction of Public Policy and Private Choices or Why Introducing the Market Worked So Well,” in Innovation Policy and the Economy, vol. 1, eds. A.B. Jaffe, J. Lerner, and S. Stern (Cambridge, Massachusetts: MIT Press, 2001): 151-186; “Mosaic Browser,” History of Computers: Hardware, Software, Internet, accessed Sept. 24, 2019, https:// history-computer.com; and W.J. Campbell, The ‘Netscape Moment,’ 20 Years On, Poynter Institute, Aug. 3, 2015, accessed March 9, 2019. 3 R. SHARMA, “Evaluating the EOS ICO,” Coin Notes, June 5, 2018, coinnotes.news; D. Pozzi, “ICO Market 2018 vs 2017: Trends, Capitalization, Localization, Industries, Success Rate,” Cointelegraph, Feb. 14, 2019, cointelegraph.com; and “White Papers & Blockchain Philosophy,” EOS Collective, 2018. 4 G. MARKS, “FedEx Making Strategic Use of Blockchain Technology,” Fox Business, June 19, 2018, www.foxbusiness.com; and S. Castellanos, “FedEx CIO Looks to Industry Collaboration to Scale Blockchain,” The Wall Street Journal, April 29, 2019, www.wsj.com. 5 “Digitization of Freight and Transport,” BiTA deck, accessed March 20, 2019. 6 D. CHRYSTIE, interviewed via telephone by Kirsten Sandberg, March 12, 2019. 7 H.M. KIM, M. Laskowski, and N. Nan, “A First Step in the Co-Evolution of Blockchain and Ontologies: Toward Engineering an Ontology of Governance at the Blockchain Protocol Level,” Jan. 6, 2018, SSRN, accessed Feb. 27, 2019. 8 V. BUTERIN, “Bootstrapping a Decentralized Autonomous Corporation: Part I,” Bitcoin Magazine, Sept. 19, 2013, bitcoinmagazine.com; and C. Jentzsch, “Decentralized Autonomous Organization to Automate Governance,” final draft, Slock. it GmbH, April 30, 2016. Available at archive.org/stream/DecentralizedAutonomousOrganizations/WhitePaper_djvu.txt (accessed Sept. 24, 2019). 9 N. POPPER, “A Hacking of More Than $50 Million Dashes Hopes in the World of Virtual Currency,” The New York Times, June 17, 2016, www.nytimes.com. 10 P. DE FILIPPI AND G. MCMULLEN, “Governance of Blockchain Systems: Governance of and by Distributed Infrastructure,” foreword by D. Tapscott, white paper, Blockchain Research Institute and Coalition of Automated Legal Applications, June 27, 2018. 11 D. CHRYSTIE, interviewed via telephone by K. Sandberg, March 12, 2019.

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12 “Fresh Leafy Greens: New Walmart Food Traceability Initiative: Questions and Answers,” Walmart Food Safety and Health, Walmart and Sam’s Club, Sept. 10, 2018, corporate.walmart.com. Walmart’s Q&A referred to the definition under its “leafy greens marketing agreement,” which covers conventional and organic arugula, baby leaf lettuce, butter lettuce, cabbage, chard, endive, escarole, green leaf lettuce, iceberg lettuce, kale, red leaf lettuce, romaine lettuce, spinach, and spring mix.

HOW BLOCKCHAIN WILL CHANGE CONSTRUCTION

Ricardo Viana Vargas Executive Director – Brightline Initiative Don Tapscott Chairman - Blockchain Research Institute

Published in Harvard Business Review Cambridge - USA - 2019

How BlockChain Will Change Construction

Blockchain technology is among the most disruptive forces of the past decade. Its power to record, enable, and secure huge numbers and varieties of transactions raises an intriguing question: Can the same distributed ledger technology that powers bitcoin also enable better execution of strategic projects in a conservative sector like construction, involving large teams of contractors and subcontractors and an abundance of building codes, safety regulations, and standards? “Increasingly, we are thinking more carefully about when and where we need to compete and what can we share and collaborate on,’ said David Bowcott, global director of growth, innovation, and insight in Aon’s global construction and infrastructure group. Using blockchain to automate the contractual processes and paperwork underpinning these complex projects could save money, free up valuable resources, and speed up project delivery. (Unless otherwise noted, quotes are from interviews we conducted as part of our research.) Blockchain-Enabled Real Estate Development ProjectsIn commercial real estate, Amsterdam-based HerenBouw is applying blockchain to a large-scale development project in the city’s harbor. According to Propulsion Consulting founder Marc Minnee, HerenBouw’s objective was to set up a blockchain-enabled project management system to make the building development life cycle more efficient. Minnee’s blockchain application for HerenBouw focused on registering transactions at legally binding moments, where accuracy and an audit trail are essential. “Blockchain provides a platform for clearly cascading work products down the chain and holding everyone accountable for completing key tasks,’ said Minnee. The system’s benefits include timely information, unambiguous communication, and fewer mistakes. “Stakeholders have a clear and evenly distributed incentive to register these facts on-chain: Either you won’t get what you ordered or you won’t get paid,’ said Minnee. They also develop trust, which reduces friction in their mutual business processes. “Stakeholders spend more time discussing creative design and building method options.’ Blockchain Pilots in Construction Achieve Liftoff Aon, the global risk adviser to the construction industry, estimates that 95% of building construction data gets lost on handover to the first owner. Briq, a California-based blockchain firm, is demonstrating the potential to capture and secure a construction project’s documentation in a blockchain ledger that parties can navigate and give to the owner as a deliverable. Working on behalf of Minneapolis-based Gardner Builders, Briq developed a “digital twin’ of a new office construction, with a room-by-room inventory of every asset. “When a product or specification needs to be found in a building, there is finally a place to go to simply search for what is actually in that building,’ said Briq CEO Bassem Hamdy. The blockchain-encoded specifications are granular: paint colors, ceiling fixtures, LED bulbs, door hardware — plus manuals, warranties, and service life in

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a countdown clock that building owners can monitor. “Any improvements and refurbishments to the building can be documented, and the whole repository can be transferred to new owners if the asset is put up for sale,’ said Ellis Talton, Briq’s director of growth marketing. In other words, building owners get a living ledger of everything that has happened with the building. Overcoming Cultural Obstacles Engrained practices in the construction sector will likely prolong widespread blockchain adoption. “The construction industry is technologically advanced in many aspects of what it does,’ said Talton. “But the industry is very relationship-based. There are many family-owned firms and private companies. The selection of contractors and subcontractors can be based on relationships that have existed for decades.’ Talton also noted that very little money — less than 1% of revenues — is invested in up-front contracting and technology infrastructure for managing complex construction projects (that number is 3.5%–4.5% in aerospace and automotive). “The vast majority of the projects costs are in the building process, including the people and materials,’ he said. Scott Nelson, CEO of Sweetbridge (where one of us, Don, is an adviser), finds construction a natural for blockchain-based project management: “&Projects are well-structured and contract-based. Objectives are clear — be on time, on spec, and avoid rework. Classic project management techniques still work, but projects can benefit from a more decentralized and agile approach, where transparency is high and parties can be compensated for outcomes as well as for work performed.’ Identifying Applications of Blockchain in Project Management Over time, blockchain will have breakthrough applications for project management. We encourage organizations to explore and capitalize on this potential. Here are a few next steps. Identify use cases for blockchain adoption. Look for where success depends on mobilizing resources across enterprise boundaries; where identities, contracts, and payments must be audited and protected; and where the provenance and ownership of assets must be tracked. Some ideas for this: a reputation ledger that tracks subcontractors’ deliverables could help to identify reliable subcontractors for a project smart contracts that identify accountabilities and trigger milestone-based payments could automate agreements blockchain-enabled applications that aggregate data into a shared project management dashboard could help to manage workflow a distributed ledger that keeps an end-to-end chronicle of the construction process could record all building inputs and assets, including warranties and maintenance checkpoints

How BlockChain Will Change Construction

blockchain-enabled apps that track materials, testing, and results against building codes and standards could streamline inspections Develop prototypes and pilot projects. Do preliminary analysis: Audit the systems in use, consult their users, and think about who would need to be involved in identifying viable options, selecting one to prototype, designing the pilot, and participating in testing. Make a business case for investing in blockchain. Identify ways blockchain can increase project success, such as improving processes and organizational capacity to locate and share large quantities of data with specific individuals and entities. As David Bowcott of Aon said, “Collectively, we are all better off if we encourage data collaboration and use blockchain and machine learning to help us establish longer-term industry road maps for investments, and technologies that can boost productivity and efficiency and lessen risk.’ While the fundamentals of project management will remain important, blockchain enables managers to focus their talents on solving problems and achieving better project outcomes.

RESPONSE TO THE CRISIS: BUILD PERPETUAL CAPABILITIES

Ricardo Viana Vargas Executive Director – Brightline Initiative Edivandro Conforto Head of Strategy Research – Brightline Initiative Tahirou Assane Oumarou Director of Operations - Brightline Initiative Emil Andersson Researcher - Brightline Initiative

Published in Transforming Beyond the Crisis Thinkers50 - London – United Kingdom – 2020

Response to the Crisis: Build Perpetual Capabilities Related Podcasts

“Life will never be the same after COVID-19.”

on How the Current Crisis Accelerated Business and Digital Transformation http://rvarg.as/15x/

You hear that a lot these days. For many organizations, life will indeed be different after the outbreak as it had a severe and long-lasting impact on its ecosystem. For some organizations, there were even more immediate pressing concerns. A question about survival.

♫ Unimaginable Examples

♫ Organizational and Personal Resilience: The Ability to Cope with Change http://rvarg.as/resilience/

♫ Crisis Management: The Denial Problem http://rvarg.as/x17/

Virtually all organizations have been forced to rethink their business and operating model – its purpose, its strategy, its employees, its customers, its processes, and its technologies. Whatever the answers are to these questions, the ability to quickly learn and adapt to crises is critical. Let’s consider airlines as an example. Within just a few weeks, the airline travels dropped with as much as 96 percent1. This forced all airlines into a mode of crisis. A mode where they need to adjust their business to find ways to survive, or even prepare for the worst. Since there were very few passengers and baggage transported in the aircrafts, there were more capacity for cargo, which goes hand in hand with the increased demand for ordering goods online. Virgin Atlantic, followed by many other airlines, shifted focus to operating cargo-only flights2. This way airlines could to a limited extent repurpose its core operations to meet this new demand and thus mitigate the impact imposed by the COVID-19 outbreak.

Mode of operation: crisis In previous research in 2018 we looked more generally at how best to deal with and respond to crises3. We surveyed more than 1,200 senior global leaders and executives from the government, non-profit, and private sectors about management decisions and organizational strengths during and after crisis events. After all, the experience of crisis is a common one: 68 per cent of our respondents agreed with the inevitability of their organizations facing a crisis in the future – though few could have anticipated something as global and dramatic as the COVID-19 outbreak. The circumstances of an organization’s next major crisis vary, but its appearance is inevitable. A hack that exposes millions of customers’ data. A major holiday product launch pushed into next year. A chairman’s immediate dismissal for misconduct. An employee’s misstep gone viral. While crises differ in character, scale and scope, the following three aspects are true no matter the organization, industry, or geography. First, learning from a crisis requires moving into crisis mode. When facing a crisis, organizations must make a deliberate break from their traditional ways of work1 https://edition.cnn.com/2020/04/09/politics/airline-passengers-decline/index.html 2 https://www.forbes.com/sites/jamesasquith/2020/03/28/commercial-airlines-are-nowoperating-cargo-only-flights/#5202969b6f0e 3 https://www.brightline.org/resources/learning-from-crisis-mode/

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ing. Operating as business as usual prevents organizations from isolating their in-crisis learnings and applying them to post-crisis improvements. Second, organizations ought to shift focus to two key areas: people and process. Organizations with increased flexibility in internal processes, as well as those that empowered non-leadership-level employees who were closer to the crisis to make decisions, tend to emerge stronger post-crisis than those that don’t. And finally, crisis mode forces organizations to examine four key areas, regardless of whether they’re post-crisis or enjoying business as usual: prioritization of strategic initiatives, speed in decision-making and execution of processes, empowerment of teams, and commitment to internal communication. Information must flow and communication needs to be transparent and consistent.

Turning the tide with strategic transformation While of course no leader would ever wish for a crisis to happen, it doesn’t need to spell disaster. On the contrary: it is possible for certain opportunities to emerge from a crisis and have a lasting positive influence on a business. Organizations can find a powerful momentum and take advantage of the energy, mindset, capabilities, and processes formed in crisis mode to boost their chances to successfully undergo a significant strategic transformation. Take the example of the Dutch technology company, ASML Holding, which makes lithography machines – the equipment that semiconductor manufacturers use to produce microchips. With over 24,500 employees and its strong market position among large and small chip manufacturers, ASML was negatively impacted by the financial crisis in 20084. While other players in the industry were planning on riding out the crisis, ASML reinforced its strong market position by outsourcing most manufacturing to cut costs and instead invest in internal R&D to deliver better products, which was also synergized with an innovative customer co-investment program. In addition, ASML started a cost savings program, reducing its workforce by 12 percent – mainly contract employees – and instead focus on getting the right talent. ASML shows us that organizations can capitalize on the momentum of a crisis and strategically and swiftly transform in times of uncertainty. In 2020, Brightline conducted another extensive study with more than 1,000 global C-level executives to examine the correlation between success in strategy implementation and effectiveness and speed in transformation5. The results confirmed our hypothesis that organizations that master strategic transformation 4 https://www.bcg.com/publications/2020/crisis-spark-transformation-renewal.aspx 5 https://www.brightline.org/resources/strategic-transformation-research/

Response to the Crisis: Build Perpetual Capabilities

are skilled at both developing and implementing strategic initiatives, as well as at transforming quickly and effectively. It shows that high-performing organizations are about twice as likely to report effective organizational change. Meanwhile, transformation capabilities are core competencies linked to successful strategic initiatives. We asked us the question: what can we learn from these high-performing organizations? We found three perpetual capabilities that lead to higher success rates for strategic transformation.

Focus on internal talent As your business evolves, overcoming the skills gap may be one of the biggest challenges you face. Going through a crisis, this challenge may become even more apparent for organizations. Hiring new talent may be part of the solution, but it shows that faster-transforming organizations are nearly twice as likely as slower-transforming peers (34 percent vs. 19 percent) to report a greater focus on developing internal talent.

Organizations that are fast at transformation prioritize developing talent that can quickly make decisions and delegate and empower teams, as the employees, for obvious reasons, have higher skills and better understanding of their responsibilities and how to meet goals. The more subtle value in focusing on internal talent versus hiring external talent has to do with the personal growth, mindset, and behavior. It’s a match-making and mutual bonding between the individual and the organization. Change must start from within, and focusing individual transformation of the employees is (at least) as important as transforming the more business oriented activities in the organization. Without a focus on internal talent and personal growth of the employees, the chances of an organization continuing to evolve and respond to new changes

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are almost non-existent.

Formalize to adapt Formalized processes are critical for organizations who wish to become more adaptive. They’re also a key part of the equation for fast and effective transformation. More specifically, we found that high-performing organizations are 21% more likely to have well-formalized processes in place versus low performers. What is more interesting is how formalized processes impact organizations’ ability to change. Organizations with very well-formalized processes also report being “often or consistently adaptable” in the face of unforeseen strategic challenges. A full 94% of adaptable organizations have formalized processes in place, compared to just 59% of rigid organizations.

In the same way we can see the correlation between adaptability and speed. Organizations with the capability of balancing ongoing business with their transformation efforts are also more likely to do it at a faster rate. This swift footedness is key in strategic transformations when challenges arise and it’s necessary to course correct. And that means reviewing lessons learned from past failures, speaking with customers or end users, or reevaluating milestones and timelines. Leaders can provide a foundation for strategic transformation in which balance the installment of formalized processes yet abide to methods for course correction. This type of discipline affords an organization a greater degree of adaptability and lets it take full advantage of the technology and frameworks at its disposal.

Response to the Crisis: Build Perpetual Capabilities

Leadership The third key variable for successful transformations has to do with leadership, suggesting there is a strong need for a central figure who can inspire and orchestrate organization-wide transformation efforts. Without effective leadership and accountability in place, transformation efforts slow down. Now, as organizations undertake the difficult task of rebuilding in the wake of COVID-19, we think that the role of a Chief Transformation Officer (CTO) has never been more relevant6. As suggested in our research, a CTO can take top management’s vision of the transformation and make sure it’s properly disseminated throughout the organization. He or she can translate that vision into concrete goals. Even more important, the CTO can devise and deliver the master plan for achieving these goals—overseeing the multitude of projects that will be needed to turn the organization’s transformation strategies into reality in the post-COVID world. The CTO will make sure that internal talent and capabilities are leveraged and developed, just like the employees are inspired and allowed to personally transform with the organization. The CTO will serve as a catalyst for formalizing the processes used, as well as actively adhere to methods of course correction. Having a CTO leads to greater accountability. Investing in one person with the responsibility for overseeing a transformation eliminates any potential leadership gaps. It ensures that there will always be one person—the CTO—whose sole priority is the success of the transformation initiative. Sometimes a black swan event can disrupt a trend that has been gaining momentum. In the case of the CTO role, however, the COVID-19 pandemic should accelerate that momentum. In the post-COVID world, we need to bring on more CTOs to manage the arduous task of re-building our organizations, our economies and our society.

6 https://www.pmi.org/about/blog/bring-on-the-cto-why-having-a-chief-transformationofficer-makes-sense-in-a-post-covid-world

USING CRISES AS A CATALIST FOR TRANSFORMATION

Published in Transformation Playbook Thinkers50 - London – United Kingdom – 2019

Using Crises as a Catalist for Transformation Related Podcasts

♫ Unimaginable Examples

on How the Current Crisis Accelerated Business and Digital Transformation http://rvarg.as/15x/

♫ Organizational and Personal Resilience: The Ability to Cope with Change http://rvarg.as/resilience/

♫ Crisis Management: The Denial Problem http://rvarg.as/x17/

Most organizations are not well prepared to deal with a serious crisis. The end result of a crisis can either help transform the organization or leave it in a very dangerous position. Crises can evoke fight-or-flight conditions, which are a physiological reaction in response to perceived danger, attack, or threat to survival. In animals, as well as humans, this is a natural response mechanism that helps keep us alive. Consider the crisis that Johnson & Johnson faced in 1982. You have probably heard the name Tylenol before, one of the most common painkillers available in the market. In September 1982, seven people in and around Chicago died after consuming capsules of Extra-Strength Tylenol. The capsules were laced with cyanide. At the time, Tylenol was Johnson & Johnson’s best-selling product, making up 17 percent of the company’s net income in 1981. Experts predicted that the company would never recover. But, only two months later, Johnson & Johnson was back on track with improved, tamperproof packaging and an extensive media campaign. What helped Johnson & Johnson to overcome this terrible crisis was not luck or magic. It was effective leadership and action, with the right approach to crisis management. The business followed a deliberate sequence of decisions and process changes to create a position of strength after the crisis had almost destroyed it. First, the company prioritized consumers. All 31 million bottles of Tylenol capsules on store shelves were recalled and consumers were offered a replacement product free of charge. Leadership was key. As well as taking immediate action to pull the product off the market, Johnson & Johnson’s chairman, James Burke, took a transparent approach with the media and public. He released a full chronology of actions the company had taken in the month that followed the start of the crisis. Johnson & Johnson’s responses were extremely expensive, but the right actions and practices helped the organization transform itself, and today it is a global pharmaceutical powerhouse. Of course, Johnson & Johnson is not alone. Examples of crises that threaten organizations’ reputations, image, and market value are everywhere. They are industry agnostic and occur on a global scale as consumers, markets, and our economies are more connected than ever. Recent examples include defective lithium batteries in Samsung phones that were prone to explosion and banned from flights in the US, the Facebook data breach by Cambridge Analytica, the deadly accidents caused by Tesla autopilot problems, and the tragic accidents during 2019 involving two Boeing 737-MAx planes.

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To better understand what happens during a crisis, and most importantly, what leaders can learn from a crisis situation that can help organizations to transform, Brightline Initiative partnered with Quartz Insights to conduct a global study. Our goal was to uncover ways organizations could leverage the experience of a crisis and use those learnings to improve their performance and transformation capabilities. The study comprised both qualitative research and interviews with senior executives who had experienced a crisis situation in the past. Candidates represented different industry sectors and all had managed a team or organization through a crisis. Using a qualitative study, 1,258 executives and managers around the globe were surveyed to investigate specific aspects of crises. The sample was selected based on geographic representation, organization type and size, including government, non-profit, and the private sector. There are key learnings on both why crises happen, and what can be gained from them.

Understanding crises Crises are inevitable. As pointed out, organizations are facing, have faced, or will face a crisis that may threaten their existence at some point. The study confirmed this argument, with 68 percent of respondents agreeing with the inevitability of their organizations facing a crisis in the future. There is also evidence that crises are usually a sign of poor leadership (Exhibit 1), regardless of whether it is in the private sector, non-profit or government, and the size of the organization. A CEO from an aerospace company stated in the report: “A leader has to understand that dealing with a crisis requires a change of culture, and when they don’t understand that, things will quickly spiral out of control.” Operational processes also come under scrutiny during crises. Respondents indicated that the organization must work to shift priorities and change team structure, as well as shift the operating mindset to act quicker. Sometimes, this means teams will go from making decisions over days and weeks to making decisions in a manner of minutes. “In crisis, there needs to be accelerated decision-making, a tighter grip on direction from the top executive leadership,” said a government consultant. But the question that we were more interested in was what happens when some organizations are facing a crisis situation? What changes might they experience in their leadership, processes, team dynamics, and culture? What can organizations learn from a crisis?

Using Crises as a Catalist for Transformation

Exhibit 1: Perception that the crisis was a symptom of poor leadership, by crisis severity. Source: Learning from crisis mode: implications for better strategy implementation. Brightline Initiative, a special report by Quartz Insights, 2018.

The crisis mode When organizations face crises, their natural reaction is to go into what the research calls “crisis mode.” “Crisis mode” is the operational realities of an organization in crisis, including shifts in ways of working and employee responsibilities. It seems some organizations radically change the way they do things. A full 93 percent of respondents said their organizations went into “crisis mode” when crises hit them and over half (59 percent) agreed their organizations were facing an extremely serious crisis, or a crisis that threatened the company’s existence. Entering crisis mode has to be a deliberate action, and organizations should recognize that this state of work helps them to recognize the threats and examine their ways of working, learn from specific changes, and learn how to capitalize on key opportunities that a crisis can produce. The report clearly shows the opportunities that can emerge from a crisis mode state, and if the organization fails to act on these they will be unable to make substantial improvements in the way they work.

Effects of crises It is not only about problems and challenges. Crises are ways to improve performance, if they are leveraged the right way. For instance, of the respondents who said they went into crisis mode when facing a crisis, 78 percent declared their strategy implementation capabilities grew stronger as a result of the crisis.

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They also agreed that modifications to team structures that were introduced as a result of the crisis remained in place (79 percent), close working partnerships between cross-functional teams continued (74 percent), and a clearer understanding of the organization’s priorities renewed its vision and sense of direction (71 percent). One senior partner and managing director of a large consulting company asked: “Why can’t we have this degree of cooperative effectiveness more commonly in our organization? Why does it take a crisis for us to operate this way? How can we take at least a portion of this magic and make it a more common element of business as usual?” In fact, the right answers for these questions will depend on an organization’s ability to leverage the “crisis mode” and use the learnings to transform the way it works.

Leveraging crisis to transform You can (and should) use crises to develop some of the key competences that will help your organization transform. The study identifies four areas where key capabilities need to be developed to transform organizations. •

Focusing on what matters

Prioritizing initiatives is a key success factor when facing crises. The ability to identify what is critical and focus resources on it is paramount. Teams need to encourage smart simplicity, by staying focused and keeping things as simple as possible to rapidly react to changes in the organizational environment. Leaders need “people who can get to the core of an opportunity or threat, understand the drivers, deliver the information, take the action you need in the way you need it.” Of 75 percent of the respondents who reported a “crisis mode” environment, 91 percent reported making changes to their prioritization of initiatives, and 88 percent changed their team’s priorities and dedication to initiatives. A simple and effective way to identify key initiatives is how Steelcase, the 100-year-old furniture company, applies prioritization to all projects. The company divides initiatives into three main categories: Now – projects that are closest to the core business and demand immediate attention; Near – near-term initiatives that require building internal capabilities to respond to market shifts; and Far – investments in future projects with long-term shelf lives. With this approach, the company is able to prioritize what is critical for its transformation and maximize its resources with the right mix of investments across all three horizons.

Using Crises as a Catalist for Transformation

•

Need for speed

During a crisis situation, timing and the ability to respond quickly define whether the organization will successfully overcome the crisis or succumb. The study found that organizational decision-making was 10 percent more likely to have sped up during the crisis, increasing productivity and efficiency, rather than increasing error rates and quality issues. Eighty-one percent of respondents believed that business processes benefit from adaptation, as the crisis reveals weaknesses and strengths, rather than believed that business processes would suffer because unproven processes were adopted as a response to time constraints. As a Chief Reputation Officer at a public relations agency stated in the report, “being forced to deal in the time constraints and under the pressure of a crisis forces you to identify roadblocks to getting things done.” Similarly, teams were 11 percent more likely to increase their effectiveness in response to the crisis because of the challenge than they were to decrease effectiveness because of the emotional turmoil. •

Power to the people

Those organizations that performed well during a crisis situation believe that crises help to uncover talented leaders from within the organizational ranks, allowing those leaders to advance (75 percent), and this is why empowering key employees to take the lead on transformation is essential during a crisis. The study revealed that respondents did not agree that responsibility for successfully resolving the crisis rested with the organizational leadership team more than employees. In crisis mode, non-leadership employees are motivated to take ownership of the situation, an attitude that has clear benefits in post-crisis scenarios. “I think in a true crisis, it becomes an all-hands-on-deck situation where even the most senior leaders need to roll up their sleeves and get into the dirty work just to show solidarity on some level,” said a marketing director from a healthcare company. In addition, modifications to the organizational and team structures as a result of the crisis were more likely to leave long-lasting impact and benefits, and close-working partnerships between cross-functional teams continued after the crisis was over. ING, the Dutch banking group, uses agile techniques to improve speed and collaboration. The company created the “One Way of Working,” a strategy that incorporates the principles of “agile” methodology to improve flexibility, innovate

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faster with shorter time to market, minimize handovers, and provide employees with greater freedom and responsibility than in a traditional company approach. Dina Matta, Head of the Global Transformation Office at ING, stated: “As you empower people by being clear on a strategy’s purpose, and empower teams to participate in the decision-making process, they have a lot more ownership [and] a more direct impact on business performance and strategy execution.” •

Committing to communication

Communicate, communicate, and communicate. There is no question that during a crisis a successful resolution depends on communicating widely and effectively. Improved understanding about the organization’s vision and goals, and a clear sense of direction will help organizations navigate any type of transformation. Don’t just assume your people will get it – you must firmly establish a shared commitment to communicate and get buy-in from people about what needs to be done. A retired firefighter observed: “You have to communicate. You could have all the strategic plans and all the strategies you want, but if you don’t have people that have bought into that, and if you are not communicating as a boss to your subordinates, they’re not going to understand what they’re doing and why they’re doing it. The big thing is why, why are we doing this? If you can’t communicate that, you shouldn’t be doing the job!” Open, clear, and constant communication helps employees to understand the context and why their work is critical to solving the crisis.

Resources JUDITH REHAK, “Tylenol made a hero of Johnson & Johnson: the recall that started them all”, New York Times, 23 March, 2002. https://www.nytimes. com/2002/03/23/your-money/IHT-tylenol-made-a-hero-of-johnson-johnsonthe-recall-that-started.html HAYLEY TSUKAYAMA, “How Samsung moved beyond its exploding phones”, Washington Post, 23 February 2018. https://www.washingtonpost.com/business/ how-samsung-moved-beyond-its-exploding-phones/2018/02/23/5675632c182f-11e8-b681-2d4d462a1921_story.html?noredirect=on ALEX HERN AND JIM WATERSON, “Facebook in ‘PR crisis mode’ over Cambridge Analytica scandal”, The GUARDIAN, 24 April 2018. https://www.theguardian.

Using Crises as a Catalist for Transformation

com/uk-news/2018/apr/24/facebook-in-pr-crisis-mode-over-cambridge- analytica-scandal-outrage-hallow-aleksandr-kogan NICKY WOOLF, “Elon Musk Twitter rant a ‘case study’ in how not to handle a crisis, experts say”, The GUARDIAN, 7 July 2016. https://www.theguardian.com/ technology/2016/jul/07/tesla-elon-musk-autopilot-death-crisis-management Sinéad Baker, “Boeing 737-MAx Fatal Crashes”, Business Insider, 29 May 2019. https://www.businessinsider.com/airline-disasters-crashes-that-changed-aviation-2019-5 Quartz Insights in collaboration with the Brightline Initiative, “Learning from Crisis Mode: Implications for Better Strategy Implementation”, 2019. BRIGHTLINE INITIATIVE, “The Ten Brightline Initiative Guiding Principles”, https:// www. brightline.org/principles/ “ORGANIZATIONAL TRANSFORMATION: HOW STEELCASE DEVELOPED A STRATEGY FOR GROWTH IN A CHANGING WORKPLACE”, Brightline Initiative & Forbes Insights Case Study Series. https://www.brightline.org/resources/ how-steelcase-developed-a- strategy-for-growth-in-a-changing-workplace/ “AN AGILE BLUEPRINT FOR EFFECTIVE STRATEGY EXECUTION AT ING GROUP”, Brightline Initiative and Forbes Insights Case Study Series. https://www.brightline. org/ resources/an-agile-blueprint-for-effective-strategy-execution-at-ing-group/

IDENTIFYING AND RECOVERING TROUBLED PROJECTS: HOW TO RESCUE YOUR PROJECT FROM ITS FAILURE

Accepted for publication at Project Management Technology Magazine Beijing – China – 2011 PMI Global Congress 2007 – Asia Pacific Hong Kong – China – 2007 Mundo PM Magazine Curitiba – PR – Brazil – 2006

Identifying and Recovering Troubled Projects: How To Rescue Your Project From Its Failure

Abstract Related Podcasts

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Understanding the Early Closure of a Project http://rvarg.as/b1

♫ Recovery Strategies for

Troubled Projects (audio in Portuguese) http://rvarg.as/bl

♫ Troubled Projects (audio in Portuguese) http://rvarg.as/bm

The purpose of this article is to conceptualize and identify troubled projects and allow their evaluation and possible recovery, minimizing the impact of the negative effects in the event of a total failure. The word recovery, as used here, has a meaning that is different than usual. It does not mean leading the project to the intended success from the planning phase but to minimize total failure. Currently there are lots of papers and studies on project management tools, methodologies, strategies, and processes. All these efforts are intended to enable and prepare project managers and their teams to ensure the success of their projects. BERRY (2002) complements this concept saying that virtually all the methodological patterns of projects are an important knowledge base on how to properly conduct a project, completing the work within its deadline, and with high-quality deliverables. However, the reality that many project managers make evident is significantly different. In fact, even the most experienced project manager end up as acting as a “firemen” to their projects, trying at any cost to solve the problems and the difficulties found along the road. These “Troubled Projects” are a reality in the worldwide business scenario; most of the times, dealing with a troubled project is a particularly challenging task for the project manager and his or her team. These projects require, for different reasons, specific attention and care. Finally, the article deals with lessons learned with troubled projects and how to use them to avoid future problems.

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Definition of Troubled Project A troubled project can be defined as a project where the difference between what is expected and what has been accomplished exceeds the acceptable tolerance limits, pushing into a course that will inevitably lead to failure. By assessing the nature of the projects, we can say that any project is characterized by a challenge, whether for its intrinsic complexity or its short deadline or low cost. To this regard, any project requires a level of control and management different from usual engagements, many times demanding a differentiated effort from the project manager and his/her team. However, when the variances exceed an acceptable limit of tolerance we have a troubled project that requires a specific effort. In this case, a specific approach strategy becomes necessary, by defining a possible recovery or even deciding for an early termination.

Deviation to the Forecast

ilu

tic

Cri

ble

Pro

ort

Eff

alle

Ch Acceptable deviation limit

Possible Recovery Zone

Premature Termination Zone

Conventional Project Management Zone

Exhibit 01 – Continuous sequence of troubled projects (ESI, 2005).

Low

High

It is important to note that troubled projects are not failed projects. Failed projects are unrecoverable, since the highest level of loss possible has been reached. On the other hand, the troubled project has a possibility ofStep being x recovered alStep x though it shows strong indications that if it is not managed in a specific manner Low High it can quickly deteriorate and become unfeasible.

Identifying and Recovering Troubled Projects: How To Rescue Your Project From Its Failure

When we use the term “recover”, what we mean is that there is a chance of recovery and not that the recovery is simple or easy. Exhibit 02 shows some of the erroneous perceptions regarding troubled projects. Troubled Project

It's NOT

It IS... Potential creator of negative conflicts

Moral stimulator for the team and project manager

Hard

Simple

Challenging

Easily controllable

Strong perception of out of control

Fun Short-term benefits seeker

Heavy energy consumer (people)

Generator of respect for the stakeholders

Moral reductor for the team and project manager

Exhibit 02 – Erroneous perceptions on troubled projects.

Identifying and Assessing a Troubled Project A troubled project always presents indicative factors that might help recognize it as a problem. Some aspects related to stakeholders, project resources, documentation, and the triple constraint (scope, cost, and schedule) allow us to get a quick, direct perception of the problem degree the project is in, as shown in Exhibit 03.

Increase of informality and improvisation in the execution of the processes The project's plans are updated less frequently and there's lack of trust in the process Project delay and/or overrun is evident Inadequate progress as to project's constraints

Stakeholders

Sponsor, executive managers and/ or clients express discontent with the project in an evident or disguised manner Insufficient involvement from the client The client is not ready to take his/her responsibilities in the project

Documentation and Formalization

Resources are clearly not enough

Costs and Schedule

Significant increase in the risks' exposure (probability and/or impact) Failure in the implementation of contingency plans

Indicators of a Troubled Project

Resources

Implementation team shows lack of experience in the execution of the project Significant increase of team turnover

Mudanças significativas constantes nos requerimentos iniciais do projeto

Risks Scope

Frequent changes in the initial scope of the project Lack of agreement between the stakeholders as to the requirements and project objectives

Exhibit 03 – Mindmap of the indicators of a troubled project. Based on SAROKIN (2005), WU (2000), and WARD (2003).

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Although the aforementioned warnings are quite straightforward, it is important to highlight that none of them indicates that a project is an isolated problem. Most of the times, they operate together and indicate, in several ways, that there is a problem with the project. In addition to the warnings listed before, a set of vital signs and qualitative assessment models can be built to develop a more accurate criterion on the real vitality of the project. KAMPUR (2001) says that the vital signs are measured by the variance between current status of the project and the values as planned, as shown in the example in Exhibit 04. VITAL SIGN

VARIANCE

Percent variance between actual and planned schedule

Percent variance between actual and budgeted cost

Percentage of contractual deliverables completed on schedule

Percent variance between resources actually used and budgeted resources

Number of risk events with high probability and high impact

SCORE

< 10%

10-20%

> 20%

< 10%

10-20%

> 20%

> 90%

80-90%

< 80%

< 10%

10-15%

> 15%

1-3 Risks

4-5 Risks

Above 6 Risks

Exhibit 04 – Example of vital signs models (KAMPUR, 2001).

ESI International (ESI, 2005) developed an assessment process of troubled projects based on a process of interviews, analysis and consolidation of results that allow us to assess the problem’s potential and the chances of recovering the project. The process is highlighted in Exhibit 05. In this process the participation of the main stakeholders and their personal perceptions are the key to the success of a possible recovery of the project.

Identifying and Recovering Troubled Projects: How To Rescue Your Project From Its Failure

Launch Kickoff Meeting

Analyse Project Data

Cockpit Room

Gather Result Data

Validation

Hypothesis

Questionnaires and Projects’ Evaluations

Analysis

Interview Stakeholders

Questions

Prioritize and Put the Results in Order

Exhibit 05 – Troubled project assessment model (ESI, 2005).

Several other models can be developed to identify scenarios in each organization. However, all these models should include a brief, quick, direct assessment; otherwise, when a project is identified as a problem it might be too late to recover it.

Project Recovery vs. Project Termination Once the troubled project is assessed, we have to make the decision of aborting or recovering the project. Different factors may influence this process, but some basic considerations have to be observed. LONG (2003) proposes five key questions that should be considered to decide if the project will be recovered or aborted, as follows: 1. How important is the project for the sponsor, the stakeholders, and the organization? 2. Can the project continue as planned and defined or does it need to be completely redefined? 3. The organizational impacts and the resource needs for the recovery are feasible to produce the desired recovery? 4. Is there a need for political support so that the project can be recovered? 5. Are you (the project manager) personally motivated and interested to do what needs to be done to recover the project? As a complement to the five previous questions, several factors can evidence that there is no chance of recovering the project. The most common factors are as follows:

• Business benefit to be generated by the business cannot be delivered • The political environment is no longer sustainable • There is no longer a project sponsor and there is no apparent capacity to replace them

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• The business needs have changed • There have been significant changes in technology • Contractual or judicial dispute in progress that makes the project unfeasible • Market conditions have changed If you decide to discontinue the project, its early termination can be carried out in two different ways, each with its own level of complexity, speed, and stress. STEWART & SHEREMETA (2000) assessed the different drivers and impacts involved in the early termination of a project. They say that there are basically four ways to terminate a project, as follows:

• Addition – Discontinue the project by adding the work to be performed and all its resources to a larger project.

• Absorption - Discontinue the project having another project absorbing the work of the troubled project, without the resources and infrastructure accompanying the work.

• Inanition - Discontinue the project by inanition (starvation), with the re-

sources that sustain the project no longer being supplied, and the project being terminated due to complete lack of resources.

• Extinction - Discontinue the project by extinguishing it, with an immediate cancellation action being taken, and the project ceasing to exist and only the work already completed and that can possibly be used is recovered.

In order to group all these concepts, we developed the flow chart in Exhibit 06, starting with the problem assessment up to the possible recovery strategies and the early termination of the project.

Identifying and Recovering Troubled Projects: How To Rescue Your Project From Its Failure

TROUBLED PROJECT Is the project still important to the sponsor and the organization ?

Yes Is the project unique in this category ?

Yes Is the project aligned with the organization’s objectives ?

Yes Is the project’s technology still viable ?

Yes Does the project have the client’s buy-in ?

Yes Define the vital signs to be measured Yes Apply the vital signs to the project

Confidentially communicate to main stakeholders

Is there any project that can take over this project

Yes

Evaluate the impact of taking over the project

Add a project with resources and scope into the main project

TERMINATION BY ADDITION

Yes

Add the scope into the main project

Reallocate resources and return the infrastructure

TERMINATION BY TAKE OVER

Yes

Remove the resources that support the project

The project loses its function because of lack of resources

TERMINATION BY INANITION (HUNGER)

Rescue what is possible

Reallocate resources and return the infrastructure

TERMINATION BY EXTINCTION

No Yes CONTINUE WITH THE PROJECT

Yes

Are the vital signs in a acceptable range ? Yes

Develop the Recover Plan

Can the work be taken over by some other project? No

Terminate the Project ?

Can the project be terminated in a gradual manner?

Yes

Review legal aspects and contracts

Make official the project’s extinction

Exhibit 06 – Flow chart of the decision-making process identification regarding a troubled project.

Regarding the decision on project recovery or termination, we should emphasize that there is no formula or quantification to suit all projects, in all organizations. Many times the search for the desired results can include the cancellation or anticipated termination of a project.

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Developing the Recovery Plan When a recovery strategy for a project is prepared, many say that it is not important to know what drove the project to its current status but rather to see what can be done and will be done to pull it out of that situation. However, this statement is incorrect. It is very hard to have a correct recovery strategy without knowing the facts that created the problem. Particularly, if the drivers are not eliminated, the project might be recovered in the short term, but with time it can fall back into the same critical stage it was before the recovery process. Another important issue is that recovering a project is to save the loss and restore its usefulness, preventing the project’s total failure (ESI, 2005). Thus, when we intend to recover a project we are not discussing the full recovery of the project, but the avoidance of its total failure. Basically, recovery consists of readjusting the basics of the scope, schedule, and costs and resources of the project, which is methodologically known as the project’s triple constraint, as shown in the project triangle in Exhibit 07. Scope

Cost

Schedule

Exhibit 07 – Project’s triple constraint (scope, schedule, and cost).

The proposals below can be discussed and assessed based on these constraints. Option 1 – Reducing the project scope, maintaining the planned budget and schedule. This process can save parts of the project that may survive without full scope, as shown in Exhibit 08. Scope

Cost

Schedule Troubled Project

New Scope

= Reduced Scope Action

Cost

Schedule

Recovered Project

Exhibit 08 – Project recovery by reducing scope.

Option 2 – Maintaining project scope, inflating project costs and maintaining

Identifying and Recovering Troubled Projects: How To Rescue Your Project From Its Failure

the planned schedule. Usually this process is used when project scope cannot be reduced and the interest in results outweighs the shortfall caused by an increase in costs (Exhibit 09). Scope

Scope

Schedule

Cost

Schedule Cost Increase

Troubled Project

New Cost

Action

Recovered Project

Exhibit 09 – Project recovery by increasing available budget.

Option 3 – Maintaining project scope by extending project deadlines and the budget forecast. Usually this is used when schedule is not essentially critical for the project, where a slowdown may avoid an increase in cost, thus allowing us to maintain the scope (Exhibit 10). Scope

Scope

+ Cost

= Tempo

Time

Cost

Schedule Increase Troubled Project

Action

Recovered Project

New Schedule

Exhibit 10 – Project recovery by extending schedules.

Option 4 – The project is totally redefined, thus creating a new relationship between scope, schedule, and cost, with only portions of the original projects scope. Usually this is used when options 1 to 3 are not considered as appropriate (Exhibit 11).

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Scope

Useful part of the Project

New Project

Cost

Schedule

Troubled Project

Action

Recovered Project

Exhibit 11 – Full redefinition of the project, using part of the results obtained.

The four options shown above are the foundations for any type of recovery. All other types and solutions proposed are but variations resulting from one of these options. In addition to this resizing of the triple constraint, several other actions should be taken to ensure that the recovery attains the expected success. In these cases, it is necessary not only an radical change in planning or in project requirements, but also a radical change in the behavior of the people. LONG (2003) mentions various actions that the project manager and the team should take to ensure the success of the recovery plan, as follows:

• Restructure the project team and/or increase the controls over the work. • Politically consolidate the project with its sponsor, company executives, and stakeholders, searching for broad, unrestricted support.

• Create a solid communication program with a positive message in connection with the changes.

• Conduct and maintain an optimistic attitude and environment and, hoping for success.

• Ensure the personal commitment to assure the success of the recovery strategy.

Recovery Plan Success Factors and Lessons Learned When conducting the assessment of the troubled project and its possible recovery it is possible to say that some of the lessons learned need to be presented again:

• Always prioritize . There is never time or money to solve all problems. • Be aggressive. Look for the problem and find the solutions. • Proactivity. Do not wait for problems to be solved on their own. • Establish accountability lines. Know who is responsible for what within the project.

Identifying and Recovering Troubled Projects: How To Rescue Your Project From Its Failure

• Worship experience. The authority to make decisions should be transferred

to the most experienced person and not necessarily to the highest hierarchical position.

• Find the problems. Try to identify the problems in a structured way and as a team, thus avoiding that the project be found due to its problems.

• Do not micromanage. Details prevent the whole from being easily identified. • The real problem may not be evident. It is not always that what needs to be

solved is what is more apparent in the project. Check what is behind the curtains.

• Look for and assess the impacts. • Never miss opportunities. An opportunity lost can be one of the biggest problems for the project.

• Poor decisions hurt. Making a wrong decision is as harmful for the project as not deciding at all.

• Contextualize opinions. Outside opinions need to be inside a context and must be integrated.

Finally, it is important to break the cycle that created the problem. You should always remember the rules of Newton’s Law. This law states that objects in motion tend to stay in motion unless an outside force opposes them.

Conclusions Preventing a project from becoming a challenge requires proactive action. First of all, recognizing that a problem exists is itself already a problem. The article “Why Bad Projects Are So Hard to Kill”, published by Isabelle Royer in the Harvard Business Review in 2003 (ROYER, 2003) has, in its own title, the search for more answers on the difficulty to assess and solve problems. She says that this is due to an unconditional belief that the project, even when going through stages of complete chaos, will overcome the obstacles and be a success in the future. This perception is contagious; everyone ends up believing that in the future a “miracle” might occur and transform the project in a huge success. Unfortunately, reality always proves to be the opposite of a “miracle”. If we look again figure 1, we will note that when the ones involved really accept that there is a problem, then the problem no longer exists. Now, it is a failure. Therefore, when are faced with a troubled project, the first thing to do is to accept that there is a problem and quickly identify its magnitude, and the actions that should be immediately taken. In a project this close to danger, any day lost is a day that will never be recover.

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References BERRY, D. (2002). Your Project is in Trouble! Now What? San Antonio: Project Management Institute Global Congress United States. ESI International. (2005). Rapid Assessment and Recovery of Troubled Projects. Arlington: ESI International. HARVEY, J. B. (1996). The Abilene Paradox and Other Meditations on Management. New York: Jossey-Bass. KAMPUR, G. K. (2001). How to Kill a Troubled Project. San Ramon: Center for Project Management. LONG, S. P. (2003). Succeeding With Troubled Projects. Baltimore: Project Management Institute Global Congress 2003. ROYER, I. (2003). Why Bad Projects Are So Hard to Kill. Boston: Harvard Business Review. SAROKIN, M. (2005). 10 Indicators of a Troubled Project. EDS Next Big Thing Blog. STEWART, W. M. & SHEREMETA, P. W. (2000). Will You Be Terminated Today. Houston: Project Management Institute Global Congress 2000. VARGAS, R. V. (2004). Using Earned Value Management Indexes as a Team Development Factor and a Compensation Tool. Prague: Project Management Institute Global Congress EMEA. WARD, J. L. (2003). There is a Signpost Up Ahead! Recognizing and Dealing with Project Warning Signs. Baltimore: PMI Global Congress North America 2003. WU, J. (2000). Business Intelligence: Top 10 Warning Signs of a Troubled BI Project. DM Review Online website (www.dmreview.com).

TO DRIVE INNOVATION, YOU MUST UNDERSTAND YOUR ECOSYSTEM AN INNOVATION ECOSYSTEM’S MANY ELEMENTS MUST WORK IN SYNC FOR GROWTH TO HAPPEN

Ricardo Viana Vargas Executive Director – Brightline Initiative Cris Beswick Founding Partner - Outcome Dan Toma Founding Partner - Outcome

Published in The London School of Economics Business Review London – United Kingdom – 2020

To Drive Innovation, You must Understand your Ecosystem

In 1878 Leo Tolstoy released what would become one of his most acclaimed works, the novel ‘Anna Karenina‘. The book started with a quote that over the centuries has transcended disciplines: ‘Happy families are all alike; every unhappy family is unhappy in its own way’. When taken literally, the quote is a good reflection on family life and family conduct. But as a metaphor, the quote has found application in different fields, from anthropology to ecology, and from philosophy now to business. The quote became the Anna Karenina Principle, with its popularisation by Jared Diamond in his book ‘Guns, Germs and Steel’. In the book, the principle illustrates why – throughout human history – so few wild animals have been successfully domesticated. A deficiency in any one of a high number of factors can render a species not able to be tamed. Therefore, all successfully domesticated species are not so because of a particular positive trait, but because of a lack of any number of possible negative characteristics. The Anna Karenina Principle can be summed up as: a deficiency in any one of several factors dooms an endeavour to failure – consequently, a successful effort (subject to this principle) is one where every possible deficiency has been avoided or overcome. With more and more businesses understanding the importance of building innovation ecosystems to ensure sustainable future growth, the Anna Karenina Principle comes to the forefront again in the context of improving or changing these ecosystems. An innovation ecosystem consists of many elements, all of which need to work in sync for growth to happen. Through our pioneering work around innovation maturity, we’ve concluded that when clustering the many ecosystem elements, you typically end up with five core pillars: strategy, leadership, management, culture and processes. Understanding if any of the components of these pillars is hindering the progress and outcome of the ecosystem becomes paramount when it comes to making improvements or changes. Think of the ecosystem improvement strategy in terms of a sat-nav system in a car. Sat-nav systems are great when they work, but there are times when the link is down, the signal fails, or the reading is off, and the journey can turn into a random game of mystery-road pinball. Or to put it another way, you know where you want to get to, but unless your starting point is clear then any proposed route and any stops along the way are simply guesswork, or in innovation parlance, unproven assumptions! The same applies when it comes to improving an innovation ecosystem. Unless you are clear on your current starting point, then your roadmap to developing the organisation’s innovation ecosystem and thus maturity will at best be packed with assumptions, and at worst will actively prevent further development.

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To illustrate this point, imagine an ecosystem where individual intrapreneurs are too afraid to propose a new idea for fear of being punished by senior leaders. The result is a direct limit to the number of ideas submitted, which will impact the company’s growth in the years to come. This is a clear cultural blocker that needs addressing before making any further investment. If this blocker is not flagged early on, any investment in capability development, for example, will be money poured down the drain. Or another example, if the C-level can’t agree on a clear strategy, the investments are likely to be random. Again, this strategy blocker will have dire implications for the company’s future. Therefore creating, for example, a corporate venture capital fund before developing a clear innovation strategy won’t yield expected results as investments will not be deliberate acts or strategically aligned. When it comes to improving an ecosystem, another critical aspect to understand is that just doubling down on something that’s already working will not increase outcomes and, in certain situations, may even result in something to the contrary happening. Hence all elements of the ecosystem need to be not just in sync, but equally mature. For example, merely increasing the R&D budget won’t pay off if another factor holds this variable back. ‘There is no statistically significant relationship between how much a company spends on its innovation efforts and its sustained financial performance’, states a Price Waterhouse Cooper research paper. A testament to which is the discrepancy that’s evident when you put Boston Consulting Group’s list of the Top 50 Most Innovative Companies in the World next to Statista’s Top 20 R&D Spenders in the World. Only three of the top ten spenders are in the top ten innovators. To understand the maturity of each element making up an ecosystem, one first needs to understand the difference between attributes and outputs. For an analogy: should one be considered mature because they brush their teeth and look smart at work or because they are emotionally well developed and bring thoughtful and reasoned arguments to the benefit of the organisation, its people and customers? Outward appearances don’t always reveal what is going on inside; so, measuring random ‘things’ rather than attributes won’t help you unpack and define what is truly going on. When you take ‘things’ away, what remains is a complex interaction between the five pillars of the ecosystem. Drawing those strands together produces an innovation maturity dashboard that, used correctly, informs and guides the creation of an aligned roadmap of required interventions and hence, the progress of the organisation. The maturity of an ecosystem can be divided into many levels, but from our experience, we’ve found that four levels work best.

To Drive Innovation, You must Understand your Ecosystem

Four levels of maturity Novice

Just because an organisation is at the novice end of the spectrum doesn’t mean that there is no innovation activity. On the contrary, there might be little spurts of ad-hoc activity in one or more departments and innovation may occasionally be discussed amongst the leadership team. But discussion is typically as far as it gets. With no leadership sponsorship and no innovation strategy, any positive outcomes are likely to be accidental rather than designed and generally low value. Competent This level requires not only cultural change but also a sea change in the leadership approach. At this level, leaders now recognise the need for innovation as a driver of required outcomes. To this end, they have likely developed a basic innovation strategy and are prepared to sponsor innovation, albeit at a limited level. Some innovation training has usually taken place, and the organisation will likely have looked to incorporate essential innovation tools into the mix. However, organisations at this level still typically focus on short-term outcomes, and there is a fair chance that the innovation strategy and corporate strategy remain misaligned. That’s certainly not the case for the third level. Expert Attaining this new level of maturity sees a cultural and leadership shift from regarding innovation as an add-on to recognising it as an intrinsic driver of growth across the organisation. There is still some way to go, but at least innovation is now aligned to the core strategy with visible leadership sponsorship of innovation activity. At this level innovative ideas are starting to spread across the

Leader

organisation, becoming embedded in product and process design as well as influencing and informing how teams and projects are managed. Admittedly the focus remains on mid-term goals, but on the positive side, a suite of innovation tools alongside defined metrics and KPIs is starting to enable activity across the innovation mix. This fourth and highest level is achieved when preparation ends, and the full innovation journey begins. Innovation is no longer just aligned with the strategy. Instead, it is an intrinsic part of the strategy and fully embedded into organisational culture. Metrics and KPIs are fully integrated and deliver a realistic and rounded picture of organisational capability, thereby enabling product

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and process development to be focused on delivering innovation-led outcomes. There is also full leadership sponsorship alongside the development of a core innovation team. Together these help to transform business management, building innovation engagement and enabling people to innovate without fear of failure. This, in turn, moves the organisation’s viewpoint away from short-termism and towards the attainment of long-term goals.

Leadership matters A specific leadership culture is needed to achieve the fourth and highest level of innovation maturity. If we assume that innovation is baked into organisational culture, along with the tools of measurement to gauge its progress, the challenge of leading in that environment remains. In far too many instances, this is where efforts to build innovation ecosystems break down and fail to deliver on the innovation investment promise. Or, put another way, it is at this stage that many organisations put the wrong people into positions of leadership. Innovation is still primarily viewed as a technical accomplishment that requires leaders with strong technical skills. And while that may be true – your leaders certainly need to know the nuts and bolts of any specific project – it is not enough on its own to deliver on the final vision. Effective leadership today requires an expert-level grasp of both hard and soft leadership skills. What do we mean by soft skills? Increasingly, leadership development visionaries are pushing for a more holistic view of the skills necessary to motivate and engage employees, particularly when they are involved in a specific project with particular expectations and goals. Leading for innovation is about demonstrating a full range of skills that could be captured under the broad label of ’emotional intelligence’ (EI), a hot commodity now among leadership development gurus. EI has a broad and still evolving definition and can include everything from extensive and effective communication, cross-disciplinary collaboration and building a sense of community. However, there is a consensus around the need to develop more complex skills like empathy and compassion. Although they can be developed in most leaders, these are generally not included in the natural skill set of many strong technical leaders. The result is a significant gap between an organisation coming up with ideas (inventing) and executing and implementing those ideas (innovating). Bridging that gap is often the missing element in many promising projects that stumble before they come to fruition. Applying liberal doses of empathy and compassion, even in the development of

To Drive Innovation, You must Understand your Ecosystem

a purely technical challenge, has many different benefits. First, it makes it much more likely that an innovation team will reach its goal and bridge the gap between idea and execution. Leaders able to apply high levels of EI to their project oversight usually find the right outcome arrives on-budget and on-time more often and with a much higher degree of buy-in from the team. EI-driven leaders are also more likely to assemble a team that operates by consensus and mutual support. However, organisations that instil EI across all leadership levels also benefit from much broader and deeper engagement and acceptance of innovation by employees, greatly reducing the possibility that new innovative ideas developed by one team are resisted by the rest of the organisation.

Gauging an ecosystem’s maturity The best way to gauge an ecosystem’s maturity and needs is to apply a company-wide survey asking multiple-choice questions about every subsection of each of the five pillars. For example, questions around the ecosystem’s leadership could be along the lines of: •

Our leadership team provides the right support and resources to deliver on our company’s innovation goals and ambitions.

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Our leadership team is consistent in putting words into action around innovation.

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There is clear and visible leadership ownership and sponsorship for innovation.

For statistical accuracy, a review of this kind needs to be distributed both vertically and horizontally across the organisation. Note that to get a statistically accurate (within an agreed margin) high-resolution picture of what the ecosystem needs, the survey doesn’t need to be taken by everyone in the company. As long as it gets to a cross-section of people both horizontally and vertically, there is usually no need to get participation from more than 10% of the ecosystem. For increased accuracy, you can combine the survey data with an analysis of the ecosystem’s outcomes. If the findings from the survey can explain the shortcomings in the outcomes, now the ecosystem’s needs are clear. In a nutshell if one wants to improve an existing ecosystem, a continuous improvement process is made up of the following five steps: 1. Assess the current state of the ecosystem using both the survey method and analysis of its outcomes. 2. Understand the limiting factors or blockers of the ecosystem – the things

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that hinder the ecosystem’s level of maturity and its outcomes. 3. Tackle the blockers with appropriate actions (e.g. leadership development, process improvement initiatives, cultural transformation) 4. Re-measure the ecosystem once the steps have been deployed to evaluate the level of expected change. 5. Re-do the loop every time the ecosystem needs improvement. Successful innovation requires more than just process transformation; it calls for the entire ecosystem to have an innovative ethos and be equipped to tackle the challenges of today and the unfolding opportunities of tomorrow. This includes the need to have an appropriate HR strategy, a proper structure, demonstrable executive-level support, and a culture built around the pursuit of innovation. All this also built around a core purpose and a deep-rooted desire to pursue better. Action to improve any ecosystem shouldn’t be taken until the current situation is fully understood. So, analysis of the ecosystem needs to have the full support of the executive team if it is to uncover the ecosystem blockers preventing the company from achieving its growth goals. Making sure your family is a happy one is not a matter of luck nor will, but a matter of understanding the particular needs of your family. In the same way, developing your innovation ecosystem cannot be done by observing another company’s innovation strategy, capability or culture. Assessing your ecosystem should be the first step before any improvement measures are taken as they need to be specific to your ecosystem and your ecosystem alone.

Read the published article at LSE Business Review https://blogs.lse.ac.uk/businessreview/2020/10/29/to-drive -innovation-you-must-understand-your-ecosystem/

TRANSFORMATION IS PERSONAL

Ricardo Viana Vargas Executive Director – Brightline Initiative Tahirou Assane Oumarou Director of Operations - Brightline Initiative Emil Andersson Researcher - Brightline Initiative

Published in The London School of Economics Business Review London – United Kingdom – 2021

Transformation is Personal

Transformation is personal. Always. Think about it. Organizational change is brought about one individual at a time. A change of behavior or a change of outlook by one individual in an organization is the cellular basis of achieving transformation. Individual transformation of employees is (at least) as important as any organizational transformation. Without it, the chances of any transformation succeeding are low. Without it, the chances of an organization continuing to evolve and respond to new changes are almost non-existent. And yet if we look around at organizations time and time again we find lamentable levels of employee engagement and transformation programs which look good in a deck of slides, but which fail when they face the first hurdle of reality: convincing and motivating people to act. First, engagement. The research into employee engagement is uniformly depressing. Research by Gallup for its State of the Global Workplace report concluded: “Worldwide, the percentage of adults who work full time for an employer and are engaged at work — they are highly involved in and enthusiastic about their work and workplace — is just 15 percent. That low percentage of engaged employees is a barrier to creating high-performing cultures. It implies a stunning amount of wasted potential, given that business units in the top quartile of our global employee engagement database are 17 percent more productive and 21 percent more profitable than those in the bottom quartile.” It is worth repeating the key statistic: a mere 15 percent of people engaged in their work. With employee engagement at these abysmally low levels, organizations then instigate transformation programs with little consideration of or consultation with employees. It is no secret that people are recognized as the most important source of an organization's competitive advantage. And the same goes for transformation; the main hurdle to overcome in large change programs is the human one. Likewise, the main power to harness in large change programs is the human one. Regardless of which industry, geography or type of transformation, the solution is the same. Yet, paradoxically, we find that people are frequently the most misunderstood asset and least leveraged capability. It is easy to say how important people are, but it is often difficult to translate the recognition of this importance into implementation. There are subtle skills which need careful development individually and organizationally. Our own research at the Brightline Initiative suggests that hiring and retaining top-notch talent facilitates a smoother journey for transformation. But, make no mistake, ensuring you have the right people in the right places with the right skills is a big challenge. Across all the respondents in our research, reported success factors for imple-

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menting strategy included “sufficient resources” and “existing talent with the right skillset” as the top two ingredients. While “resources” can refer to many different things—capital, technology, equipment, time—it certainly includes people, teams, and sufficient talent. Across all respondents, 28 percent noted that their organization focuses more on developing internal talent; 50 percent stated they invest in both hiring and developing existing talent, but place a higher priority on the latter; 16 percent said they focus on both hiring and developing talent, but place more emphasis on recruitment; and just 6 percent noted they are more heavily focused on attracting external talent. When examined from the perspective of faster- versus slower-transformation organizations, we see significant differences. Faster-transforming organizations were nearly twice as likely as slower- transforming respondents (34 percent vs. 19 percent) to report a greater focus on developing internal talent. This highlights the importance of improving internal learning and development programs, while still continuing to invest in recruitment efforts. Transformation must come from within. Once the focus is on the organization’s internal talent, leaders must attempt to get everyone on the same page. The ultimate task is to change key beliefs and behaviors as changing and nurturing the organizational culture is one of the most difficult and complex things a leader can take on. Brightline's People-Centered Transformation Framework helps leaders establish a culture of aspiration, alignment, autonomy, and accountability. In practice it is built on these elements:

Communicate a compelling change narrative: A purposeful, passionate and emotionally resonant case for change provides motivation to re-shape the future.

Act to think differently: Leaders need to re-align their own thinking and behavior to act as role models of what transformation demands and involves.

Embrace situational humility: No-one knows all the answers to transformation. To discover the best organizational ways forward requires humility, willingness to ask difficult questions and admit limitations and frailties.

Focus on the few: A profusion of programs and initiatives provides a recipe for confusion. There must be a focus on a small number of targeted initiatives.

Motivate discretionary effort: Why do some people go the extra mile to make things happen to get things done? Passion, autonomy, and pride are all elements. Organizations need to develop rewards and recognition processes which enable these elements to be brought to bear on the organization’s most

Transformation is Personal

difficult problems.

Give others agency: How much independence are your people allowed? Research suggests that the ability of people to take independent action is key to transformation success.

Decentralize decision making: Centralization creates ponderous decision making. Decentralizing decision making can set people free.

Catalyze the network: Transformation is slowed by silos, bureaucracy and linear organization. Maximizing internal and external networks is the way forward.

Lead the system: Organizational systems and those who lead them must be geared to making change happen and anticipating when it is required.

Nudge the culture: Massive cultural overhauls are difficult if not impossible. Instead, cultures can be nudged in the right direction to embrace new behaviors and perspectives.

How to begin Transforming from within must begin with an appreciation of how people naturally perceive and experience change. Employees tend to view transformations in one of three ways – as a threat, as a burden, or as an opportunity. Too often leaders fail to recognize the fear of being replaced or minimized due to transformation, or actually stoke those fears through opaque or limited communication. Faced with these challenges we encourage each employee participating in the transformation, at any level, to focus on their personal strengths and unique contributions to the organization, and to connect these to the vision of the organization after transformation – helping them to understand how they can contribute to the transformation effort, and how the transformation will help their progression and growth. This self-evaluation exercise is typically a three-step process for each participant:

Define your aspiration of where you want to be – create a Personal Vision Statement that defines where you want to be. It is important that this is within the transformation context – and defines what you can contribute to the transformation, and what you want to get out of the transformation process.

Develop an understanding of yourself. There are multiple tools to choose from – organizations have had some success with personality tests such as Myers-Briggs, Enneagrams or GC Index, or tools such as StrengthsFinder.

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Develop a Personal Transformation Plan, and share with your transformation team as a method of making, and getting, a public commitment. A key aspect of leadership of transformation is instituting and responding appropriately to these reflections. At Brightline the primary tool we use for employee transformation is called the SEE Model. SEE stands for Strengths-Evokes-Elates. The three-part SEE model helps people define their calling within the context of transformation. The aim is to find the intersection between:

Your strengths – the areas of work in the business and the transformation where you are or are willing to become good at

The elements of the transformation that evoke personal meaning – the things that give you long-term satisfaction Actions that make you feel elated – activities and actions that give you immediate joy. Many organizations find it difficult to involve employees so intimately in forming and leading the transformation. But, never forget that change is a human endeavor and, as such, can make delivering strategy a messy and complicated process. People have different interests, motivators and tolerance – which influence behaviors and create potential misalignment and barriers. Their response may not appear rational at first, because change is often processed as a threat. New strategies always require different ways of working, so leaders must recognize the time and effort required to shift individual interests, mindsets and behaviors. Once that is practiced, employees will in return feel engaged and help leaders make the transformation a success.

Resources Gallup. (2017). State of the Global Workplace. Gallup, Inc. https://www.gallup. com/workplace/238079/state-global-workplace-2017.aspx O'Driscoll, T. (2020). People-centered Transformation is an Urgent Priority for a Fast-changing World. Brightline Initiative. https://www.brightline.org/resources/ people-centred-transformation-is-an-urgent-priority-for-a-fast-changing-world/ Brightline Initiative. The Brightline Transformation Compass. Brightline Initiative. https://www.brightline.org/resources/transformation-compass/ Brightline Initiative. (2019). The Transformation Playbook. Brightline Initiative & Thinkers50. https://www.brightline.org/resources/thinkers50-transformation-playbook/ Read the published article at LSE Business Review https://blogs.lse.ac.uk/businessreview/2021/01/12/transformation-is-personal/

TRANSFORMING TRADITIONAL BUSINESS DOESN’T HAVE TO BE AN ELUSIVE GOAL FIRMS NEED TO DEVELOP AN APPETITE FOR INNOVATION, LEVERAGE KNOWLEDGE ABOUT CUSTOMERS, AND ADOPT AGILE WAYS OF WORKING

Published in The London School of Economics Business Review London – United Kingdom – 2020

Transforming Traditional Business Doesn’t Have to Be an Elusive Goal

Even though the literature on business transformation is enormous, managerial and organisational appetite for change tends to be limited. Leaders of organisations that are remotely successful are not inclined to change things. Transforming the business is not the same as implementing incremental change or improvements in processes, operations and products. We argue that transformation refers to an organisation achieving a sustainable quantum-leap improvement in performance while transforming the mindsets of employees and thus the culture of the organisation. Few individuals or corporations have managed to change successfully, and even fewer have done so repeatedly. And, of course, transformation programs have a low success rate anyway: research has shown that fewer than a third of organisational transformations actually succeed at improving a company’s performance and in sustaining those gains. Similarly, only 16 per cent of digital transformations succeed. Transformation needs not be so elusive for established organisations. The right vision, knowledge about needs and transformation know-how can convert strategic apathy into action. First they need to understand their own capacity to change and the fundamental building blocks of successful transformation. The Brightline Transformation Compass offers a guide for organisations, established or up and coming, to successfully implement enterprise-wide change programs and transform their future for the better.

Develop an appetite for innovation A bold move or business innovation is preceded by a crisp, inspiring articulation of the vision and strategic objectives for the change initiatives. This is highly critical for established organisations that want to innovate their businesses. Visions tend to become stale over time and revisiting them can look like a cynical exercise rather than a genuine pursuit of change. But, it can – and increasingly must — be done. One of the most eye-catching stories of transforming from a position of strength is the Chinese white goods manufacturer Haier. Having surpassed GE in market share, Haier completed its acquisition of GE’s appliances business for $5.6 billion in 2016. It is now the global number one in white goods with an operating revenue of $28 billion in 2019. In just over three decades it has risen from being a tiny, inefficient state-owned enterprise based in an unpromising part of China to become a global champion. Central to the Haier story is its appetite to reinvent itself time and time again. A

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pithy statement of the company’s strategy has been key at each stage. Every few years this is completely overhauled, as the company points in a sometimes radically different direction. “What Haier has done is be more aggressive and more comprehensive in the way they’ve thought about reinvention,” says IMD’s Bill Fischer, co- author of Reinventing Giants (with Umberto Lago and Fang Liu). “They have moved before they needed to: whether it was building a brand reputation for quality, or whether it was about being a great service provider, or being intimate with the customer. Each of those was done earlier, when resources were abundant, and people felt good about the company, rather than later.” The leader behind this energy and inspiration is Zhang Ruimin, chief executive of Haier Group. CEO Zhang, as he is known within Haier, believes in relentless and ongoing revolution. And he puts it into practice. “There are no successful companies,” he says, “only companies that remain relevant.” Haier has done just that. During the 1980s, Haier dedicated itself to brand building. In the nineties the focus shifted to diversification with a variety of mergers and restructurings. The Haier refrigerator brand was extended to a range of other home appliances – washing machines, air conditioners, microwave ovens, televisions, computers and more. From there, Haier moved to internationalisation with an emphasis on localised research and development, manufacturing and marketing. Its international moves were bold – eschewing easier and closer markets, it headed to the United States and Europe, highly competitive markets, with high quality expectations. Following on was Haier’s global brand stage, its acceptance as a powerful worldwide brand. This ability to come up with a motivating and directing North Star is not unique. General Electric did something similar under Jack Welch in the 1980s and 1990s. Welch was a great communicator and each period of his tenure was marked by a clear and understandable aspiration or target – for example, being number one or two in every market GE competed in. Some dismiss this as a catchphrase or a vision, rather than a strategy. Whatever the semantics, it is a powerful means of focusing attention on what the company needs to do.

Leverage knowledge about customers Few people question the importance of understanding and leveraging customer insights and megatrends for any business purpose. Yet, a global study conducted for Accenture Interactive reveals that less than 60 per cent of all companies

Transforming Traditional Business Doesn’t Have to Be an Elusive Goal

directly involve their customers in developing better customer experiences. Furthermore, the companies that are leveraging and mastering customer experience in their transformation journeys realise higher rates of success in market differentiation, scale and efficiency, customer loyalty, revenues, return on investment, and cost savings. What is clear is that there is a strong need for more effective outside-in approaches to transformation initiatives. What is notable about a company like Haier is that the customer is at the centre of its journey. In the 1990s, Toyota and other quality pioneers led the way to Zero Defects. Haier takes this production standard and, instead, talks of Zero Distance. It aims to cut down the distance between consumers and creators. For this reason, it removed entire swathes of middle managers. Now, Zhang Ruimin is talking about the UX Cloud, the user experience cloud, which provides the best, seamless experience for users. Or consider the American agri-industrial business Archer Daniels Midland (ADM). Over a century old, ADM found that its growth opportunities and margins were diminishing as the dynamics of its industry changed. It returned to basics – things that had been increasingly overlooked – and invested time and money in figuring out the key trends and markets so that it could really understand the new competitive environment. Once ADM started looking more rigorously at the new world order, it found increasing demand for natural flavour and ingredients and greater worldwide protein consumption. This informed ADM’s strategy. It acquired companies with expertise in the emerging areas and built up expertise and infrastructure that reflected the marketplace reality. It increased the diversity of its product portfolio and developed a global food processing business.

Adopt agile ways of working A third key block highlights the need to adopt a flat, adaptable and cross-functional transformation operating system that enables speed and mobility for the employees driving the change. It is clearly very difficult for established companies to shift structures and ways of working. This challenge demands ambidextrous ways of working, the ability to do two potentially contradictory things at the same time. At its heart is the ability to transform the organisation while keeping up with the core business. This is what we call the “the two-speed model” companies need in order to be able to keep the business while changing it. This requires business agility capabilities. Global research conducted by the Project Management Institute (PMI) identified key strategies to help organisations develop agility competences:

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Quick responses to strategic opportunities

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Shorter decision / production review cycles

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Focus on change management

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Integrating the voice of the customer

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Focus on risk management and anticipation

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Interdisciplinary product teams

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Elimination of organisational silos

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Contingency planning

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Use of interactive project management practices

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Leverage technology

Mastering all this is unquestionably highly demanding. But the number of examples of traditional organisations that have successfully transformed themselves is growing. In 2012 Haier announced its entrance into a fifth development stage, named networking strategy. “The internet mindset for a business should be a zero distance and networked mindset,” Zhang Ruimin explained. “The internet has eliminated the physical distance and enabled businesses to become networked. The competitive tension among a company, its employees and its partners should be defused with the aim of building a collaborative, win-win ecosystem.” And now, Haier is in stage six: what it calls an ecosystem of micro-communities, leveraging many of the agile business traits such as an operating system consisting of small, autonomous and self-managed teams. As this has been widely successful and brought Haier closer to the customers, Zhang Ruimin is already laying plans for the next corporate refresh. Another example is the ING Group. Back in 2015, ING identified the need for more effective ways to deal with hyper competition, regulatory challenges, and market shifts. The Dutch bank launched the program “One Way of Working”, a strategy that incorporates the principles of “agile” methodology to improve flexibility, innovate faster with shorter time to market, minimise handovers, and provide employees with greater freedom and responsibility than in a traditional company approach. They started by shifting the organisational structure. ING created 350 nine-person squads — self-directed and autonomous teams comprising representatives from various functions, from marketing to IT. These small teams have end-to-end responsibility for specific, customer-focused projects. At the end of a project,

Transforming Traditional Business Doesn’t Have to Be an Elusive Goal

squads are disbanded, and the members arrange to work in other squads. Collections of squads, totalling no more than 150 people, are called tribes, and are united in a common purpose. For example, within the tribe responsible for payments, employees may work on credit cards, payments and merchant payment solutions for Dutch and Belgian retail and wholesale banking customers. This resulted in a more effective operating system and an organisational structure that enabled collaboration and fast decision-making. The way the teams worked better fits the pace of change and means they are better equipped to overcome different business challenges.

Notes •

This blog post expresses the views of its author(s), not the position of LSE Business Review or the London School of Economics.

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Featured image by geralt, under a Pixabay licence

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Read the published article at LSE Business Review https://blogs.lse.ac.uk/businessreview/2020/07/31/transforming-traditionalbusinesses-doesnt-have-to-be-an-elusive-goal/

TEN QUESTIONS TO HELP YOU TURN STRATEGY INTO REALITY

Ricardo Viana Vargas Executive Director – Brightline Initiative Edivandro Conforto Head of Strategy Research – Brightline Initiative

Published in The London School of Economics Business Review London – United Kingdom – 2018 Chief Strategy Officer Playbook London – United Kingdom – 2019

Ten Questions to Help You Turn Strategy into Reality Related Podcasts

♫ Think about how to connect

your strategy design with your ability to deliver – or pay the price http://rvarg.as/yn/

♫ The Strategic Execution Framework (SEF) http://rvarg.as/db/

♫ Strategy and Delta Model Part 1 of 2 http://rvarg.as/x84/

♫ Strategy and the Delta Model – Part 2 of 2 http:// rvarg.as/x83/

Sustainable growth depends on delivering the right strategies the right way. Yet this is something that organisations appear ill-equipped to do. Our work at the Brightline Initiative is examining1 what causes the gap between strategy and implementation and how it can be closed. Our research suggests that business leaders need to answer ten key questions for their strategies to more effectively make the leap from design to delivery.

1. Is delivery as important to you as design? Strategy design can be an exciting process. It is cerebral, based on marshalling facts and figures, views and opinions, hunches and suggestions. Creating a coherent and persuasive strategy is part art and part science. It is demanding, difficult and vitally important, but without implementation it is nothing. In a 2017 Economist Intelligence Unit global survey2 of 500 senior corporate executives, over 90 per cent admitted they failed to reach all of their strategic goals because of flawed implementation.

2. Does the leadership team take responsibility for delivery too? Once you have defined and clearly communicated the strategy, your responsibility shifts to tracking implementation. You need to know where in your organisation change happens and who manages the programs that drive change. You proactively address emerging gaps and challenges that may impact delivery. Without this discipline, your strategy has little chance of success.

3. Do you mobilise the right resources? Consulting firms and more fluid organisations are adept at ensuring that their best people are working on the projects that need them most. In contrast, hierarchical and other organisational structures can limit mobility and many organisations struggle to release their best minds to become involved with implementing key strategies. “Always put your best talent on critical initiatives and make sure that you reward them accordingly, but more importantly make sure that they will have roles after the completion of the assignment,” advises David Marlow, company transformation lead at Bristol-Myers Squibb, in his article3 at the Strategy@ Work book.

1 https://www.brightline.org/resources/economist-qa-preventing-a-frozen-middle/ 2 https://www.brightline.org/resources/eiu-report/ 3 https://www.brightline.org/resources/thinkers50-strategy-at-work-book/

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4. Do you leverage insight on customers and competitors? Organisations atrophy when they lose touch with their customers, whereas the best leaders are constantly seeking insights from customers and competitors. In the EIU survey 53 per cent of companies said that changing customer expectations/demands impeded implementation. As a result, the majority monitored customer trends. But only one in five reported having effective feedback loops to use all of this data in strategy delivery. In a Harvard Business Review article4, Amy Edmondson and Paul Verdin argue that customer input – at all stages – is a vital lubricant of strategy and execution. It creates what they call “strategy as learning”.

5. Is your implementation bold, focused and as simple as possible? Delivery is complex – often global, always interconnected. To succeed the constant emphasis must be on boldness, focus and simplicity. The Boston Consulting Group talks of “smart simplicity”5. This involves empowering people sufficiently for the requirements of their jobs by giving them the right resources, removing unnecessary constraints, and aligning interests by consequences to actions and results.

6. Do you promote team engagement and crossbusiness cooperation? Support and understanding of the strategy throughout the organisation: Middle and line managers must be engaged and activated as strategy champions rather than just as managers and supervisors. In the EIU survey, 62 per cent of respondents said that lack of buy-in from middle managers, line managers or both created a significant barrier to strategy implementation at their organisations. Second the understanding that teams are the means by which strategies are delivered. The Boston Consulting Group refers to performance integrity6. By this it means a highly motivated and thoughtful project team with a bias for action, clear on its objectives, with a strong leader and sufficient member resources, plus the right mix of skills for the effort.

4 https://hbr.org/2017/11/your-strategy-should-be-a-hypothesis-you-constantly-adjust 5 https://www.bcg.com/en-us/capabilities/smart-simplicity/smart-simplicity-concept.aspx 6 https://hbr.org/2005/10/the-hard-side-of-change-management

Ten Questions to Help You Turn Strategy into Reality

7. Do you own the decisions you make? Commit to making strategic decisions rapidly. Move quickly to correct course, reprioritise, and remove roadblocks. Accept that you likely won’t have all the information you want, and rely on those you can trust to deliver sufficient reliable input to allow thoughtful decisions. Consider and address risks and interdependencies explicitly—both upfront and regularly throughout delivery. Build a lean and powerful governance structure to reinforce accountability, ownership, and a bias towards action, based on agreed metrics and milestones.

8. Do you check ongoing initiatives before committing to new ones? Organisations tend to overburden people with strategic initiatives of one form or another, creating change fatigue. You must regularly evaluate your portfolio of strategic initiatives. Add new initiatives in response to new opportunities, but first be sure you understand both the existing portfolio and your organisation’s capacity to deliver change. Agility, the organisational capability to quickly adapt strategy in response to external and internal changes, plays an important role in both the governance and management of any portfolio of strategic initiatives.

9. Do you develop robust plans but allow for missteps? We live in fast-moving times. Even the most far-reaching and elaborate strategies must contain room for learning and realignment. Strategy planning cycles are more rapid, dynamic, and agile than in the past. Delivery teams must be able to experiment and learn in an environment where it is safe to fail fast. In practice, this means you must discuss challenges openly, and adjust the plan as needed for success. Learn to reward failure, or at least accept it as a valuable input. In agile organisations it is common to have frequent interactions between teams and leaders, to review and understand what went wrong, what worked well and what needs to be improved. It is a powerful iterative process. This is not simply about communication, it is about interaction up, down and across the company. Interestingly, what appears to be evolving is acceptance of the need of a hybrid approach to strategy. The intention is to manage the current business and operations while dynamically seeking out the next disruptive idea. This can be seen at Volkswagen Group according to this report7. The company’s chief strategy officer Thomas Sedran talks of a “two-speed organisation” – “One part goes with proven 7 https://s3.us-east-2.amazonaws.com/brightline-website/downloads/reports/brightlineeconomist-report.pdf?utm_source=resource-page&utm_medium=skip-link

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processes that lead to reliable products. This is necessary for those developing vehicles because delivering an unsafe one can cost you the company. For the other part, where you need to be much quicker, but where the impact of a failure is less severe, you need to be agile and have different planning and execution processes.”

10. Do you celebrate success and recognise those who have done good work? Success in organisations is often whispered rather than celebrated. If strategy delivery is to become embedded in any organisational culture, successes must be widely and wildly celebrated. It is notable how adept the best business leaders are at acknowledging good work. They tend to have developed a personal means of communicating great work – a handwritten note, a phone call. Any leader who can answer these ten questions in the affirmative is well placed to translate strategy into delivery. Even failing to deal with the issues raised by one of the questions can fatally handicap even the most brilliant strategy.

Notes: This post gives the views of its authors, not the position of LSE Business Review or the London School of Economics. Featured image credit: Photo by qimono, under a CC0 licence. For more information about the Brightline Initiative visit www.brightline.org8

8 https://www.brightline.org

THE INNOVATOR’S MINDSET: RADICAL CAN-DO

Ricardo Viana Vargas Executive Director – Brightline Initiative Edivandro Conforto Head of Strategy Research – Brightline Initiative

Published in Rotman Management Magazine Toronto – Canada – 2018

The Innovator’s Mindset: Radical Can-Do Related Podcasts

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your strategy design with your ability to deliver – or pay the price http://rvarg.as/yn/

♫ The Strategic Execution Framework (SEF) http:// rvarg.as/db/

♫ Strategy and Delta Model Part 1 of 2 http://rvarg.as/x84/

♫ Strategy and the Delta Model – Part 2 of 2 http://rvarg.as/x83/

When business leaders are quizzed about what it takes to succeed today, they tend to suggest the usual suspects. Innovation is one constant refrain. But, look around. While radical innovation is a common aspiration, incremental innovation is the more frequent reality. Very few products or services are truly innovative. Is leadership to blame? Harvard Professor Rakesh Khurana estimates that anywhere from 30 to 40 per cent of the performance of a company is attributable to ‘industry effects’; 10 to 20 per cent to cyclical economic changes; and perhaps 10 per cent to the CEO. Our experience suggests this is a maximum figure rather than a mean. Originality is similarly over-rated. Research by Costas Markides and Paul Geroski of London Business School has highlighted the myth of the importance of being the ‘first mover’. Markides and Geroski point out that Amazon was not the first company to focus on online retailing; nor was Charles Schwab the originator of online share dealing. The commercial genius of Amazon and Schwab was to take existing technology and bring it to the mass market. Hardly original—but hugely successful. So, if it’s not leadership or originality, what accounts for innovative success? The reality is that time and time again, the most innovative organizations are characterized by what we describe as relentless execution driven by a radical ‘can-do’ attitude and aptitude. They realize that the best strategies in the world will get you nowhere unless you relentlessly make them happen. Consider how Henry Ford revolutionized the auto industry at the turn of the 20th century. More than 17,000 Model Ts were sold during the first year of production. Four years earlier, the world’s entire automobile industry produced 22,000 cars. In the 19 years of the Model T’s existence, Ford’s production total amounted to half the auto output of the world. Along the way, the entrepreneurial crowd that had set out to conquer the new auto market became a select corporate few. In 1914 the Ford Motor Company, with its 13,000 employees, produced 267,720 cars; the other 299 American auto companies with 66,350 workers produced only 286,770 cars combined. Ford had 48 per cent of the American car market, with $100 million in annual sales. Ford’s route to success was not about originality. There were a host of others ahead of him in terms of automotive technology. The fledgling automobile market was highly competitive and crowded. People like Charles Edgar and J. Frank Duryea, Elwood Haynes, Hiram Percy Maxim and Charles Brady King were all chasing the great automotive prize.

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Ford’s much-vaunted production process was actually an adaption of something that was already being used elsewhere: The assembly line had been used in the food processing industry in the Midwest United States since the 1870s. Ford himself is reputed to have toured Chicago’s meat-packing plants in search of inspiration, noting that the meatpackers had an overhead trolley to speed up production. He also would have noted the operations of Sears, Roebuck’s Chicago mail-order plant, which opened in 1906. At the time, it was the largest business building in the world, with three million square feet of floor space. Time and time again, when you analyze the route to significant commercial success, you find that the magical, sexy stuff of innovation and entrepreneurialism isn’t the main reason why an organization succeeded. Innovation and entrepreneurship are, of course, crucial, but successes like Ford and Amazon show us that there is a premium on making things happen and getting things done. Unfortunatley, this is something modern organizations—backed by the full panoply of technological marvels at their disposal—remain poor at. A full 90 per cent of respondents to a 2017 global survey of 500 senior executives from companies with annual revenues of over $1 billion, conducted by the Economist Intelligence Unit (EIU), admitted that they had failed to reach their strategic goals because of flawed implementation. More than half (53 per cent) of respondents admitted that ineffective implementation of strategic initiatives had a profound impact on the organization’s competitive advantage and performance. In addition, the 2017 Project Management Institute’s Pulse of the Profession Survey found that for every $1 billion invested, $97 million is wasted through poor implementation performance. The trouble with executing innovation is that, unlike formulating a clever strategy in a boardroom, it has to take shape in the real world. And that involves making hard decisions—and taking risks.

Can-do Capabilities The relentless execution of innovation has four key characteristics:

Clarity The beauty of relentless execution is that it concentrates managerial minds. At its best, it cuts away superfluous activities and targets all of an organization’s resources on what really matters to get things done. While other auto-makers made their models more complicated and expensive, Henry Ford provided extreme clarity: the company would produce just one affordable car, in just one colour (black). This brand of smart simplicity is common in radical can-do organizations.

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Consider the Chinese bike sharing company ofo (always in lower case to mimic the shape of a bicycle). “Ofo was created for sharing and aims to unlock every corner of the world by making bicycles accessible to everyone,” says the company’s website. Ofo began life in 2014 when CEO Dai Wei and his co-founders saw an opportunity to leverage smart technology to improve cycling as a sustainable form of transportation. As students at Peking University, Wei and his partners convinced about 2,000 students to add their bicycles to a private registry. The registry could be accessed through a mobile app and allowed participants to use any registered bike, anywhere, at any time. Today, ofo is operating in over 250 cities across 20 countries: China, Singapore, the UK, the U.S., Kazakhstan, Malaysia, Thailand, Austria, Japan, Russia, the Czech Republic, Italy, the Netherlands, Australia, Spain, Portugal, Israel, Hungary, India and France. It generates 32 million transactions daily and is valued at more than $3 billion. In 2017, it raised $700 million of funding from, among others, Alibaba, Hony Capital and CITIC Private Equity. Once again, ofo is not breathtakingly original. Thanks to Airbnb and Uber, the sharing economy is an increasingly mainstream idea. The Velib bike-sharing network was introduced in Paris in 2007 and a similar program was introduced in London in 2010. Ofo’s differentiator is its ‘dockless’ technology: Its eye-catching yellow bikes are hired through a smartphone app and can be picked up and dropped off anywhere that bike parking is allowed. In addition, Ofo does not require riders to pay a deposit before use. To access a bike, they can simply download the ofo app and unlock their nearest bike via Bluetooth. Once their ride is complete, users close the lock to complete their ride and make it available for the next person to use. Ofo uses ‘geo-fencing’ technology to ensure that riders use the bikes within the designated ‘Home Zone’ shown in the app. Such simple, practical technology is driven by a bold sense of purpose. Ofo is engaged in a constant round of raising finance and opening in new territories. Success in any highly competitive global market requires constant momentum, relentless execution enabled by clarity of purpose.

Redefining Focus “You have to continually prioritize,” says David Marlow, company transformation lead at Bristol-Myers Squibb. “There are always competing activities going on in any organization and different functions wanting to do things with a different view of what to prioritize. This requires a senior management team to look at the enterprise level, at the overall amount of activities going on, and conduct a pri-

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oritization assessment once or twice a year. This will ensure a focus on where the big value drivers are and that they are being properly resourced and prioritized.” Such focus is a powerful commercial weapon, but it needs to shift and change with time. Today’s focus will not be appropriate tomorrow. This was something that Jack Welch practiced impressively as CEO of GE in the 1990s. Welch had a knack of distilling down his company’s strategy to memorable phrases: ‘Number one or two in every market’; ‘getting the work out of the company’; ‘dot-com your business’. Each slogan was repeated endlessly by Welch as he travelled through GE and beyond, providing focus for the company; but then he would always move on to provide the next focus. A similar approach has been taken by the CEO of Chinese white- goods manufacturer Haier. As the company has evolved from a small local player into the world’s leading white goods company, its CEO Zhang Ruimin has changed the company’s focus time and time again. “In the last three decades of dedication to entrepreneurship and innovation, Haier has been through five stages of strategic development, each of which represents a major endeavour of management innovation,” he explains. “Our most recent strategic adjustment began in 2012, when we transitioned our company from being a traditional manufacturer to become a platform-based and networked organization in line with the lifestyle changes brought about by the internet.” During the 1980s, Haier dedicated itself to brand building. Its realization was that to compete internationally it had to raise the standards of its products. Zhang Ruimin proposed the principle of “a late starter with a high starting point”. Famously, in 1985, after receiving letters from consumers complaining about quality problems with Haier refrigerators, Zhang joined employees in demolishing 76 of the sub-standard refrigerators with sledgehammers. The point was made: Haier had to match or exceed the highest quality standards. In the 1990s, the company’s focus shifted to diversification, with a variety of mergers and restructurings. The Haier refrigerator brand was extended to a range of other home appliances—washing machines, air conditioners, microwave ovens, televisions, computers and more. From there, Haier moved to focus on internationalization, with an emphasis on localized R&D, manufacturing and marketing. Its international moves were bold: Eschewing easier and closer markets, it headed to the United States and Europe, where markets were highly competitive and quality expectations high. Following this stage, the focus was on was Haier’s global brand stage—it’s acceptance as a powerful brand presence worldwide. In December 2012, Haier announced its entrance into a fifth development stage: networking strategy. “The ‘Internet mindset’ for a business should be zero dis-

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tance and networked mindset,” Ruimin has explained. “The Internet has eliminated physical distance and enabled businesses to become networked. The competitive tension among a company, its employees and its partners should be defused with the aim of building a collaborative, win-win ecosystem.”

Commitment to Change For companies dedicated to relentless execution, the only danger is to press pause. Yet this is a fairly regular occurrence. As small entrepreneurial companies expand into larger ones, there is a temptation to ‘bring the grown-ups in’ to add bureaucracy and processes. Sometimes, this works, but it can bring an entrepreneurial rocket to an abrupt halt. Just look at Steve Jobs’ initial departure from Apple as the company sought to bring management discipline to bear. A commitment to change must be real and constant. The challenge of change is to instigate it from a position of strength. Repeatedly, companies attempt to change things as their performance deteriorates or their market strength evaporates. Not Haier nor the other relentless can-do companies. Indeed, the more successful they become, the greater their apparent appetite for change. This continuous appetite for change is exemplified by Netflix CEO Reed Hastings. “You have to fight the idea that as you get bigger, your culture gets worse. At Netflix, we’re significantly better [than we were] because we have more brains thinking about the problem. If you have 1,000 really thoughtful people thinking about how to improve, you’ll make a lot more progress than if you have 100,” he says. “Some companies operate by the principle of the product genius at the top. There’s this whole motif that to be a great CEO you have to be a great product person. That’s intoxicating and fun, but you build in incredible amounts of dependence on yourselves. You’re much stronger building a distributed set of great thinkers.” In 2010, Netflix began operating in Canada; in 2011, Latin America, Central America and the Caribbean; in 2012, the UK, Ireland and Scandinavia; in 2013, the Netherlands; in 2014, Austria, Germany, France, Belgium, Luxembourg and Switzerland; in 2015, Australia, New Zealand, Japan, China, Italy, Portugal and Spain; and in 2016 it expanded into more Asian countries. “You are witnessing the birth of a global TV network,” proclaimed Hastings as another 130 countries were added to the company’s reach to take the global figure to nearly 200 countries from Afghanistan through to Zimbabwe.

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Radical Failure Of course, with a radical can-do mindset comes the inevitability of failure. Role modelling failure starts at the top of any organization, division, team or small company. “If we’re not making mistakes, we’re not trying hard enough,” observed James Quincey when he became CEO of Coca-Cola in 2017. Amazon’s Jeff Bezos has been similarly vocal in making it clear that failing is an integral part of succeeding. “If you’re going to take bold bets, they’re going to be experiments. And if they’re experiments, you don’t know ahead of time if they’re going to work. Experiments are by their very nature prone to failure. But a few big successes compensate for dozens and dozens of things that didn’t work.” At Netflix, where the culture is built around what they call ‘radical candor’, Hastings is an outspoken champion of failure. “You should have more things that don’t work out,” he has observed. “As you grow, the drive towards conformity is substantial. As a leader, you want to drive people to take more risks.” Speaking at a recent conference, Hastings lamented: “Our hit ratio is way too high right now. I’m always pushing the content team. We have to take more risks. We have to try more crazy things, because we should have a higher cancel rate, overall.”

Getting Close to Customers “Innovators and entrepreneurs rarely fail because their initial idea was stupid,” observes Alex Osterwalder, co-author of Business Model Generation. “They don’t succeed because they fail to listen to the reality of the market. They fail to iterate their idea and adapt their value proposition and business model until it’s scalable and profitable.” The radical can-do mindset is not about pressing ahead and blindly doing things for the sake of doing them. As indicated, it requires clarity and focus, but it also demands an in-depth knowledge of what your customers want and what they aspire to. In the most successful projects we have witnessed, there is a strong correlation between employee engagement and awareness of what customers want and need. “Insight into customer needs and competitor moves, combined with the speed and agility to capitalize on this insight, will be increasingly critical for competitive success,” predict Perry Keenan and his colleagues at the Boston Consulting Group. It is especially challenging for companies in fast-moving markets to connect with the future aspirations and needs of customers. It is interesting to see the work of the modern-day Ford Motor Corporation in this area—given the identification with customers exemplified by its founder. “Henry Ford’s mission was to help people move. That’s still at our core, but now we’re looking at the larger scope of

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what ‘mobility’ means,” observes Jamel Seagraves, an advanced research engineer at Ford. For Ford, re-connecting with this original purpose has brought the company to Silicon Valley. Among its interesting takes on ‘its place in the world’ is that tech companies are increasingly automotive suppliers. In 2015, it opened the Ford Innovation and Research Centre in Palo Alto. The Center is led by a former Apple engineer and is charged with exploring the future of mobility with a team of 160 researchers, engineers and scientists. Among its projects are autonomous vehicles and helping cyclists and motorists share roads.

In closing Getting things done with a relentless can-do attitude demands a degree of boldness and bravery that few executives and organizations are encouraged to demonstrate. These characteristics are founded on a deeply-held belief that the status quo is not good enough—that things can and must get better; that getting out there and connecting with people is preferable to building internal walls; and that failure is inevitable along the way. As indicated by the stories of Amazon, ofo, Netflix, Haier and many others, fortune truly favours the brave.

GREAT STRATEGIES NEED GREAT DELIVERY: THE 10 PRINCIPLES OF IMPLEMENTATION EXCELLENCE

Ricardo Viana Vargas Executive Director – Brightline Initiative Edivandro Conforto Strategy Research Advisor – Brightline Initiative

Published in Strategy@Work: From design to delivery London – United Kingdom – 2017

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Great Strategies Need Great Delivery: The 10 Principles of Implementation Excellence

Related Podcasts

♫ Think about how to connect

your strategy design with your ability to deliver – or pay the price http://rvarg.as/yn/

♫ The Strategic Execution Framework (SEF) http://rvarg.as/db/

♫ Strategy and Delta Model Part 1 of 2 http://rvarg.as/x84/

♫ Strategy and the Delta Model – Part 2 of 2 http://rvarg.as/x83/

Every CEO or leader is accountable for overseeing strategy design and delivery in his or her organization. Leaders also recognize that strategy implementation excellence is central to the organization’s sustainable growth and prosperity. Yet most strategic initiatives fail because of flawed implementation, at great cost in time and resources. Consider these data points:

• A full 90 per cent of respondents to a 2017 global survey of 500 senior exec-

utives from companies with annual revenues of over $1 billion, conducted by the Economist Intelligence Unit (EIU), admitted that they failed to reach all of their strategic goals because of flawed implementation.

• More than half (53 per cent) of respondents to the EIU survey admitted that

ineffective implementation of strategic initiatives has a profound impact on the organization’s competitive advantage and performance.

• In addition, the Project Management Institute’s Pulse of the Profession Sur-

vey found that for every $1 billion invested, $97 million is wasted through poor implementation performance.

Mind the strategy implementation gap Global competition and a networked society are driving almost daily changes in the competitive landscape. In this highly kinetic environment, sustainable growth will depend on delivering the right strategies the right way. Yet the average organization fails to meet at least 20 per cent of its strategic goals as the result of implementation issues and challenges, according to the 2017 EIU survey. An earlier EIU study, published in 2013, showed that just a little over half of the strategic initiatives (56 per cent) have been successful. The dynamic interplay between strategy design and delivery starts at the moment the organization defines its strategic goals and investments. Most organization leaders appear to understand the importance of implementation and acknowledge they need to upgrade their delivery capabilities. At least 59 per cent of respondents to the most recent EIU survey acknowledge a gap between their strategy design and implementation and recognize its negative impact on organizational effectiveness. That’s barely an improvement over the 2013 EIU survey, when 61 per cent of respondents admitted to performance-sapping shortfalls in implementation. There is, however, no single true path to implementation excellence. Because there are several frameworks for strategy design and implementation, every organization needs to craft its own recipe for strategic success. We believe this recipe will be more effective when it adheres to a set of core principles.

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Why are principles are so important? At the Brightline Initiative, a coalition of leading global organizations, we have crafted a set of guiding principles to help leaders close the expensive and unproductive gap between strategy design and delivery. We use the word “principles” for a reason. The Cambridge Dictionary says that a principle can be either a “moral rule” that defines “good behaviour or fair dealing” or a “basic truth” that “explains or controls how something happens or works.” We view these principles as both a moral rule and a basic truth. Practices can change, business models are disrupted, technology evolves, but principles do not change. They are the soul of strategy design and delivery. No matter the conditions of the organization’s environment, no matter how complex and challenging its strategic goals, its principles are permanent. They safely guide leaders and teams toward the right decisions, practices, and processes. They enable organizations to counter the threats to the implementation of strategic initiatives and the realization of strategic goals. And they point the way toward more effective behaviours and attitudes and guide the use of appropriate practices, tools, and techniques aligned with the business’s needs and challenges.

How we identified the 10 principles The process of identifying and crafting the 10 principles started in May 2017, when a group of experts, practitioners, and researchers, supported by the Brightline team, met to discuss the most complex challenges that organizations face regarding strategy design and delivery. Some of those challenges are well known, such as the absence of alignment between senior and middle managers on one hand and those responsible for executing the strategy on the other. Other problems still require further exploration and understanding. How, for example, can people be highly motivated to deliver certain strategic initiatives? And how can organizations deal with failure when they need to deliver results quickly and effectively at all times, with little room for mistakes? Even without full answers to those and other questions, we were able after that first meeting to initiate a process that melded a scientific approach with the experience and knowledge of leading organizations and experts in the strategy discipline. Intensive discussions and group work produced some very rich content. We then analyzed the results using text mining techniques and cluster analysis to identify key areas for further exploration and refinement.

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We analyzed the most recent scientific studies and empirical surveys of strategy design and implementation with the aim of corroborating and complementing the ideas and concepts identified in this process. The outcome of this process was a preliminary set of concepts that evolved into the first version of the principles. We then took the draft principles through several iterations to refine the concepts that underlie the ten Brightline Principles. Our aim was to develop simple, clear, robust, and practical guidance to help leaders succeed at bridging the gap between strategy design and delivery. The members of the Brightline coalition believe that every leader can rely on these ten principles to help them close the gap between strategy design and delivery.

The Brightline Initiative Guiding Principles #1 Acknowledge that strategy delivery is just as important as strategy design.

Strategy delivery doesn’t just happen automatically once it is designed! The importance of active and visible leadership cannot be overstated. You invest substantial resources, creative time, and energy in designing the right strategy. You need to give equal priority and attention to delivering it – before you move on to something else. It’s an essential part of your role to ensure that your organization has the programme delivery capability it needs to implement your strategy.

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#2 Accept that you’re accountable for delivering the strategy you designed.

Do not underestimate entropy! The orchestration required to succeed in today’s fast-changing and complex business environment is enormous. Once you have defined and clearly communicated the strategy, your responsibility shifts to overseeing the progress of implementation so that the strategy delivers results and achieves its goals. You need to know where in your organization change happens and who manages the programmes that drive that change. You are accountable for proactively addressing emerging gaps and challenges that may impact delivery. Without this discipline, rigor, and care, your strategy has little chance of success.

#3 Dedicate and mobilize the right resources.

Inspire and assign the right people to get the job done! Actively balance “running the business” and “changing the business” by selecting and securing the right resources for each – they often have different needs. Recognize that team leadership skills are at a premium, and assign the best leaders with sufficient capacity to tackle head-on the most challenging programmes and those essential for successful strategy implementation.

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#4 Leverage insight on customers and competitors.

Don’t forget to look outside! Continue to monitor customer needs, collect competitor insight, and monitor the market landscape for major risks, unknowns, and dependencies. Advantage in the market flows to those who excel at gaining new insights from an ever-changing business environment and quickly responding with the right decisions and adjustments to both strategy design and delivery.

#5 Be bold, stay focused and keep it as simple as possible.

Encourage smart simplicity! Initiating or rapidly reacting to dramatic changes in the business environment is an increasingly important capability for success. Many of the delivery challenges you will face will be complex and interdependent. In the face of this, the best way to remain nimble is to surround yourself with simplifiers rather than complicators. You need people who can get to the core of an opportunity or threat, understand the drivers, deliver the information, and take the action you need in the way you need it. That way, you minimize bureaucracy, explore ideas, take appropriate risks, prioritize work, ensure accountability, and focus on delivering value through your strategic initiatives.

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#6 Promote team engagement and effective cross-business cooperation.

Beware of the “frozen middle”! Gain genuine buy-in from middle and line managers by engaging and activating them as strategy champions rather than just as managers and supervisors. Don’t just assume your people will “get it” – leadership must firmly establish a shared commitment to strategy-delivery priorities and regularly reinforce it. This isn’t the time or place for subtlety. Govern through transparency to engender trust and enhance cross-business cooperation in delivery.

#7 Demonstrate bias toward decision-making and own the decisions you make.

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regularly throughout delivery. Build a lean and powerful governance structure to reinforce accountability, ownership, and a bias towards action, based on agreed metrics and milestones.

#8 Check ongoing initiatives before committing to new ones.

Follow your decisions through to delivery! Commit to making strategic decisions rapidly. Move quickly to correct course, reprioritize, and remove roadblocks. Accept that you likely won’t have all the information you want, and rely on those you can trust to deliver sufficient reliable input to allow thoughtful decisions. Consider and address risks and interdependencies explicitly—both upfront and regularly throughout delivery. Build a lean and powerful governance structure to reinforce accountability, ownership, and a bias towards action, based on agreed metrics and milestones.

#9 Develop robust plans but allow for missteps – fail fast to learn fast.

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Proper planning and preparation prevent poor performance! The old axiom is as true as it ever was, but in today’s business environment, strategy planning cycles must be more rapid, dynamic, and agile than in the past. Empower programme delivery teams to experiment and learn in an environment where it is safe to fail fast. Discuss challenges openly, and adjust the plan as needed for success. Learn to reward failure, or at least accept it as valuable input.

#10 Celebrate success and recognize those who have done good work.

Inspiring people is part of your job! Yes, you have to drive accountability and focus on delivery, but you also need to motivate those who do the work. Actively shape a winning culture by engaging and exciting the people responsible for delivering strategic change programmes. Celebrate successes and quick wins. Generously and publicly acknowledge those who demonstrate the leadership behaviours and programme delivery capabilities that make strategy succeed, and ask them to share their experiences. ... Organizations rise or fall on their ability to successfully implement winning strategies. At a time when leaders are expected to do nothing less than transform their organizations so that they can survive and prosper in a hyper-connected, fast-changing world, they urgently need to know how to turn ideas into reality. We at the Brightline Initiative are ready, willing, and eager to help leaders and their organizations build their implementation capabilities. We offer these 10 principles as a first step on a long and, we hope, ultimately rewarding journey toward implementation excellence.

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Resources Economist Intelligence Unit (EIU), “Closing the Gap: Designing and Delivering a Strategy that Works”, 2017. Economist Intelligence Unit (EIU), “Why good strategies fail: Lessons from the C-suite” 2013. Project Management Institute (PMI), “PMI’s Pulse of the Profession: Success Rates Rise – Transforming the high cost of low performance”, 9th Global Project Management Survey, 2017.

SUSTAINABILITY INDICATORS TO ASSESS INFRASTRUCTURE PROJECTS SECTOR DISCLOSURE TO INTERLOCK WITH THE GLOBAL REPORTING INITIATIVE

Ricardo Viana Vargas Universidade Federal Fluminense

Mariana de Almeida Costa Georgia Institute of Technology

Ricardo Prata Fernandes Ferrarez Universidade Federal Fluminense

Bernardo Lino de Oliveira Expert Analytics AS

Jeferson Carvalho Alvarenga Universidade Federal Fluminense

Assed Naked Haddad Universidade Federal do Rio de Janeiro

Christine Kowal Chinelli Universidade Federal Fluminense

Published in

Carlos Alberto Pereira Soares Universidade Federal Fluminense

Engineering Journal Banckok – Thailand – 2020

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Sustainability Indicators to Assess Infrastructure Projects

Abstract Infrastructure projects have great potential to impact the sustainability of cities due to typically being large-sized projects and having a high level of intervention. Thus, evaluating the sustainability of these projects through sustainability reports is highly relevant, mainly regarding their impacts on the environment, public health, and the local economy. The Global Reporting Initiative (GRI) is the most widespread and internationally accepted Sustainability report tool. However, the GRI does not have an infrastructure sector disclosure. This research addresses this gap by providing a sustainability assessment instrument for infrastructure projects that interlocks with the Global Reporting Initiative (GRI). An extensive and detailed literature review was conducted, identifying 97 potential indicators to measure the sustainability of infrastructure projects. These indicators were evaluated following a top-down approach, conducting a survey of professionals experienced in the relevant field using Lawshe’s content validity ratio. The results showed that 42 indicators were validated as essential, with 21 of them, not specifically related to infrastructure projects, already covered by the standard disclosure of the GRI. This assessment enabled the proposal of a sector disclosure formed by 21 new indicators related to the environmental, economic, and social dimensions. This study closes a gap in the evaluation of the sustainability of infrastructure projects and contributes to the discussion about sustainability indicators in infrastructure projects.

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1. Introduction The necessity of measuring the sustainability of infrastructure projects has aroused the interest of researchers, as well as governmental and intergovernmental organizations. A significant part of the infrastructures built in several countries demanded large investments [1] and was later classified as unsustainable, because of their harmful effects on the population, fauna, flora, landscape, climate, water, and cultural heritage—this demands evaluation [2]. Many classifications can be adopted for the construction industry; however, in general, it is composed of three subsectors: building, industry, and infrastructure, involving, primarily, construction activities, reconstruction, alteration, repair, and demolition. The products from the “building subsector” are residential and commercial buildings. The products from the “industrial subsector” are factories, refineries, etc. The products from the “infrastructure subsector” are great works of engineering, typically public ones related to transport (roads, bridges, busways, cycleways, footpaths, railways), water (sewage, drainage, water storage, supply), energy (transmission and distribution), communication (transmission and distribution), sanitation, utilities, facilities, and others [3-5]. Despite being a precondition for industrialization and economic development [6,7], impacts caused by infrastructure projects have been cited in several studies, such as new roadways and railway lines—that increase noise pollution [8] and affect the mobility and well-being of communities [9,10] —and dams—that are among the most impactful stressors that affect aquatic ecosystems [11]. Infrastructure projects are typically characterized by large extension, broader and more varied potential impacts [3], and “significant impact on the sustainable construction environment” [12]. These features combined make infrastructure projects have a “great impact on urban and overall project management, mostly because of the large zones of influence” [13]. Sahely et al. [14] stated that massive urban growth had taken place in the previous few decades and that infrastructure projects are crucial to urban sustainability. According to Shen et al. [15], infrastructure projects are vital to the economy of a country but can also cause negative environmental and social impacts. The construction industry is estimated to account for about 40% of global energy use, as well as 20% of water consumption and 40% of global carbon emissions [16]. In the civil construction sector, infrastructure projects account for a considerable portion of this impact. Statistical data show that there is a correlation between these impacts and the deaths of children under five years of age in developing countries, where 15% die from contaminated water, 36% from infectious

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respiratory diseases and 22% from chronic lung diseases caused by pollution from dust particles [3]. Thus, infrastructure projects directly impact not only the environment, but also public health, community well-being, the economies of surrounding cities and regions, and urban sustainability [13], reinforcing the demand for indicators of infrastructure project sustainability [12]. Ugwu et al. [13] claim that infrastructure sustainability indicators have a key role in evaluating infrastructure sustainability and stakeholder education. In this context, many infrastructure sustainability indicators and infrastructure indicator frameworks emerged. These frameworks relate indicators, forming a global index [15-20]. However, there is still plenty of scope for improvement in infrastructure project sustainability indicators, mainly concerning relating infrastructure project sustainability to social and economic dimensions, by aligning project sustainability measurement with the Triple Bottom Line model [5, 19,21,22]. One of the biggest problems in evaluating sustainability lies in the identification of sustainability indicators and the selection of the most important indicators [23]. From extensive and detailed bibliographical research, we identified seven infrastructure sustainability indicator lists proposed by researchers [12-15, 17-20]. In general, these lists were generated from case studies, surveys, and governmental guidelines. Sahely, Kennedy, and Adams [14] state that urban growth has taken place in the last few decades and that infrastructure projects are crucial to urban sustainability. The authors proposed 34 social, economic, environmental, and constructive indicators in a study case conducted in Toronto. Ugwu, Kumaraswamy, Wong, and Ng [13] claim that infrastructure sustainability indicators have a key role in contributing to the reflection on infrastructure sustainability and the education of stakeholders. The authors used an instrument formulated through a lengthy interview process, study case data, and governmental guidelines. Based on 134 Hong Kong respondents, the result is a model containing 55 indicators. Ugwu and Haupt [12] highlight the international and urgent demand for measuring infrastructure projects’ sustainability. The authors applied Ugwu, Kumaraswamy, Wong, and Ng’s [8] indicators in South Africa with little adaptation and including few indicators, resulting in 61 infrastructure sustainability indicators. Koo, Ariaratnam, and Kavazanjian [24] propose an underground infrastructure sustainability assessment model that emphasizes the social, economic, and en-

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vironmental dimensions. The authors used the AHP method and an instrument consisting of 47 indicators to enable more sustainable decision making in infrastructure projects. Shen, Wu, and Zhang [15] reinforce the suggestion that infrastructure projects are vital to the economy of a country but also cause adverse environmental and social impacts. The authors, based on a bibliographical review, propose an instrument consisting of 30 indicators applied in a case study in the Chinese construction industry. Ariaratnam, Piratla, Cohen, and Olson [18] investigate the social, environmental, and economic impacts of underground infrastructure projects, comparing four underground construction techniques and using 13 sustainability indicators to measure these impacts. Boz and El-adaway [19] affirm that there is little agreement among stakeholders on what sustainability is and how to measure it. They use a methodology based on expert knowledge and propose a model of sustainability indicators for infrastructure projects containing 22 indicators combining “work” and “nature”. Other researchers have published research about civil construction sustainability indicators in the last few years [15, 22-25]. Some researchers focus on specific infrastructure sustainability indicators, including energy and electricity infrastructure projects [26, 27], transport infrastructure projects [28], rail trains [29], and water supply systems [30]. We also find four infrastructure sustainability indicators proposed by organizations and governments: ASPIRE [31], CEEQUAL [32], ENVISION [33], and GRI Real Estate Sector Disclosure [34]. ASPIRE (A Sustainability, Poverty, and Infrastructure Routine for Evaluation) is a model designed by Engineers Against Poverty in partnership with the multinational Arup Group Limited and is structured around four main themes: environment, society, economy, and institutions. Each theme is subdivided into subthemes, which in practice are sustainability indicators for each theme, totaling 97 indicators. CEEQUAL (Civil Engineering Environmental Quality Assessment and Award Scheme) is a model that proposes indicators related to twelve areas: project management, land use, landscape issues, ecology and biodiversity, historic environment, water resources and the water environment, energy and carbon, material use, waste management, transport effects on neighbors, and relations with the local community and other stakeholders. The model proposes 241 indicators, where each indicator contributes scores that are linked to summarize the project

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practices and sustainability consciousness level. ENVISION was developed by the Institute for Sustainable Infrastructure and engages five categories: quality of life, leadership, resource allocation, natural world and climate, and risk. Each category contains themes, and each indicator addresses issues related to project governance and the resilience of infrastructure projects, aiming to contribute to more resilient and long-term sustainable projects. ENVISION contains 182 indicators. The GRI sector disclosure for construction and real estate encompasses infrastructure projects, although the disclosure must be used with standard disclosures. The disclosure is aligned with the Triple Bottom Line framework, following the GRI standard. It consists of four areas (economic, environmental, social, and construction and real estate) and 47 indicators By analyzing the lists of indicators and models, the predominance of indicators focused on the environmental aspect is evident. This characteristic has been observed in most of the listings produced by researchers, and the list compiled by Ugwu and Haupt [12] contains the highest number of indicators related to social and economic aspects: eight indicators out of 61. Regarding the models, we noted that ASPIRE and the GRI Real State Sector Disclosure align more clearly with the Triple Bottom Line model, while the CEEQUAL and ENVISION again tend to emphasize environmental indicators. Another observation is the difficulty of focusing on only a few indicators due to the large nature of infrastructure projects. The CEEQUAL model lists 241 indicators, and Ariaratnam et al. [18] and Boz and El-adaway [19] presented the lowest number of indicators, 13 and 22, respectively. Also, there is an increasing demand for corporate and governmental accountability. Corporations and governments are compelled by society to be transparent about the sustainability of their industrial and managerial activities [35-39]. It is precisely at the point of confluence of the growing need for “accountability” and the holistic concept of sustainability that sustainability reporting models have emerged in the last few decades [40-42]. Sustainability reporting models provide a framework of indicators that facilitates corporate and governmental accountability reports for stakeholders regarding the sustainability of projects [39-41]. Their use has focused mainly on informing the decision-making process of external stakeholders, legitimation, reputational enhancement, and marketing [45]. Studies aiming at the development and improvement of sustainability reports for the private sector have received more attention from researchers [46]. Among

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these sustainability reporting models, the most widespread and widely accepted internationally is the Global Reporting Initiative (GRI), which is based on the Triple Bottom Line framework and thus encompasses the social, economic, and environmental dimensions of sustainability [47-48]. The GRI provides a standard disclosure format and some sectoral disclosures with indicators for specific sectors [48]. The development of indicators for specific sectors is arousing interest among many researchers [49-52]. The GRI sustainability reporting model consists of two parts: reporting principles and standard disclosures. The reporting principles are accuracy, timeliness, clarity, and reliability. The standard disclosures are divided into general standard disclosures—organizational identity, strategy, stakeholders, and governance—and specific standard disclosures, which include 91 sustainability indicators covering the social, economic, and environmental dimensions [53]. In addition to providing standard disclosures, the GRI contains sustainability reporting models with indicators related to specific sectors, such as the media, airport operation, electric utilities, event organization, financial services, food processing, mining and metals, non-governmental organizations, and oil and gas. The GRI also has a sector disclosure for construction and real estate, the indicators of which are geared towards the construction of buildings. This disclosure sector consists of 47 indicators, of which some are relevant to infrastructure projects, not including specific project indicators of such nature, such as those relating to the water pollution control plan, noise pollution, toxic waste, access to potable water and sanitation services, drainage systems, preservation of historical and archaeological sites, disaster risks, climate change risks, flood risk during site selection, and soil conservation and restoration. Not having a specific sector for infrastructure projects is surprising, considering the potential impacts of these projects on sustainability, particularly on public health [13, 15], worker health [15], environmental health [19], local economies [18], and both public [12-13, 18] and worker safety [24]. Siew et al. [5] conducted a wide-ranging global review of existing models to assess infrastructure project sustainability, concluding that “There is a need to bridge the current gap and look at avenues by which building/infrastructure SRTs can interlock with GRI”. Therefore, there is a demand for an instrument to measure the sustainability of infrastructure projects that intentionally involves and relates environmental, social, and economic aspects, following the GRI sectorial content standard. Furthermore, international bodies, including the United Nations (UN) through the Commission on Sustainable Development, started a research agenda cen-

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tered on sustainability indicators to facilitate a more measurable discussion of sustainability [43]. This intentional stimulus has led to an accelerated growth of sustainability indicators and models that reconcile indicators, relating them to form a global index, with many of these models being sector-specific [14]. One sector in which the number of indicators and modeling of sustainability indicators is increasing is the infrastructure sector [5]. However, the substantial increase in the number of infrastructure sustainability indicators introduces the risk that it might become too extensive. Also, there are no reports that identify the indicators that must be included in the GRI to improve aspects related to the environmental, social, and economic sustainability dimensions of infrastructure projects. This study addresses this gap and contributes to the literature handling indicators for the evaluation of infrastructure projects by identifying the indicators considered most important by researchers who publish on the topic, from extensive and detailed bibliographic research. It distinguishes what can be regarded as essential, according to the professionals who work in this field. Considering the importance of the GRI project sustainability reports in international sustainability assessment, this paper also contributes to the evaluation of infrastructure project sustainability, proposing a GRI infrastructure sector disclosure. In construction, most studies are geared towards reducing the impacts of building works, so this article also contributes to the sustainable planning and the evaluation of the impacts of a less explored sector—the infrastructure sector.

Materials and Methods

2.1. General Approach Although the GRI is a structure mainly used for organizational reports, some sector contents include specific projects, such as, for example, Construction and Real Estate Sector Disclosure, in this article, we propose specific sector disclosure for infrastructure projects to improve the sustainability assessment of these projects. The strategy adopted was to use only indicators of articles with a specific focus on infrastructure projects. We consider that the fact that these researchers work on the theme establishes a first important filter on the applicability and importance of the indicator and, therefore, from the experience of these authors, we have a set of indicators more relevant, representative and specifically focused on infrastructure projects as a whole.

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In this sense, it has three secondary purposes: a) to identify the evaluation indicators of the sustainability of infrastructure projects that are considered most important by researchers who publish on the topic; b) to distinguish which of these indicators can be regarded as essential due to the assessment of professionals working in this field; c) to propose a sustainability assessment instrument for infrastructure projects that interlocks with the Global Reporting Initiative (GRI). The four main steps of the study were bibliographic research, identification of infrastructure sustainability indicators, a survey of expert opinions, and data analysis.

2.2. Bibliographic Research A bibliographic search was conducted using the keywords “infrastructure,” “sustainability,” “indicators,” and “assessment.” We adopted the recommendations of Webster and Watson [54] and the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). The keyword search returned 924 articles. The articles were screened by a brief study of their titles and abstracts to eliminate non-relevant texts. Publications in journals without peer review systems, those whose full text was not available, and those in languages other than English, Portuguese, or Spanish were also excluded. As a result, 712 titles were excluded. The remaining 212 articles were screened to exclude models an d indicators that were not generally applicable to infrastructure projects. Many studies have been published on civil construction sustainability indicators [4, 22-23]. Some researchers have focused on very specific types of infrastructure projects, leading to very specific sustainability indicators for sectors such as energy and electricity [27], transport [28], railway [29], and water supply [30]. These studies were excluded considering that the resulting sets of indicators were too specific. Besides, some models of sustainability assessment such as LEED, BREEAM, Green Star, CASBEE, HK-BEAM, NatHERS, BASIX, NABERS, Energy STAR, MFA, PaLATE [5], Envest, UrbanSim, and GreenLITES [19] focus on civil construction as a whole. Conversely, some models focus on specific infrastructure projects, such as Green Roads, which is specific to road infrastructure projects [19]; these models were excluded because they are too focused on specific types of infrastructure projects and are not relevant to infrastructure projects as a whole. As a result, 201 articles were excluded, and the remaining 11 texts—7 articles and 4 models—were analyzed in detail, becoming the basis for the data collection instrument. Figure 1 summarizes the literature search conducted using the PRISMA flowchart.

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2.3. Infrastructure Sustainability Indicators The indicators found in the papers and models selected through bibliographic research were listed, analyzed, interpreted, and cross-checked. This process resulted in 299 indicators, which were then compared and indexed. The indicators that were not mentioned in more than one source were discarded. This procedure was conducted following the methodological procedures employed in a similar study, in which the same mechanism was used in the face of a comprehensive theoretical framework [15, 54]. As a result, 97 infrastructure sustainability indicators were obtained, which included environmental, economic, and social dimensions (Appendix A). The strategy of focusing on the indicators with broader bibliographical support concentrates the scope of research to increase the concentration of the respondents and, consequently, the quality of answers [55].

Figure1 – A literature search using the PRISMA flowchart.

2.4. Survey of Expert Opinions An expert opinion survey involves the determination of the profile of each respondent, data collection, and content validation. Three main procedures are used to establish sets of indicators for specific sectors: an expert survey (top-down), general stakeholders (bottom-up), and research that mixes the two approaches [22]. Most scientific research and civil engineering sustainability models use the top-down approach, which is based on expert recommendations [12, 19, 26]. Most researches adopt the top-down approach due to the complexity of the concept of sustainability and the holistic paradigm simultaneously involving social,

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economic, and environmental dimensions, which adds to the complex definition of sustainability indicators [44]. As a consequence of this scenario, researchers have appealed to professionals with expertise in infrastructure projects who also demonstrate knowledge of sustainability concepts and practices [19, 26]. Although this practice significantly reduces the sample of potential respondents, it yields more reliable results. Thus, this study preferred the methodological option of conducting an online survey with a sample of experts—the top-down approach—with the following profile: active professionals in civil engineering with more than ten years of experience in infrastructure project management and expertise in sustainability. Respondents received an online version of the research tool presented in Appendix A, containing the 97 indicators resulting from the processes reported in sections 2.2 and 2.3 of this study. The platform used to generate the online surveys was Survey Monkey, whereby the sequence of factors for each respondent was randomized to help prevent bias. The indicators were not organized around the social, economic, and environmental dimensions in the data collection instrument, again to avoid bias. Some experts were contacted through a personal network of relationships of one of the authors due to his work at the United Nations Office for Project Services (UNOPS). The other experts were contacted through e-mail addresses obtained from 45 publications on infrastructure sustainability and civil engineering sustainability. As for the process of selecting the 45 publications used for the expert panel, from the 212 full-text articles assessed for eligibility (Figure1), we used selected articles with original contents, investigation methods sufficiently described, results that support the study, and focus on infrastructure sustainability indicators, even if they approached only a specific sector of infrastructure. The first part of the data collected was demographic, and the experts were invited to identify themselves (name, organization name, role, professional qualification, nationality, gender, academic qualification, organization type, length of experience, and technical-scientific knowledge on sustainability). All respondents stated that they possessed ten or more years of experience in infrastructure project management. The experts were also invited to share their knowledge regarding infrastructure project sustainability, including indicating their understanding of the models used in the present study or other sustainability models. The results are shown in Figure 3. The experts were further invited to evaluate the relevance of each indicator to the sustainability of infrastructure projects.

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The data collection ran for 12 weeks, and 25 respondents completed the survey. Thus, the sample was non-probabilistic and intentional, with a focus on experts who met the profile established in section 2.4 on the determination of the profiles of the respondents. To establish the essential infrastructure sustainability indicators based on the data collected, the content validation process proposed by Lawshe [56] was used, which considers the expert evaluation of indicators as “essential,” “useful, but not essential,” or “not useful” to determine which indicators should compose the research instrument used to measure a given universe, according to the calculated content validity ratio (CVR). Linked to the CVR, Lawshe proposed a minimum number of “essential” answers from experts based on the number of respondents for each item to be validated or otherwise in the final instrument, called the Critical N. The Critical N reformulated by Ayre and Scally [57] was used in the present study. The indicators were validated in view of the number of “essential” answers of the specialists. The reliability of the data collection instrument and the respondents was estimated using Cronbach’s alpha [58], which relates item and respondent variance.

3. Results 3.1. Selected Indicators Ninety-seven infrastructure sustainability indicators were selected according to the bibliographical research, as shown in Appendix A. Of the 97 selected indicators, 63 are focused on environmental dimensions, ten on economic dimensions, and 24 on social dimensions.

3.2. Survey Results Initially, we calculated Cronbach’s alpha of the dataset to be 0.969, which indicates that the data are highly reliable and confirms the reliability of the research data. The next step was to use the demographic data from the first section of the questionnaire to obtain the profile of the respondents. Most respondents work in engineering (60% of respondents), followed by those who work in management (40%). Figure 2 presents the data related to the respondents’ knowledge about infrastructure sustainability models and sustainability tools. The experts evaluated each indicator according to its relevance in measuring the sustainability of infrastructure projects (Appendix A). As determined using the Critical CVR proposed

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by Ayre and Scally [57], the minimum number of “essential” evaluations for each indicator is 18, considering that 25 specialists contributed to the research.

Figure 2. Self-reported knowledge of the expert respondents of infrastructure sustainability models and tools

Table 1 shows the indicators which were considered essential by, at least, 18 specialists. To improve the measurement of the economic aspect, the economic indicators that came closest to the limit of the critical CVR were included: “economic benefits,” “durability of structures,” “cost of any relocation of people,” and “ecosystem rehabilitation cost.” The experts evaluated each indicator according to its relevance in measuring the sustainability of infrastructure projects (Appendix A). As determined using the Critical CVR proposed by Ayre and Scally [57], the minimum number of “essential” evaluations for each indicator is 18, considering that 25 specialists contributed to the research. Figure 3 summarizes the evaluation of the essentiality of the indicators greater than 80%, considering the respondents’ professional area.

4. GRI Sector Disclosure Proposed for Infrastructure Projects The objective of this research was to develop a sustainability assessment instrument for infrastructure projects that interlocks with the Global Initiative Reporting (GRI). As well as specific contents for other sectors that already exist—airport operators, electric utilities, oil and gas, event organizer and others—this study proposes infrastructure project-specific content. Considering that all specific contents must be utilized jointly with the GRI standard content, we compared the indicators resulting from this study with those used by the GRI at standard

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disclosures. By doing so, it was possible to note that some are already considered in the standard disclosures (Table 2). Table 3 presents the specific infrastructure indicators that are not covered by the GRI standard disclosures, which together form the infrastructure sector disclosure proposed in the present report to interlock with the GRI. The indicators were listed following the standard used by GRI for its contents for specific sectors: the prefix “EC” is for economic indicators, the prefix “EN” is for environmental indicators, and the prefix “SO” is for social indicators. The economic indicators were placed first, followed by the environmental and social indicators, again according to the GRI standard. Figure 3. Evaluation on the essentiality of the indicators

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Indicator Focus 1. Aquatic ecosystem preservation Biodiversity preservation Table 1.2.Infrastructure sustainability indicators which were considered essential by, at 3. Preservation of historical and archaeological sites least, 18 specialists. Environmental preservation 4. Protected area preservation 5. Soil conservation 6. Water preservation Dimension Indicator Focus Air pollution 1.7. Aquatic ecosystem preservation CO2 emissions 2.8. Biodiversity preservation 3.9. Preservation historicalcontrol and archaeological sites Environmentofpollution plan Environmental preservation 4.10. Protected area preservation Greenhouse gas emissions 5.11. Soil conservation Long-term ground/soil contamination 6.12. Water preservation Long-term water pollution Pollution management and control 7.13. Air pollution Noise pollution 8.14. CO 2 emissions Toxic waste management pollution Waste disposal methodcontrol plan Environmental 9.15. Environment 10. gas control emissions 16. Greenhouse Water pollution plan 11. ground/soil contamination 17. Long-term Potable water consumption 12. water pollution 18. Long-term Water reuse and recycling Pollution management and control 13. pollution management 19. Noise Environmental 14. Toxic waste management 20. Establishment of a sustainability management system Environmental management methodenvironment Environmental 15. 21. Waste Impactdisposal on the natural 16. pollution plan 22. Water Climate changecontrol risks and resilience 17. 23. Potable Disasterwater risks consumption 18. and recycling 24. Water Flood reuse risk during site selection Environmental risk management 19. management 25. Environmental Risk of landslides, erosion, and sedimentation 20. of a sustainability management system Environmental management 26. Establishment Risk management 21. on systems the natural environment 27. Impact Drainage 22. change risks and resilience 28. Climate Soil restoration Sustainable Practices 23. risksmaterial source 29. Disaster Sustainable 24. risk during site selection Environmental risk management 30. Flood Ecosystem rehabilitation cost Environmental costs 25. of of landslides, erosion, and sedimentation 31. Risk Costs any relocation of people Economic 26. management 32. Risk Durability of structures Economic benefits 27. 33. Drainage Economicsystems benefits 28. Soil restoration Sustainable Practices 34. Access to potable water and sanitation services 29. Sustainable material source 35. Accidents, injuries, fatalities, etc. 30. rehabilitation cost 36. Ecosystem Public health Environmental costs Public and worker health and safety 31. Costs of any relocation of people 37. Public safety Economic 32. Durability of structures 38. Worker health Social Economic benefits 33. Economic benefits 39. Worker safety 34. Access to potable water and sanitation services 40. Conflict sensitivity from locals Social Responsibility 35. Accidents, injuries, fatalities, etc. 41. Social and cultural impact due to the project 36. Public health Public and worker health and safety 42. Project governance and strategic management Governance 37. Public safety 38. Worker health Social 39. Worker safety 40. Conflict sensitivity from locals Social Responsibility 41. Social and cultural impact due to the project 42. Project governance and strategic management Governance

Table 2. Indicators already considered in GRI-specific standard disclosures. Infrastructure indicators

Indicators of specific standard disclosures Extent of mitigation of environmental impacts of products and services.

Impact on the natural environment Infrastructure indicators

Environmental management Impact on the natural environment

Significant environmental impacts of transporting products and other goods and materials for the of the organization and transporting Indicators ofoperation specific standard disclosures workforce members. The extent of impactofmitigation of environmental impacts ofand products and Extent of mitigation environmental impacts of products services. services Significant environmental impacts of transporting products and other Total environmental protection expenditures and investment type goods and materials for the operation of the organization andby transporting workforce members.

Environmental management

Indicator Identification according to GRI G4-EN27 Indicator G4-EN30 Identification according to GRI G4-EN27 G4-EN27

G4-EN31 G4-EN30

The extent of impact mitigation of environmental impacts of products and services

G4-EN27

Total environmental protection expenditures and investment by type

G4-EN31

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Toxic waste management

Weight of transported, imported, exported, or treated waste deemed hazardous under the terms of the Basel Convention 2 Annex I, II, III, and VIII and the percentage of transported waste shipped internationally

G4-EN25

Water reuse and recycling

Percentage and total volume of water recycled and reused

G4-EN10

Potable water consumption

Total water withdrawal by source

G4-EN8

Air pollution

NOx, SOx, and other significant air emissions

G4-EN21

CO2 emissions

NOx, SOx, and other significant air emissions

G4-EN21

Climate change risks and resilience

Financial implications and other risks and opportunities for the activities of an organization due to climate change

G4-EC2 G4-EN12

Biodiversity preservation

Description of significant impacts of activities, products, and services on biodiversity in protected areas and areas of high biodiversity value outside protected areas Total number of IUCN red list species and national conservation list species with habitats in areas affected by operations, by the level of extinction risk

G4-EN14

Long-term water pollution

Identity, size, protected status, and value of water bodies and related habitats significantly affected by the discharge of water and runoff

G4-EN26

Waste disposal method

Total weight of waste by type and disposal method

Greenhouse gas emissions

G4-EN23

Direct Greenhouse Gas (GHG) emissions (scope 1)

G4-EN15

Energy indirect GHG emissions (scope 2)

G4-EN16

Other indirect GHG emissions (scope 3)

G4-EN17

GHG emission intensity

G4-EN18

Protected area preservation

Operational sites owned, leased, managed in, or adjacent to protected areas and areas of high biodiversity value outside protected areas

G4-EN11

Water preservation

Water sources significantly affected by the withdrawal of water

G4-EN9

Economic benefits

Direct economic value generated and distributed

G4-EN9

(Customer Health and Safety) Percentage of significant product and service categories for which health and safety impacts are assessed for improvement

G4-PR1

Public safety

Total number of incidents of non-compliance with regulations and voluntary codes concerning the health and safety impacts of products and services during their life cycles, by type of outcome

G4-PR2

(Customer Health and Safety) Percentage of significant product and service categories for which health and safety impacts are assessed for improvement

G4-PR1

Total number of incidents of non-compliance with regulations and voluntary codes concerning the health and safety impacts of products and services during their life cycles, by type of outcomes

G4-PR2

Percentage of the total workforce represented in formal joint management–worker health and safety committees that help monitor and provide advice regarding occupational health and safety programs

G4-LA5

Health and safety topics covered in formal agreements with trade unions

G4-LA8

Percentage of the total workforce represented in formal joint management–worker health and safety committees that help monitor and provide advice regarding occupational health and safety programs

G4-LA5

Health and safety topics covered in formal agreements with trade unions

G4-LA8

Social and cultural impacts of the project

Operations with significant actual and potential negative impacts on local communities

G4-SO2

Accidents, injuries, fatalities, etc.

Types and rates of injury, occupational diseases, lost days, absenteeism, and the total number of work-related fatalities, by region and by gender

G4-LA6

Public health

Worker safety

Worker health

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Aspects

Indicators EC-01 Cost of any relocation of people

Economic

EC-02 Durability of structures EC-03 Ecosystem rehabilitation cost EN-01 Aquatic ecosystem preservation EN-02 Disaster risks EN-03 Drainage systems EN-04 Environnent pollution control plan EN-05 Establish a sustainability management system EN-06 Flood risk during site selection EN-07 Risk of landslides, erosion, and sedimentation

Environmental

EN-08 Long-term ground/soil contamination EN-09 Noise pollution EN-10 Preservation of historical and archaeological sites EN-11 Risk management EN-12 Soil conservation EN-13 Soil restoration EN-14 Sustainable material source

EN-15 Water pollution control plan

Social

SO-01 Access to potable water and sanitation services SO-02 Conflict sensitivity from locals SO-03 Project governance and strategic management

Report Guidelines Report each action of relocation. Inform the cost of each action and the total cost. Present the percentage of the cost of each action in relation to the total cost of relocation and the total cost of relocation in relation to the total cost of the project. Report, during the design and specification phases, if the team considered the durability and maintenance requirements of structures and components. Report which aspects for durability were implemented in construction. Report each action of rehabilitation. Present the cost of each action and the total cost. Present the percentage of the cost of each action in relation to the total cost of rehabilitation and the total cost of rehabilitation in relation to the total cost of the project. Report the total actions of preservation and protection of rivers, lakes, vernal pools, wetlands, shorelines, or waterbodies performed, as well as to characterize them, their scope, their frequency, and their importance for the reduction of impacts. Report if, during the planning and design phases, the team considered the possibilities of flood risk, quakes, and potential natural risks. Report which specific actions have been taken to prevent and mitigate risks. Report if sustainable drainage systems have been incorporated in the scheme where appropriate. Present which drainage system is used, the impacts caused, and the actions of prevention/mitigation. Report the standards, methodologies, and assumptions used, including if the information is calculated, estimated, modeled, or originated from the direct measurements, and also the approach to do so. Specify actions to prevent and mitigate air, land, and water pollutions during construction. Report how the sustainability management system considers the scope, scale, and complexity of the project. Introduce standards, methodologies, and assumptions used, including if the information is calculated, estimated, modeled, or originated from the direct measurements, and also the approach to do so. Report if the team considered the characteristics, the environmental issues, and flood risk during the selection of the site location. Report which specific actions have been taken to prevent and mitigate risks. Report if the team considered the risks of landslides, erosion, and sedimentation on the planning and design stages. Present which specific actions have been taken to manage erosion and prevent landslides. Report how the measures and equipment were incorporated into the project, which will allow the long-term monitoring of the project's impact on the soil. Notify the sources of contamination factors, quantifying the types of contamination that occurred, the extent of the impacts and the measurements to be taken to reduce these impacts. Report if, during the planning and design phases, the team considered monitoring and mitigation of ambient noise to reduce noise to accepted standard target levels during and after construction. Notify the sources of the factors of emissions, quantifying the extent of the impacts and the measurements to be taken to reduce these impacts. Report how the historical and archaeological sites are identified and develop a sensitive design and approach to conservation and protection. Present the total number of preservation actions and characterize them, their scope, their frequency, and their importance to the reduction of possible impacts. Report if, during the planning and the design phases, the team developed a documented plan to identify and mitigate risks concerning the many areas of the project. Report which specific actions have been taken to prevent and mitigate risks. Report if, during the planning and the design phases, the team considered actions for the conservation of topsoil and subsoil and conservation of on-site mineral resources. Report which specific actions have been implemented in the construction stage. Report in square kilometers the extension of the land remediated for the existing or intended land use, according to applicable legal designations. Report which specific actions have been implemented for the soil restoration. Report if, during the planning and the design phases, the team considered responsible sourcing of sustainable materials. Report which specific actions have been taken for the use of sustainable materials. Report if, during the planning and the design phases, the team created a plan to control the impacts of the project on the water environment during construction. Notify about standards, methodologies, and assumptions used, including if the information is calculated, estimated, modeled, or originated from the direct measurements and the approach used to do so. Report which specific actions have been taken to control the impacts of the project on the water in the construction stage. Report if, during the planning and the design phases, the team considered the availability of potable water and sanitation to workers. Report the availability of potable water and sanitation to workers in the construction stage. Report if, during the planning and design phases, the team inquired about local customs. Report if the team monitored and managed possible conflicts in the construction stage. Report if appropriately skilled personnel were commissioned to undertake the implementation of the management plan, monitoring of the establishment, and review of the objectives and management prescriptions.

Table 3. Proposed infrastructure sector disclosure to interlock with the Global Reporting Initiative

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Figure 4 shows the number of indicators grouped by focus and Fig. 5 shows the number of indicators of the sustainability assessment tools grouped by the sustainability dimension. Figure 4. Number of indicators grouped by focus

Figure 5. Number of indicators of the sustainability assessment tools grouped by sustainability dimension

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5. Discussion In general, environmental indicators are, to a greater or lesser extent, related to environmental health. More specifically, environmental indicators mainly reflect the concern with reducing pollution on the one hand and maximizing preservation on the other, essential and complementary actions in terms of the environmental health of a city or region. Of the nine social indicators, six are directly related to public or worker health, demonstrating that an important aspect of social sustainability is related to how infrastructure projects impact the health of the people most directly affected by this type of project—people living in the neighborhood and the workers. Of the other social indicators, two are related to the project’s social responsibility to the affected community, and one represents the project’s governance issues, emphasizing the importance of project leadership for sustainability. Two economic indicators involve environmental costs, and two involve economic benefits, of which one targets the economic benefits to the surrounding community arising from the project, and one measures the costs given the life cycle, durability, and maintenance of the project. The results show that the environmental aspect significantly influenced the judgment of the experts regarding the sustainability of infrastructure projects. The resulting indicators also reflected the tendency to utilize a higher proportion of environmental sustainability indicators, which is in agreement with previous studies [5, 14, 19, 22, 59, 60]. When we consider the indicators evaluated as essential (Table 1), analysis of Figure 3 shows that regarding the professional area, the assessment of managers concerning the essentiality was mainly between 70 and 90% of the indicators, and those of the engineers were between 70 and 100%. Only less than 5% of engineers assessed that less than 50% of the indicators are essential. When we consider the essentiality of the indicators above 80%, the analysis of Figure 3 shows that for the environmental dimension, the evaluation of engineers and managers was very similar, between 60 and 70%. Regarding the economic and social dimensions, the engineers did not attribute this essentiality to the economic indicators, giving greater emphasis to the social aspects. One possible explanation for this result concerns the difference between professional activities. The engineer is responsible for the operationalization of the construction activities, which makes the environmental and social impacts more visible than the economic ones. Besides, it is common to find as a characteristic of the engineer’s profile, that of being able to understand the impact of engineering solutions in the socio-environmental context. In the project management environment, the factors of scope, time, and cost are conflicting and interdependent.

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They must be balanced by the project manager for the project success, which is why they are called Triple Constraint. Thus, the cost factor is more present in the activities of managers than in that of engineers. These analyses show that the economic indicators are still a controversial aspect and are little explored in the field of sustainability indicators in infrastructure projects, which is confirmed by Boz & El-adaway [19]. Also, environmental indicators tend to be considered more critical than others. Analysis of the indicators considered essential for engineers and managers reveals a significant presence of indicators linked to risk management: flood risk during site selection; risk management; climate change risks and resilience, risk of landslides, erosion, and sedimentation; and disaster risks, which is in agreement with the literature that states the high level of risk involving infrastructure projects due to their extension and impact [3, 12,13, 61]. Figure 4 shows that the 42 indicators can be grouped according to their focus on ten themes, of which Pollution management and control (29%), Environmental preservation (14%), and Public and worker health and safety (14 %) contains more than 50% of the indicators. In the discussions on sustainability, in general, these three themes have been widely discussed. Comparing the essential indicators with other related instruments, it is possible to notice a considerable reduction in the number of indicators regarding ENVISION, CEEQUAL, ASPIRE, Ugwu et al. [13], and Ugwu and Haupt [9]. At the same time, the resulting indicators involve the environmental, economic and social aspects by following the holistic model of the Triple Bottom Line, with a better balance than ENSIVION, CEEQUAL, Ugwu, et al. [13], Ugwu and Haupt [10], Shen et al. [15], Sahely et al. [14], Koo et al. [24], Boz & El-Adaway [19] and Ariaratnam et al. [18]. Considering the number of indicators for the environmental, economic, and social aspects, the most balanced instrument in this scenario is ASPIRE, as shown in Figure 5. When comparing the essential indicators with the sectoral contents of GRI “Construction and Real Estate”, it is possible to notice that specific indicators of infrastructure projects are missing in this sectorial content, such as noise pollution, toxic waste, preservation of historical and archaeological sites, disaster risks, climate change risks, flood risk during site selection, likelihood of landslides, erosion and sedimentation, access to potable water and sanitation services, drainage systems, life-cycle cost, and durability of structures. This result is in agreement with the research of Liu et al. [62] that compared instruments intended to measure sustainability in construction projects in general and

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specific instruments for infrastructure projects. The authors conclude that particular aspects are covered only by tools of measurement in infrastructure projects, such as durability, benefits, landscape, humanities, culture, and creativity. Considering that out of the 42 indicators that are considered essential 21 are already considered by the GRI, the data in Figure 6 show that most of the additional indicators added in GRI by the infrastructure sector disclosure are related to pollution management and control (8) and public and worker health and safety (5), representing 62% of the total additional indicators.

6. Conclusion Due to their scale and nature, infrastructure projects can have significant environmental, social, and economic impacts. Aimed at evaluating the sustainability of these projects, several researchers have related indicators considered appropriate to this kind of analysis. However, the substantial increase in the number of infrastructure sustainability indicators introduces the risk that it might become too extensive. A significant problem related to the evaluation process through indicators is to identify sustainability indicators and to select an indicator set. At the same time, there are specific aspects of infrastructure projects that are not covered by the indicators of sustainability-reporting instruments for construction in general. Also, there are no reports that identify the indicators that must be included in the GRI to improve aspects related to the environmental, social, and economic health of infrastructure projects. This study addresses these gaps.

Fig.ure6. Amount added to the GRI indicators by the infrastructure sector disclosure for each focus of approach

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At the same time, there are specific aspects of infrastructure projects that are not covered by the indicators of sustainability-reporting instruments for construction in general. Also, there are no reports that identify the indicators that must be included in the GRI to improve aspects related to the environmental, social, and economic health of infrastructure projects. This study addresses these gaps. As for the goal of identifying the indicators of evaluating the sustainability of infrastructure projects considered the most important by researchers who publish on the topic, the strategy of focusing on the indicators with broader bibliographical support that concentrates the scope of research to increase the concentration of the respondents and consequently the quality of answers was adopted. After extensive analysis to eliminate redundancies, the result was a relation containing 97 indicators considered by researchers as suitable for the evaluation of infrastructure sustainability. This summary of indicators is an important contribution to the literature on the subject. The results show that the environmental aspect significantly influenced the judgment of the experts regarding the sustainability of infrastructure projects, and, in general, environmental indicators are, to a greater or lesser extent, related to environmental health. Regarding the goal of identifying indicators that can be considered essential, following an experts’ opinion survey, we found that 42 indicators may be considered essential to assess the sustainability of the infrastructure projects. The possibility of reducing the number of indicators to be evaluated, considering only the essential ones, is an important contribution for professionals in the area. As for the objective to propose a specific set of indicators that enable the inclusion in the GRI project sustainability reports of a specific sector to assess the sustainability of the infrastructure projects, using the relation of the indicators considered essential as its basis, we found the need to add 21 indicators. Considering the importance of the GRI project sustainability reports in international sustainability assessment, this paper contributes to the evaluation of infrastructure project sustainability, proposing a GRI infrastructure sector disclosure. The results show that of the 21 indicators proposed for the infrastructure sector disclosure, eight are related to pollution management and control, and five are related to public and worker health and safety. The results also show that economic indicators are still a controversial aspect and are little explored in the field of sustainability indicators in infrastructure projects, agreeing with other researchers’ work on the theme. The comparison of the indicators considered essential to the other related in-

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struments in this study shows a significant reduction in the number of indicators, involving environmental, economic, and social aspects by following the holistic model of the Triple Bottom Line. This study has a typical research limitation based on the literature review. Although extensive and detailed bibliographic research was conducted, the possibility remains that something important may not have been studied. Also, It is important to stress that by focusing on fewer indicators, it is possible that indicators that are relevant to specific realities, depending on the location and characteristics of the infrastructure project, may not be considered. To improve the results of this research, we have two suggestions. The first is to conduct a survey with specialists in infrastructure projects to identify other economic and social indicators based on the experience of these professionals, since in the literature on the subject, there are few references to this type of indicator. The second is to use the set of identified indicators in real cases of infrastructure projects to assess the needs for improvement concerning their effectiveness for the dissemination of sustainability aspects. However, we recognize that this will be a time-consuming process, considering the deadlines usually demanded by this type of project. We hope that future developments of this research will encourage other researchers to identify and quantify the impacts of infrastructure projects.

Acknowledgments The authors would like to thank Universidade Federal Fluminense, Brazil, for funding the research reported in this paper. The authors would like to thank all the experts who answered the survey. The authors also express their gratitude to the editor and anonymous reviewers for comments and suggestions.

Conflicts of Interest The authors declare no conflict of interest.

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preferences,” Sustainability Science, vol. 14, no. 30, pp. 565–578, 2019. [62] T. Y. Liu, P. H. Chen, and N. N. Chou, “Comparison of Assessment Systems for Green Building and Green Civil Infrastructure,” Sustainability, vol. 11, no. 7, pp. 1-22, 2019. Appendix A. Data collection instrument containing Infrastructure sustainability indicators resulting from bibliographic research. Dimension Environment

Indicator

Air pollution

2. 3. 4.

CO2 emissions Greenhouse gas emissions Indirect emissions

Indoor air quality

Aquatic ecosystem preservation Impact of the assessment on water under competent legislation Long-term water pollution

7. 8.

9. Potable water consumption 10. Water pollution control plan 11. Water preservation 12. Water reuse and recycling 13. Land acquisition 14. Habitat and feeding ground preservation 15. Risk of landslides, erosion, and sedimentation 16. Long-term ground/soil contamination 17. Soil conservation 18. Soil restoration 19. Dredged/excavated material 20. Environmental management 21. Environment pollution control plan 22. Impact on the natural environment 23. Effects on trees within the project limits 24. Insertion of invasive species 25. Preservation of historic and archaeological sites 26. Protected area preservation 27. Biodiversity preservation 28. Research and innovation 29. Life cycle energy assessment 30. Energy consumption 31. Energy consumption reduction 32. Renewable energy use 33. Innovative material 34. Use of regional materials

Definition Minimization of adverse impacts on the local air quality by taking appropriate measures at the construction stage CO2 emissions of the project during construction and CO2 emission assessment Greenhouse gas emission intensity of the entire project Emissions from the manufacturing and transportation of building materials and construction equipment and offsite construction-related staff activities Minimization of adverse impacts on indoor air quality during the construction stage by taking appropriate measures in design and construction Preservation and protection of rivers, lakes, vernal pools, wetlands, shorelines, or waterbodies Consultation with regulatory authorities about water issues related to the project, including the need for any consents Incorporation of measures or equipment into the project that will allow long-term monitoring of the impact of the project on the water environment Incorporation of means to monitor water performance during operations Creation and implementation of a plan to control the impacts of the project on the water environment during construction At the construction stage, protection of existing water features from degradation or physical damage by the construction plant and processes Potable water consumption reduction through water reuse and recycling Minimization of the need for land acquisition (outright purchases and/or expropriation of property and purchases of access rights such as rights of way) Extent of loss of habitat or feeding grounds Following of best-management practices to manage erosion and prevent landslides Incorporation of measures or equipment into the project that will allow long-term monitoring of the project's impact on the soil Conservation of topsoil and subsoil and conservation of on-site mineral resources Land remediated and in need of remediation for the existing or intended land use, according to applicable legal designations Design of the project to balance cut and fill to reduce the dredged/excavated material taken off site Creation and use of a unified document to consider and assess the environmental aspects for each stage of the project Creation and implementation of an environment pollution control plan to specify actions to prevent and mitigate pollution to air, land, and water during construction Impact on the natural environment, such as soil, air, water, and ecosystems Percentage of substantial trees present on the site that have been retained Insertion of invasive plants and animals into the environment Identification historic and archaeological sites and develop a sensitive design and approach to conservation and protection Preservation of a protected area, landscape, or townscape Preservation of the local biodiversity Development and implementation of innovative technologies or methods Life cycle energy assessment for the key materials and components to be used in the project Consideration of the energy consumption of the project during construction and energy consumption assessment Energy consumption reductions achieved during project lifetime Extent of renewable energy use to meet the energy needs of the project during construction Implementation of innovative materials in the project Research of all the locally available material sources, including recycled materials, by the designer and contractor

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35. Use of recycled materials 36. Material consumption 37. Material recycling after decommissioning 38. Prefabricated material 39. Sustainable material source 40. Risk management 41. Climate change risks and resilience 42. Disaster risks 43. Flood risk during site selection 44. Establish a sustainability management system 45. Long-term planned maintenance 46. Mitigating vibration 47. Noise pollution 48. Light pollution 49. Construction traffic management plan 50. Sustainable procurement practices 51. Approach/criteria towards contractors 52. Selection of an effective contract type 53. Design for disassembly 54. Quality of infrastructure 55. Drainage systems 56. Toxic waste management 57. Waste disposal method 58. Non-toxic waste management 59. Traffic congestion 60. Transport impact

Economic

61. View from competent authorities 62. Visual harmony with the surroundings 63. Visual impact 64. Adverse impact on tourism values 65. Economic impact on surrounding businesses 66. Economic benefits 67. Affordability for users 68. Durability of structures 69. Life cycle cost 70. Maintenance, operation, and rehabilitation costs of the completed project

Identification of the appropriate reuse of existing structures and materials on the site and incorporation into the project Material consumption by volume of material incorporated into the project Recycling or reuse of materials after the useful life of the project has ended and after disassembly by the owner and project team Consideration of the selection and use of prefabricated units, such as pre-cast concrete units and panels Consideration and implementation of responsible sourcing of sustainable materials Development of a documented plan to identify and mitigate risks concerning the project Creation of a climate impact assessment and adaptation plan that identifies climate change risks and possible responses Consideration of flood risk, quakes, and potential natural risks in the planning and design phase Consideration of the site characteristics, environmental issues, and flood risk during the selection of the site location Creation of a sustainability management policy commensurate with the scope, scale, and complexity of the project; assessment and prioritization of the environmental, economic, and social aspects of the project; and definition of project sustainability goals, objectives, and targets appropriate for the affected communities Consideration of long-term planned maintenance in the design process Performance of appropriate studies to predict the levels of vibration during construction and proposals for ambient vibration mitigation and monitoring Monitoring and mitigation of ambient noise to reduce noise to accepted standard target levels during and after construction Consideration of appropriate measures to prevent light spillage to neighboring areas during operation Creation and implementation of a construction traffic management plan for minimizing the disruption caused by construction traffic Definition of a sound and viable sustainable procurement program Contract requirements for the designers and contractors expressly include the achievement of specified environmental and social performance The construction contract includes clauses on the preservation of the environment and sustainability Active inclusion of a design for disassembly and/or deconstruction at the planning and design phase Consideration of a high quality of design, fully achieved in the construction stage Consideration of the incorporation of sustainable drainage systems Development, execution, and monitoring of a comprehensive toxic waste management plan Specific documented mechanisms for managing waste and identifying and dealing with all waste arising from the civil engineering work Development, execution, and monitoring of a comprehensive non-toxic waste management plan Consideration of measures to minimize the traffic impacts of the completed project on the local community Consideration of transport impacts during the construction and design stages as well as appropriate measures to minimize them The project is in accordance with the aims of applicable policies published by the relevant local, regional, or national authority Consideration of visual harmony with the landscape and other construction at each stage of the project Minimization of the adverse visual impact of the site during the construction stage Negative economic impact on local tourism Economic impact assessment in the local community Economic growth and development of the local community generated by the project Affordable costs, compatible with the capacity of users to pay for the services Consideration of active durability and maintenance requirements of structures and components in the design and specification stage Costs involving construction completion along with the effect of cost decisions on using, maintaining, and supporting the infrastructure Costs of resources allocated to the monitoring, maintenance, and rehabilitation of the completed project

ricardo-vargas.com 71. Rehabilitation cost of the ecosystem 72. Social costs due to the project development 73. Costs for any relocation of people 74. Project governance and strategic management

Social

75. Stakeholder management 76. Participation of stakeholders 77. Accountability and grievance mechanisms for stakeholders 78. Conflict management from locals 79. Respect for local customs 80. Social and cultural impact due to the project 81. Displacement and resident relocation 82. Addition of benefits to users 83. Mobility and transport 84. Extent of blockage 85. Enhance the public space 86. Employment of labor 87. Intergenerational and gender practices 88. Employee training 89. Public health 90. Public participation 91. Public safety 92. Public services 93. Worker health 94. Access to potable water and sanitation services 95. Worker safety 96. Accidents, injuries, fatalities, etc. 97. Project duration

Costs involving the rehabilitation of the ecosystem as a significant indirect economic impact due to project development Social costs involving land tenure and other issues relative to locals Cost of resettling people Appropriately skilled personnel commissioned to undertake the implementation of the management plan, monitoring of the establishment, and review of the objectives and management prescriptions Documented plan to identify and manage the stakeholders Carry out a community consultation at each stage of the project Mechanisms to ensure that comments from the local community will be recorded

The project team is aware of local customs and monitors and manage possible conflicts Reasonable determination of the local customs and informing the workers of them Consideration of the wider social impacts of the project during construction and operation and the effects of the completed project on the human environment Number of persons voluntarily and involuntarily displaced and/or resettled The completed project creates new capacity or increases the quality of the existing, operating, recreational, or cultural capacity for business, industry, or the public Incorporation of design strategies to address access and mobility concerns during and after construction Traffic impacts of the completed project on the local community The project adds to the public space in a way that significantly enhances community livability The project creates a significant number of new jobs during its design, construction, and operation Composition of governance bodies and breakdown of employees per employee category according to gender, age group, minority group membership, and other indicators of diversity Average hours of training per employee by gender and employee category Reduction of the risks to public health to acceptable levels and receipt of approval from the appropriate public health officials Acquisition of input from and alignment between the views of the project team and those of local officials, communities, and decision makers Reduce the risk to public safety to acceptable levels and receive approval from the appropriate public safety officials Incorporation of design strategies to provide and improve public services, such as transportation infrastructure efficiency, walkability, and livability Reduction of the risks to worker health to acceptable levels and receipt of approval from the appropriate public health officials Potable water and sanitation available to workers Reduction of the risks to worker safety to acceptable levels and receipt of approval from the appropriate public safety officials Types of injury and rates of injury, occupational diseases, lost days, absenteeism, and the total number of work-related fatalities Period of time from the beginning of execution of an element to its completion

145

THE SUSTAINABILITY MARKER TO SUPPORT THE PROJECT SELECTION PROCESS: THE UNOPS CASE

Accepted for publication at PMI Global Congress 2014 – North America Phoenix – Arizona – USA – 2014 2015 AACE International Annual Congress Las Vegas – Nevada – USA – 2015

148

The Sustainability Marker to Support the Project Selection Process: The Unops Case

Abstract Related Podcasts

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Understanding the Rolling Wave Planning http://rvarg.as/6z

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♫ Using Generic Resources in the Resource Allocation http://rvarg.as/3e

The objective of this paper is to present a non conventional approach that is being currently implemented at the United Nations Office for Project Services (UNOPS), when selecting new projects globally, in order to include, as project selection criteria, social, environmental and economic sustainability aspects in humanitarian and development projects. Using a set of twenty five themes in four major groups, an internal tool called Sustainability Marker was developed to analyse projects above and beyond the traditional financial criteria in order to evaluate the real impact of the project to the sustainable development goals.

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Traditional Project Evaluation Criteria When looking into how organizations decide over which projects to execute, we can notice a constant desire to have clear, objective and mathematical criteria (HAAS & MEIXNER, 2005). However, decision making is, in its totality, a cognitive and mental process derived from the most possible adequate selection based on tangible and intangible criteria (SAATY, 2009), which are arbitrarily chosen by those who make the decisions. Basically, the prioritization of projects in a portfolio is nothing more than an a sequencing scheme based on a benefit cost relationship for each project. Projects with higher benefits, when compared to their costs, will have a higher priority. It’s important to observe that a benefit cost relationship does not necessarily mean the use of exclusive financial criteria like the widely known benefit cost ratio, but instead a broader concept of the reaped benefits from executing the project and their related efforts (VARGAS, 2010). In most enterprises, the main criteria groups are related to financial, strategic, 1See Catalog of Catastrophe http://calleam.com/ risks, urgency and stakeholder commitment aspects (VARGAS, 2010). The main at WTPF/?page_id=3 challenge is to put in place criteria that can capture outcomes instead of just basic outputs. Many real cases support the lack of understanding of the real expected outcomes, where projects were delivered to time, cost and quality objectives and yet are not yielding positive results (DUGAL, 2010). Project managers have constructed bridges without access roads, have built hospitals and courthouses which are empty later on, have implemented ERP systems and other business changes that have destroyed organizations1. PMI’s Standard for Portfolio Management (PMI, 2012) mentions that the scope of a project portfolio must stem from the strategic objectives of the organization. These objectives must be aligned with the business scenario which in turn may be different for each organization. Consequently, there is no perfect model that covers the right criteria to be used for any type of organization when prioritizing and selecting its projects. The criteria to be used by the organization should be based on the values and preferences of its decision makers.

UNOPS Sustainability Criteria With a strong focus on the developing world, United Nations Office for Project Services (UNOPS) states that a project can only be considered sustainable if it address the impacts on a broader set of stakeholders, including generations not yet born (BOBROW, 2014). This comprises sustainability aspects that should be embedded into the project while executing it (How) and the sustainability aspects after its conclusion (Aim).

149

150

The Sustainability Marker to Support the Project Selection Process: The Unops Case

AIM

HOW

Projects and programmes comply to strict criteria concerning environmental and social scope and planned results before they are initiated.

UNOPS teams embed cross-cutting initiatives, such as how to get the best community support, into their project planning and implementation phases.

Exhibit 1 – UNOPS definition of a sustainable project

Insert sustainable principles into every single project is a major task and sometimes the decision on what actions should be in place create a dilemma for the project manager and the project team. In the locations where UNOPS operates, funding is often too limited to address all of the basic needs. Let’s take the example of building a school. There can be enough funding to put solar panels on the roof or to build more class room space but not both. If we put the panels on then the school can have electricity and provide space for computers and potentially evening classes. However, if instead the classroom is made larger, more children can attend. How should a project manager make such a decision? (BOBROW, 2014). Input Consultants and materials

Process Construction

Output School built

Outcome Children successfully attending school

Impact Achieve universal Primary Education (MDG2)

Exhibit 2 – Example of transforming Inputs in to outputs, outcomes and impact in the development sector (UNOPS, 2014)

In order to support an informed decision making process, four categories of sustainability were identified based on the Aspire Model with some adaptation to the development context:

• Social – It covers aspects such as gender, population, vulnerability and other aspects related to the community where the project is being implemented.

• Environmental – It covers aspects such as air, land, water and biodiversity where the project is being implemented.

• Economic – It covers aspects such as the economic relevance for the community, job generation, equity and livelihood.

• National Capacity – It covers aspects such as the use of local capacity to deploy the project including skills and knowledge, corruption, political and social stability.

25 Themes of the Sustainability Marker After discussions with partners, project managers and experts in the field, the 20 themes within the ASPIRE model were adapted and augmented into the 25 themes now represented in the UNOPS Sustainability Marker. (Exhibits 3, 4, 5 and 6).

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SOCIAL

THEMES

QUESTIONS

CONSIDERATIONS

Populations

What is the likely effect of the project on local communities?

Population change Community cohesion &RQÀLFW VHQVLWLYLW\ Displacement Population density DIIHFWLQJ HQYLURQPHQWDO sustainability 3RSXODWLRQ PRYHPHQW DQG WUDI¿F

Cultures

What is the likely effect of the project in terms of community culture and identity?

Socio-cultural identity Cultural and religious facilities Main stakeholder’s heritage and archaeology 8VH RI HQYLURQPHQW Intergenerational practices

Services

What is the likely effect of the project on access to HVVHQWLDO VHUYLFHV"

Energy Mobility & transport Road construction Telecommunications Education Communal space Crime, Security, Police, Fire and Ambulance VHUYLFHV

Health

What is the likely effect of the project in relation to the public health infrastructure and VHUYLFHV"

Sanitation Solid waste Drainage Healthcare Shelter Nutrition HIV/AIDS and other communicable diseases

Vulnerability

What is the likely effect of the project on community institutions and social networks?

Community cohesion Vulnerable groups Indigenous groups Minority groups People with disabilities

Resilience

What is the likely effect of the project in terms of resilience of communities to shocks, stresses and hazards?

Ability to resist and UHFRYHU LQ D WLPHO\ DQG HI¿FLHQW PDQQHU Physical infrastructure /LYHOLKRRG GLYHUVL¿FDWLRQ $FFHVV WR ¿QDQFH DQG insurance

Gender

What is the likely effect of the project on gender equality or female empowerment?

Differential effect on men and women Access for women

Exhibit 3 – Social themes

151

The Sustainability Marker to Support the Project Selection Process: The Unops Case

ECONOMIC

152

THEMES

QUESTIONS

CONSIDERATIONS

Viability

What will be the likely effect on the project after external funding and UNOPS involvement are withdrawn?

Value for money Risk management Carbon pricing Operation and maintenance Alignment with national/regional strategies Appropriate technologies Functionality for the full planned life span

Macro

What is the likely effect of the project on the vitality of the local economy?

Vitality and regeneration Value added/multiplier effects Debt ,QÀDWLRQ HIIHFWV Ethical competition

Livelihoods

What is the likely effect of the project on employment and livelihood opportunities of the project-affected communities?

Local sourcing $FFHVV WR ¿QDQFH Distortions to local economy Employment creation Labor standards Training

Equity

:LOO WKH EHQH¿WV of the project be equally accessible to all members of society? If yes, indicate a positive effect. If no, indicate a negative effect.

Equal opportunities Affordability of services Debt Land tenure Communication about the project

Exhibit 4 – Economic themes

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ENVIRONMENTAL

THEMES

QUESTIONS

Air

What is the likely effect of the project on local air quality?

Ambient air quality Direct emissions Dust and particulates Ozone depleters

Land

What is the likely VLJQL¿FDQW HIIHFW RI the project on land resources and land usage?

Site location Planning intent Diversity/mixed use Contaminated land Soil conservation”

Water

What is the likely effect on surface, groundwater or coastal waters?

Drainage systems W ater pollution Sewage treatment and disposal Water availability :DWHU HI¿FLHQF\ ³

Biodiversity

What is the likely effect of the project on natural ecosystems?

Protected areas Nature conservation Ecosystems Environmental risk management

Energy

What is the likely effect on shared / municipal energy supplies?

(QHUJ\ HI¿FLHQF\

Materials Lifecycle

What is the likely effect on the ecosystems and communities from the materials used throughout the life of the project or asset?

Whole life analysis Local sourced materials Recyclability

Materials Waste

What is the likely effect of project ‘waste’ on the ecosystems and communities?

Shared / municipal waste disposal facilities Volume of solid waste Toxic or hazardous waste

Global Climate

What is the likely effect of the project on greenhouse gas emissions?

Net reduction or net increase in greenhouse gas emissions

Disaster Risk Reduction

What is the likely effect of the project on the risk environment?

Reduction or increase LQ ULVN SRVHG E\ ÀRRGV droughts, landslides, due to construction

Exhibit 5 – Environmental themes

CONSIDERATIONS

153

The Sustainability Marker to Support the Project Selection Process: The Unops Case

THEMES Structures Institutions

NATIONAL CAPACITY

154

QUESTIONS

CONSIDERATIONS

What is the likely effect of the project on institutional civil structures?

National/local government effectiveness Effective delivery of services Capacity to run/ maintain the project/ services after handover? Project - Government coordination

Will this project have effect on corruption within institutions (private sector / government / civil society)?

Corruption Civil society Rule of law

Skills & Capacity Development

What is the likely effect of the project on the skills and capacity of local / national private sector / government / civil society to undertake similar projects in the future?

Research and innovation Local supply chains Information disclosure and reporting Monitoring and evaluation Media channels Knowledge exchange

Political

What is the likely effect of the project on political stability or security?

Stability of national governments Effect on areas of FRQÀLFW 3ROLWLFDO UDPL¿FDWLRQV

Policies

What is the likely effect of the project in terms of alignment with local policy and regulatory frameworks or international as appropriate?

Regulatory quality Human rights Health and safety Quality assurance Intellectual property rights

Structures Corruption

Exhibit 6 – National Capacity themes

Evaluating the Sustainability Themes UNOPS project life cycle follows a five stage process (UNOPS, 2014) as presented in the Exhibit 7.

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Lead Generation

High

Final Stage

DURATION

Subsequent Stages

PreEngagement

FIXED DURATION Initiation

RESOURCE ORIENTED DURATION

Resource orientation validity limit Exhibit 7 – UNOPS Engagement Process (UNOPS, 2014)

PROJECT MANAGEMENT

The Engagement process steps follow a progressive development whereCYCLE more effort is put in place during the initial phases, in orderHigh to collaborate with the AMOUNT OF RESOURCES project’s key stakeholders to adjust the plans and outputs to address the relevant sustainability aspects. This approach follows the concept that the capability to add value decreases and the cost of correction increases exponentially over time for an given project (VARGAS, 2014).

COST OF CHANGE/CORRECTION

Constructive Opportunity

Destructive Intervention

POTENTIAL TO ADD VALUE Begin

TIME

End

Exhibit 8 – Potential to add value again the cost of correction for an specific project (VARGAS, 2014)

On the Lead Generation phase, a bigger effort is put into place to work with the key stakeholders on the relevance of each sustainability aspect.

155

Economic

Livelihoods Macro

The Sustainability Marker to Support the Project Selection Process: The Unops Case Viability

During this stage, training, discussion forums and informal assessments are put Gender in place to create the positiveVulnerability environment for the Pre-Engagement phase asResilience sessment.

Social

156

Equity

Health

On the Pre-Engagement phase,Services the team needs to assess the twenty five themes for the project delivery (effectsCultures during project delivery) and for the post-project During Delivery Post-project Population (effects after the outcome has been delivered). The effect-based scale is presented on the Exhibit 9. Addressed Effect Effect Addressed Effect

Environmental

I don’t know

After consolidating the information, chart is presented for both Low Positive N/A Materials –Life Cyclethe marker Materials scenarios – Waste Post-project and During Delivery (Exhibit 10). High Positive

National Capacity

Social

Global Climate

Cultures Services

Population Air

Land

Health

Energy Materials – Life Cycle

Vulnerability

I DON’T Gender KNOW

HIGH NEGATIVE

Economic

Structures – Institutions Structures – Corruption

Cultures Services

Environmental Land

Water Biodiversity

Health

Skills & Capacity

Global Climate

Political

Energy Materials – Life Cycle Materials – Waste

Vulnerability

Global Climate

Policies LOW NEGATIVE

Disaster Risk Reduction

Population Air

Resilience

Materials – Waste

Structures – Institutions

Viability

Gender

Disaster Risk Reduction LOW POSITIVE

NEUTRAL OR N/A

HIGH POSITI

Structures – Institutions

Viability

Equity Structures – Corruption

Macro

Structures – Corruption

Livelihoods

Macro

SPECIFIC ACTIVITIES Politics

Skills & Capacity Building

Livelihoods

Equity

Economic

Social

During Delivery

Environmental Social Disaster Risk Water Biodiversity

Resilience

SPECIFIC ACTIVITIES

None

Land

High Negative Specific Activities Water Exhibit 9 – Effect based scale on the Pre-Engagement Phase (during and after project Low Negative Activities/Deliverables/Budget Lines Biodiversity delivery) Neutral or N/A Theme is a direct project benefit Energy

Post-Project

NONE

Marker

Air

I DON’T KNOW

HIGH NEGATIVE

Policies LOW NEGATIVE

NEUTRAL OR N A

Macro

ACTIVITIES, DELIVERABLES, Livelihoods Viability BUDGET LINES AND/OR National Capacity Economic REPORTING REQUIREMENT

LOW POSITIVE

HIGH POSITIVE

Gender

SPECIFIC ACTIVITIES

I DON’T KNOW

Skills & Capacity Building

Equity

HIGH NEGATIVE

THEME ISPolitics A DIRECT BENEFIT Policies OF THE PROJECT National Capacity LOW NEGATIVE

NEUTRAL OR N A

LOW POSITIVE

HIGH POSITIVE

Exhibit 10 – Example of Sustainability Marker Wheel Graph during the Pre-Engagement Vulnerability phase Resilience

After the Pre-engagement phase, the project team will work with the stakeholdHealth ers to address any relevant sustainability aspects through direct actions in the Services project delivery or changes toCultures the scope statement and scope definition (PMI, Population potential budget lines or a direct benefit of 2013a) to include specific activities, the project, if applicable (Exhibit 11). Effect I don’t know High Negative Low Negative

Marker None Specific Activities Exhibit 11 – Action plan scale on the Initiation Phase Activities/Deliverables/Budget Lines

Neutral or N/A Theme is ato direct benefit The final result depicts an expected improvement theproject sustainability aspects Low Positive N/A to be delivered by the project by the implemented actions, like it is presented in High Positive the Exhibit 12.

N/A

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During Delivery Addressed

Post-project

Effect

Addressed

Environmental

Air Land Water Biodiversity Energy Materials –Life Cycle Materials – Waste Global Climate

National Capacity

Disaster Risk Structures – Institutions Structures – Corruption Skills & Capacity Political

Economic

Policies

Equity Livelihoods Macro Viability

Gender

Social

Vulnerability Resilience Health Services Cultures Population

Effect

Marker

I don’t know

None

High Negative

Specific Activities

Low Negative

Activities/Deliverables/Budget Lines

Neutral or N/A

Theme is a direct project benefit

Low Positive

N/A

High Positive

I DON’T KNOW

HIGH NEGATIVE

SPECIFIC ACTIVITIES

LOW NEGATIVE

NEUTRAL OR N/A

ACTIVITIES, DELIVERABLES,

LOW POSITIVE

THEME IS A DIRECT BENEFIT

NONE12 – Example of Sustainability BUDGET LINES AND/OR Exhibit Marker Bar Graph during the InitiationOFPhase THE PROJECT REPORTING REQUIREMENT (Including Action Plans)

Conclusions This paper aimed to present and discuss the selection criteria implemented by UNOPS to address social, environmental and economic sustainability in human-

HIGH POSITIVE

N/A

157

158

The Sustainability Marker to Support the Project Selection Process: The Unops Case

itarian and development projects by using twenty five themes grouped in four dimensions for the project execution and post-project results. The UNOPS Sustainability Marker is currently on pilot in most of the 1,300 UNOPS projects globally. Challenges related to different cultural aspects, resistance to change and short term need x long term perspective have been addressed and incorporated on each new release of the tool.

References ASPIRE Version 1.3 (2013). Arup International Development. Available at http:// www.oasys-software.com/products/environmental/aspire.html. Email: aspire@ arup.com BOBROW, H. (2014). Taking Responsibility for Project Success: Sustainability and the Project Process. Dubai: PMI Global Congress EMEA. DUGGAL, J.S. (2010). Next Level Up: How Do You Measure Project Success? Rethinking the Triple Constraint. Retrieved on 1st August 2014 from http://www.pmi.org/ Knowledge-Center/Next-Level-Up-How-Do-You-Measure-Project-Success.aspx HAAS, R. & MEIXNER, O. (2005). An Illustrated Guide To Analytic Hierarchy Process. Vienna: University of Natural Resources and Applied Life Sciences. PMI (2013a). The Project Management Body of Knowledge: Fifth Edition. Newtown Square: Project Management Institute. PMI (2013b). The Standard for Portfolio Management: Third Edition. Newtown Square: Project Management Institute. SAATY, T. L. (2009). Extending the Measurement of Tangibles to Intangibles. International Journal of Information Technology & Decision Making, Vol. 8, No. 1, pp. 7-27, 2009. Available at SSRN: http://ssrn.com/abstract=1483438. UNOPS (2014). Body of Knowledge Sustainable Project Management Practice: Twelfth Edition. Copenhagen: UNOPS. VARGAS, R. V. (2010). Using the Analytic Hierarchy Process (AHP) to Select and Prioritize Projects in a Portfolio. Washington/DC: PMI Global Congress North America. VARGAS, R. V. (2014). Gerenciamento de Projetos - Estabelecendo Diferenciais Competitivos: Oitava Edição. Rio de Janeiro: Brasport (Brazilian Portuguese).

APPLYING NEURAL NETWORKS AND ANALOGOUS ESTIMATING TO DETERMINE THE PROJECT BUDGET

Accepted for publication at PMI Global Congress 2015 - North America Orlando – Florida – USA – 2015

160

Applying Neural Networks and Analogous Estimating to Determine the Project Budget

Abstract Related Podcasts

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Importance of the Work Breakdown Structure (WBS) to the Project Cost Management http://rvarg.as/et

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♫ Cost Estimating – Part 2 of 3 http://rvarg.as/d0

♫ Cost Estimating – Part 3 of 3 http://rvarg.as/d2

♫ Applying Neural Networks

and Analogous Estimating to Determine the Project Budget http://rvarg.as/nn

This paper aims to discuss the use of the Artificial Neural Networks (ANN) to model aspects of the project budget where traditional algorithms and formulas are not available or not easy to apply. Neural networks use a process analogous to the human brain, where a training component takes place with existing data and subsequently, a trained neural network becomes an “expert” in the category of information it has been given to analyse. This “expert” can then be used to provide projections given new situations based on an adaptive learning (STERGIOU & CIGANOS, 1996). The article also presents a fictitious example of the use of neural networks to determine the cost of project management activities based on the complexity, location, budget, duration and number of relevant stakeholders. The example is based on data from 500 projects and is used to predict the project management cost of a given project.

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Artificial Neural Networks (ANN) Some categories of problems and challenges faced in the project environment may depend on many subtle factors that a computer algorithm cannot be created to calculate the results (KRIESEL, 2005). Artificial Neural Networks (ANN) are a family of statistical learning models inspired by the way biological nervous systems, such as the brain, process information. They process records one at a time, and “learn” by comparing their classification of the record with the known actual classification of the record. The errors from the initial classification of the first record are fed back into the network, and used to modify the networks algorithm the second time around, and so on for a large number of iterations in a learning process in order to predict reliable results from complicated or imprecise data (STERGIOU & CIGANOS, 1996) (Exhibit 01).

Exhibit 01 - Artificial Neural Networks Architecture (adapted from MCKIM, 1993 and STERGIOUS & CIGANOS, 1996)

Some typical applications of ANN are

• handwriting recognition, • stock market prediction, • image compression, • risk management, • sales forecasting • industrial process control. The mathematical process behind the calculation uses different neural network configurations to give the best fit to predictions. The most common network types are briefly described below. Probabilistic Neural Networks (PNN) – Statistical algorithm where the operations are organized in multi-layered feedforward network with four layers (input, pattern, summation and output). It is fast to be trained but it has a slow execution and requires large memory. It is also not as general as the feedforward networks (CHEUNG & CANNONS, 2002).

161

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Applying Neural Networks and Analogous Estimating to Determine the Project Budget

Multi-Layer Feedforward Networks (MLF) – MLF neural networks, trained with a back-propagation learning algorithm (Exhibit 02). They are the most popular neural networks (SVOZIL, KVASNIČKA & POSPÍCHAL, 1997).

Exhibit 02 - Training data and generalization in a Multi-Layer Feedforward Network (SVOZIL, D , KVASNIČKA, V. & POSPÍCHAL, J. , 1997)

Generalized Regression Neural Networks (GRNN) – Closely related to PNN networks, it is a memory-based network that provides estimates of continuous variables. It is a one-pass learning algorithm with a highly parallel structure. The algorithmic form can be used for any regression problem in which an assumption of linearity is not justified (SPECHT, 2002).

Analogy Process and Data Set One of the key factors of the Neural Networks is the data set used on the learning process. If the data set is not reliable, the results from the networks calculations will not be reliable. The use of Artificial Neural Networks can be considered one kind of analogy (BAILER-JONES & BAILER-JONES, 2002). Analogy is a comparison between two or more elements, typically for the purpose of explanation or clarification (Exhibit 03). One of the most relevant uses of the analogy is to forecast future results based on similar results obtained in similar conditions (BARTHA, 2013). The challenge is to understand what a similar condition is. Projects in the past can be a reference for future projects if the underlining conditions where they were developed still exist in the project subject to the analysis.

ricardo-vargas.com

are to

Exhibit 03 - Simple analogy example “sock are to feet as gloves are to hands” (Adaptedfrom Spitzig, 2013)

One of the most relevant aspects of the analogy is related to the simple process of estimation based on similar events and facts. This process reduces the granularity of all calculations, where the final project costs can be determined by a set of fixed finite variables.

Data Set, Dependent and Independent Categories and Numeric Variables The first step to develop an Artificial Neural Network is to prepare the basic data set that will be used as a reference for the “training process” of the neural network. It is important to highlight that usually the right dataset is expensive and time consuming to build (INGRASSIA & MORLINI, 2005). A dataset is composed by a set of variables filled with information that will be used as a reference. These references are called cases (Exhibit 04). VARIABLES INDEPENDENT VARIABLES V1

DEPENDENT VARIABLE (OUTPUT) Vn

V’1

CASES

Case 1 Case 2 Case 3

Case n Exhibit 04 - Structure of a basic dataset

The most common variables types are Dependent Category – dependent or output variable whose possible values are taken from a set of possible categories; for example Yes or No, or Red, Green or Blue. Dependent Numeric – dependent or output variable whose possible values are numeric.

163

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Applying Neural Networks and Analogous Estimating to Determine the Project Budget

Independent Category – an independent variable whose possible values are taken from a set of possible categories; for example Yes or No, or Red, Green or Blue. Independent Numeric – an independent variable whose possible values are numeric. In the project environment, several variables can be used to calculate the project budget. Some common examples are Complexity – Level of complexity of the project (Low, Medium, High). Usually it is an independent category. Location – Location where the project works will happen. Associated to the complexity of the works and logistics. Most of the time it is an independent category. Budget – Planned budget of the project. It is a numeric variable that can be independent or dependent (output). Actual Cost – Actual Expenditure of the project. It is most of the time an independent numeric variable. Cost Variance – The difference between the budget and the actual cost. It is a numeric variable that can be independent or dependent (output) Baseline Duration – Duration of the project. Independent numeric variable. Actual Duration – Actual duration of the project. Usually an independent numeric variable. Duration Variance – The difference between the baseline duration and the actual duration. Type of Contract – Independent category variable that defines the type of the contract used for the works in the project (ie: Fixed Firm Price, Cost Plus, Unit Price, etc). Number of Relevant Stakeholder Groups – Independent numeric variable that reflect the number of relevant stakeholder groups in the project. Some examples of input variables are presented at the Exhibit 05, 06 and 07.

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INPUT VARIABLES

DESCRIPTION

UNIT

RANGE

PWA

Predominant Work Activity

Category

Crushed Stone or Cement Stab.

US Dollars (2000)

572.501.64-4.006.103.95

OUTPUT VARIABLE

USDPERKM

Unit Cost of New Construction Project

Exhibit 05 – Example of Variables in Road Construction (SODIKOV, 2005)

DESCRIPTION

RANGE

Ground floor

100–3668 m2

Area of the typical floor

0–2597 m2

No. of storeys

1–8

No. of columns

10–156 1 – isolated

Type of foundation

2 – isolated and combined 3 – raft or piles

No. of elevators

0–3

No. of rooms

2–38

Cost of structural skeleton

6,282469,680 USD

Exhibit 06 – Example of key variables for buildings (ARAFA & ALQEDRA, 2011)

165

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Applying Neural Networks and Analogous Estimating to Determine the Project Budget

PROJECT CHARACTERISTICS

UNIT

TYPE OF INFORMATION

DESCRIPTORS

Gross Floor Area (GFA)

Quantitative

n.a

Principal structural material

No unit

Categorical

Procurement route

No unit

Categorical

1 – steel 2 – concrete 1 – traditional 2 – design and construct 1 – residential

Type of work

No unit

Categorical

2 – commercial 3 – office 1 – central business district

Location

No unit

Categorical

2 – metropolitan 3 – regional

Sector

No unit

Categorical

Estimating method

No unit

Categorical

1 – private sector 2 – public sector 1 – superficial method 2 – approximate quantities 1 – one to two storey(s)

Number of storey

No unit

Estimated Sum

Cost/m

Categorical

2 – three to seven storeys 3 – eight storeys and above

Quantitative

n.a

Exhibit 07 – Example of variables for a building construction ( AIBINU, DASSANAYAKE & THIEN, 2011)

Training Artificial Neural Networks When the dataset is ready the network is ready to be trained. Two approaches can be used for the learning process: supervised or adaptive training. In the supervised training, both inputs and outputs are provided and the network compares the results with the provided output. This allows the monitoring of how well an artificial neural network is converging on the ability to predict the right answer. For the adaptive training, only the inputs are provided. Using self-organization mechanisms, the neural networks benefits from continuous learning in order to face new situations and environments. This kind of network is usually called self-organizing map (SOM) and was developed by Teuvo Kohonen (KOHONEN, 2014). One of the biggest challenges of the training method is to decide on which network to use and the runtime process in the computer. Some networks can be trained in seconds but in some complex cases with several variables and cases, hours can be needed just for the training process.

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The results of the training process are complex formulas that relate the input or independent variables with the outputs (dependable variables) like the graph presented in the Exhibit 2. Most of the commercial software packages usually test the results of the training with some data points to evaluate the quality of the training. Around 10 to 20% of the sample is used for testing purposes (Exhibit 08).

Exhibit 08 – Training results example to forecast the bloody pressure where some data is used for testing the network results (Palisade Neural Tools software example)

Prediction Results After the training, the model is ready to predict future results. The most relevant information that should be a focus of investigation is the contribution of each individual variable to the predicted results (Exhibit 09) and the reliability of the model (Exhibit 10).

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Applying Neural Networks and Analogous Estimating to Determine the Project Budget

Exhibit 09 - Example of Relative Variable impacts, demonstrating that the Salary variable is responsible for more than 50% of the impact in the dependent variable (Palisade Neural Tools software example)

Exhibit 10 – Example of histogram of Probability of Incorrect Categories showing a chance of 30% that 5% of the prediction can be wrong (Palisade Neural Tools software example)

It is important to highlight that one trained network that fails to get a reliable result in 30% of the cases is much more unreliable than another one that fails in only 1% of the cases.

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Example of Cost Modeling using Artificial Neural Networks In order to exemplify the process, a fictitious example was developed to predict the project management costs on historical data provided by 500 cases1. The variables used are described in the Exhibit 11. NAME

DESCRIPTION

VARIABLE TYPE

Project ID

ID Count of each project in the dataset

–

Location

Location where the project was developed (local or remote sites) Independent Category

Complexity

Qualitative level of project complexity (Low, Medium and High)

Independent Category

Budget

Project Budget (between $500,000 and $2,000,000)

Independent Numeric

Duration

Project Duration (Between 12 and 36 months)

Independent Numeric

Relevant Stakeholder Groups

Number of relevant stakeholder groups for communication and monitoring (between 3 and 5)

Independent Category

PM Cost

Actual cost of the project management activities (planning, budgeting, controlling)

Dependent Numeric (Output)

Exhibit 11 – Variables used on the example dataset

The profiles of the cases used for the training are presented at the Exhibit 12, 13, 14, 15 and 16 and the full dataset is presented in the Appendix.

Location Local Remote Total

Projects % 250 50.00% 250 50.00% 500 100.00%

Remote 50%

Local 50%

Exhibit 12 – Distribution of cases by Location

Complexity Low Medium High Total

Projects % 201 40.20% 176 35.20% 123 24.60% 500 100.00%

High 25%

Low 40%

Medium 35%

Exhibit 13 – Distribution of cases by Complexity

1 The example was developed to demonstrate the use of the artificial networks and was not built from actual data. All data used is fictitions and should be considered only as an example.

169

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Applying Neural Networks and Analogous Estimating to Determine the Project Budget

Budget Range 500000-750000 750000-1000000 1000000-1250000 1250000-1500000 1500000-1750000 1750000-2000000 Total

180 160 140 120 100 80 60 40 20 0

Projects % 158 31.60% 111 22.20% 99 19.80% 74 14.80% 43 8.60% 15 3.00% 500 100.00%

Budget Range 500000-750000 750000-1000000 1000000-1250000 1250000-1500000 1500000-1750000 1750000-2000000 Total

Projects 158 111 99 74 43 15 500

Exhibit 14 – Distribution of cases by Project Budget

Duration Range 12-17 18-23 24-29 30-36 Total

Projects % 145 29.00% 189 37.80% 96 19.20% 70 14.00% 500 100.00%

200 180 160 140 120 100 80 60 40 20 0

Duration Range 12-17 18-23 24-29 30-36 Total

12-17

18-23

Projects 145 189 96 70 500

24-29

30-36

Exhibit 15 – Distribution of cases by Project Duration

Stakeholder Groups 3 4 5 Total

Projects % 238 47.60% 158 31.60% 104 20.80% 500 100.00%

5 21% 4 32%

3 47%

Exhibit 16 – Distribution of cases by Relevant Stakeholder Groups

The training and the tests were executed using the software Palisade Neural Tools. The test was executed in 20% of the sample and a GRNN Numeric Predictor. The summary of the training of the ANN is presented at the Exhibit 17.

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NET INFORMATION

PREDICTION

Name

Net Trained on Project Data

Number of Cases

Configuration

GRNN Numeric Predictor

Live Prediction Enabled

YES

Location

This Workbook

Independent Category Variables

3 (Location, Complexity, Relevant Stakeholder Groups)

Independent Numeric Variables Dependent Variable

TESTING

Number of Cases

100

2 (Budget, Duration)

% Bad Predictions (30% Tolerance)

0.0000%

Numeric Var. (PM Cost)

Root Mean Square Error

1,508.66

Mean Absolute Error

1,170.24

Std. Deviation of Abs. Error

952.16

TRAINING

Number of Cases

400

Training Time

00:00:38

DATA SET

Number of Trials

Name

Project Data

Reason Stopped

Auto-Stopped

Number of Rows

501

% Bad Predictions (30% Tolerance)

0.0000%

Manual Case Tags

Root Mean Square Error

944.70

Mean Absolute Error

706.47

Std. Deviation of Abs. Error

627.18

VARIABLE IMPACT ANALYSIS

Budget

54.3124%

Duration

25.4821%

Location

8.2800%

Complexity

7.0677%

Relevant Stakeholder Groups

4.8578%

Predicted

50000

40000

30000

20000

10000

The relative impact of the five independent variables are described at the Exhibit 18, demonstrating that more than 50% of the impact in the Project Management cost is related to the project budget in this fictitious example.

60000

Exhibit 17 – Palisade Neural Tools Summary Table

Rela%ve Variable Impacts 0%

10%

20%

30%

40%

Budget

Complexity

60%

70%

54.3124%

Dura9on

Loca9on

50%

25.4821%

8.2800%

7.0677%

Exhibit 18 – Relative Variable Impacts Relevant Stakeholder Groups 4.8578%

80%

171

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Applying Neural Networks and Analogous Estimating to Determine the Project Budget

The training and tests were used to predict the Project Management Cost of a fictitious project with the following variables. NAME

VARIABLE TYPE

Location

Local Project

Complexity

High Complexity

Budget

$810,756

Duration

18 months

Relevant Stakeholder Groups

5 Stakeholder groups

Relevant Stakeholder Groups

Independent Category

PM Cost

Dependent Numeric (Output)

Exhibit 19 – Basic information of a future project to be used to predict the Project Management costs

After running the simulation, the Project Management cost predictions based on the patterns in the known data is $24,344.75, approximately 3% of the project budget.

Conclusions The use of Artificial Neural Networks can be a helpful tool to determine aspects of the project budget like the cost of project management, the estimated bid value of a supplier or the insurance cost of equipment. The Neural Networks allows some precise decision making process without an algorithm or a formula based process. With the recent development of software tools, the calculation process becomes very simple and straightforward. However, the biggest challenge to produce reliable results lies in the quality of the known information. The whole process is based on actual results, and most of the time the most expensive and laborious part of the process is related to getting enough reliable data to train and test the process.

References AIBINU, A. A., DASSANAYAKE, D. & THIEN, V. C. (2011). Use of Artificial Intelligence to Predict the Accuracy of Pretender Building Cost Estimate. Amsterdam: Management and Innovation for a Sustainable Built Environment. ARAFA, M. & ALQEDRA, M. (2011). Early Stage Cost Estimation of Buildings Construction Projects using Artificial Neural Networks. Faisalabad: Journal of Artificial Intelligence. BAILER-JONES, D & BAILER-JONES, C. (2002). Modeling data: Analogies in neural

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networks, simulated annealing and genetic algorithms. New York: Model-Based Reasoning: Science, Technology, Values/Kluwer Academic/Plenum Publishers. BARTHA, P (2013). Analogy and Analogical Reasoning. Palo Alto: Stanford Center for the Study of Language and Information. CHEUNG, V. & CANNONS, K. (2002). An Introduction to Neural Networks. Winnipeg, University of Manitoba. INGRASSIA, S & MORLINI, I (2005). Neural Network Modeling for Small Datasets In Technometrics: Vol 47, n 3. Alexandria: American Statistical Association and the American Society for Quality KOHONEN, T. (2014). MATLAB Implementations and Applications of the Self-Organizing Map. Helsinki: Aalto University, School of Science. KRIESEL, D. (2005). A Brief Introduction to Neural Networks. Downloaded on 07/01/2015 at http://www.dkriesel.com/_media/science/neuronalenetze-en-zeta2-2col-dkrieselcom.pdf MCKIM, R. A. (1993). Neural Network Applications for Project Management. Newtown Square: Project Management Journal. SODIKOV, J. (2005). Cost Estimation of Highway Projects in Developing Countries: Artificial Neural Network Approach. Tokyo: Journal of the Eastern Asia Society for Transportation Studies, Vol. 6. SPECHT, D. F. (2002). A General Regression Neural Network. New York, IEEE Transactions on Neural Networks, Vol 2, Issue 6. SPITZIG, S. (2013). Analogy in Literature: Definition & Examples in SAT Prep: Help and Review. Link accessed on 06/30/2015: http://study.com/academy/lesson/analogy-in-literature-definition-examples-quiz.html STERGIOUS, C & CIGANOS, D. (1996). Neural Networks in Surprise Journal Vol 4, n 11. London, Imperial College London. SVOZIL, D, KVASNIČKA, V. & POSPÍCHAL, J. (1997). Introduction to multi-layer feed-forward neural networks In Chemometrics and Intelligent Laboratory Systems, Vol 39. Amsterdam, Elsevier Journals.

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Appendix – Example Dataset

LOCATION

COMPLEXITY

BUDGET

DURATION

RELEVANT STAKEHOLDER GROUPS

Remote

Medium

703,998.33

21,547.87

3.06%

train

Remote

High

902,327.29

33,934.58

3.76%

test

Local

Low

904,824.77

14,789.98

1.63%

train

Local

Low

640,833.02

15,128.69

2.36%

train

Local

Low

683,992.89

16,985.82

2.48%

test

Remote

High

1,467,802.93

49,416.03

3.37%

train

Remote

High

569,532.07

16,983.74

2.98%

train

Remote

Low

1,235,140.98

47,896.02

3.88%

train

Remote

Low

1,254,182.69

45,954.54

3.66%

Local

Low

634,127.64

15,747.50

2.48%

Remote

High

1,310,397.18

34,507.13

2.63%

train

Local

High

1,045,689.31

31,603.05

3.02%

test

Local

Medium

1,070,909.21

27,486.67

2.57%

train

Remote

High

1,069,089.15

31,359.95

2.93%

train

Remote

Low

600,174.43

21,491.96

3.58%

train

Remote

Low

1,274,790.04

39,018.57

3.06%

test

Remote

Low

1,333,972.58

50,212.10

3.76%

train

Remote

High

1,600,399.26

58,948.04

3.68%

train

Remote

High

1,208,443.26

28,297.71

2.34%

train

Local

Low

1,618,395.90

49,810.63

3.08%

Remote

Low

580,524.22

18,125.26

3.12%

Remote

Low

1,277,669.74

30,434.75

2.38%

train

Local

High

1,465,538.27

35,679.52

2.43%

Local

High

534,389.92

16,322.33

Local

Low

1,110,809.34

Remote

Low

938,755.52

Remote

Medium

573,363.07

Remote

High

1,030,776.33

Remote

High

961,099.65

Local

Medium

Remote

High

Local

PM COST

% PM COST

TAG USED

TEST

GOOD OR BAD

RESIDUAL

33,275.83

Good

658.75

1.94%

17,240.60

Good

-254.78

-1.50%

test

43,510.55

Good

2,443.99

5.32%

test

16,691.89

Good

-944.38

-6.00%

29,729.94

Good

1,873.11

5.93%

37,604.96

Good

1,413.61

3.62%

test

43,252.94

Good

6,557.69

13.17%

test

18,178.96

Good

-53.70

-0.30%

test

36,732.46

Good

-1,052.93

-2.95%

3.05%

test

16,106.84

Good

215.49

1.32%

26,152.74

2.35%

test

24,588.71

Good

1,564.03

5.98%

33,616.39

3.58%

train

17,287.77

3.02%

train

27,716.43

2.69%

train

41,943.37

4.36%

train

765,884.98

20,551.25

2.68%

train

1,074,273.06

37,838.28

3.52%

train

Low

762,219.86

18,928.46

2.48%

train

Local

Low

964,410.00

19,931.14

2.07%

train

Remote

Low

911,404.26

23,595.24

2.59%

test

22,578.66

Good

1,016.59

4.31%

Remote

High

1,930,468.28

57,270.56

2.97%

train

Remote

High

981,611.00

31,895.24

3.25%

train

Local

Low

1,126,200.40

21,254.80

1.89%

test

22,151.95

Good

-897.15

-4.22%

Local

High

708,383.15

17,619.63

2.49%

train

Local

Low

852,403.45

22,680.62

2.66%

train

Remote

Low

816,178.39

24,349.32

2.98%

train

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LOCATION

COMPLEXITY

BUDGET

DURATION

RELEVANT STAKEHOLDER GROUPS

Remote

Low

1,151,686.39

31,270.03

2.72%

train

Remote

High

624,255.72

24,746.14

3.96%

train

Remote

High

531,076.00

14,516.08

2.73%

train

Local

Low

1,219,803.85

27,900.59

2.29%

train

Remote

Low

1,359,202.77

41,602.27

3.06%

train

Local

Low

693,228.75

18,178.00

2.62%

train

Local

High

801,510.16

16,755.38

2.09%

train

Local

High

511,096.39

13,196.77

2.58%

train

Local

Low

590,242.71

17,838.45

3.02%

train

Remote

Low

1,116,386.68

34,170.19

3.06%

train

Remote

Medium

1,123,846.83

30,897.64

2.75%

test

Remote

High

547,802.19

17,799.60

3.25%

train

Remote

High

966,086.13

37,892.04

3.92%

train

Local

Medium

1,273,716.73

25,667.32

2.02%

train

Local

High

778,993.21

22,331.14

2.87%

test

Local

Low

894,732.93

19,276.00

2.15%

train

Remote

Low

1,171,008.92

33,425.11

2.85%

train

Remote

Low

551,582.86

15,374.28

2.79%

train

Remote

High

546,599.66

15,574.95

2.85%

train

Remote

High

1,789,071.54

49,923.62

2.79%

train

Local

Low

1,323,310.37

19,723.63

1.49%

train

Remote

Low

845,707.18

21,706.48

2.57%

train

Local

Medium

782,095.09

20,235.16

2.59%

train

Local

Medium

512,318.31

14,098.24

2.75%

train

Remote

High

1,056,680.60

32,221.10

3.05%

train

Local

Low

1,399,151.60

47,260.23

3.38%

train

Remote

Low

1,629,835.05

46,521.78

2.85%

train

Local

Low

1,747,728.47

42,896.00

2.45%

train

Local

High

584,824.62

15,140.46

2.59%

train

Remote

High

1,522,611.48

38,460.04

2.53%

train

Remote

Low

1,234,685.15

35,242.68

2.85%

train

Local

Low

982,920.06

19,533.59

1.99%

train

Local

Medium

1,788,200.40

63,977.84

3.58%

train

Local

High

1,082,133.01

29,080.18

2.69%

Remote

High

1,035,386.38

30,371.33

Remote

Medium

1,264,034.73

42,736.41

Remote

High

1,367,409.84

Remote

Low

1,002,553.31

Local

Low

1,420,828.51

Local

Low

1,709,337.52

Local

High

609,335.11

Remote

High

833,883.05

PM COST

% PM COST

TAG USED

TEST

GOOD OR BAD

RESIDUAL

31,578.29

Good

-680.64

-2.20%

22,044.71

Good

286.43

1.28%

test

28,236.53

Good

843.65

2.90%

2.93%

test

31,220.71

Good

-849.38

-2.80%

3.38%

train

45,868.20

3.35%

train

37,737.13

3.76%

train

29,189.30

2.05%

train

48,241.30

2.82%

train

14,566.01

2.39%

test

14,651.91

Good

-85.90

-0.59%

23,441.38

2.81%

train

175

176

Applying Neural Networks and Analogous Estimating to Determine the Project Budget

LOCATION

COMPLEXITY

BUDGET

DURATION

RELEVANT STAKEHOLDER GROUPS

Remote

Low

1,297,801.29

29,191.12

2.25%

train

Remote

Low

1,119,369.76

35,606.62

3.18%

train

Local

Low

925,628.02

22,718.48

2.45%

train

Local

High

667,414.59

15,276.38

2.29%

train

Remote

High

1,722,870.56

59,514.60

3.45%

train

Local

Low

951,195.05

21,395.00

2.25%

test

Local

Low

1,363,830.91

34,802.94

2.55%

train

Remote

Medium

1,151,990.74

30,975.75

2.69%

train

Local

High

1,125,818.31

26,018.91

2.31%

train

Remote

High

1,279,302.89

31,860.73

2.49%

train

Local

Medium

555,745.83

14,912.51

2.68%

test

Local

High

1,437,619.16

49,198.52

3.42%

train

Remote

Low

512,839.97

16,012.00

3.12%

train

Remote

Low

1,108,388.88

29,392.83

2.65%

train

Local

Low

1,491,757.71

44,468.59

2.98%

train

Local

High

573,367.88

14,525.32

2.53%

train

Local

High

577,732.27

14,388.28

2.49%

test

Remote

Low

1,340,923.44

25,626.54

1.91%

train

Local

Medium

1,218,034.19

19,623.88

1.61%

train

Remote

Medium

982,929.62

35,603.90

3.62%

train

Remote

Low

918,511.12

28,677.96

3.12%

train

Remote

High

799,134.56

19,022.54

2.38%

train

Local

Medium

1,699,228.84

54,051.66

3.18%

train

Local

Medium

557,737.83

14,315.27

2.57%

train

Local

Medium

1,308,696.78

27,918.86

2.13%

train

Local

Low

823,502.63

16,365.48

1.99%

train

Local

Low

1,277,239.09

28,292.78

2.22%

train

Remote

High

951,405.82

31,974.70

3.36%

train

Remote

Low

615,510.45

18,800.06

3.05%

train

Local

Low

852,551.98

15,251.21

1.79%

test

Local

Low

514,229.05

11,905.18

2.32%

train

Local

Medium

831,541.04

22,072.31

2.65%

train

Local

Medium

1,035,118.41

24,711.40

2.39%

train

Remote

High

813,527.00

30,778.44

3.78%

Local

Low

534,936.99

10,883.66

Remote

High

839,992.75

22,130.18

Local

High

968,941.49

Local

High

1,455,430.69

Remote

Low

553,402.62

Remote

Low

1,550,217.54

Remote

Medium

1,571,769.84

Local

Low

958,266.50

PM COST

% PM COST

TAG USED

TEST

GOOD OR BAD

RESIDUAL

20,650.89

Good

744.11

3.48%

17,164.08

Good

-2,251.57

-15.10%

13,498.17

Good

890.11

6.19%

16,513.71

Good

-1,262.50

-8.28%

test

31,776.14

Good

-997.71

-3.24%

2.03%

test

11,947.44

Good

-1,063.79

-9.77%

2.63%

test

21,463.60

Good

666.58

3.01%

27,776.32

2.87%

train

32,736.64

2.25%

train

15,864.21

2.87%

train

46,851.02

3.02%

train

41,913.86

2.67%

train

21,918.44

2.29%

train

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LOCATION

COMPLEXITY

BUDGET

DURATION

RELEVANT STAKEHOLDER GROUPS

Remote

High

1,203,129.39

53,873.46

4.48%

train

Local

Medium

512,774.70

13,611.02

2.65%

train

Remote

Low

1,572,775.22

34,426.30

2.19%

train

Local

High

928,720.44

23,100.08

2.49%

Local

Low

1,286,047.40

41,977.91

Local

Medium

897,200.07

Local

Medium

506,773.64

Remote

Medium

1,561,191.51

Local

High

903,316.02

Remote

Medium

580,211.77

Remote

Low

Local

Low

Local

PM COST

% PM COST

TAG USED

TEST

GOOD OR BAD

RESIDUAL

test

22,313.20

Good

786.88

3.41%

3.26%

test

43,382.91

Good

-1,405.01

-3.35%

19,624.47

2.19%

train

9,073.66

1.79%

test

10,009.00

Good

-935.33

-10.31%

54,381.50

3.48%

test

53,120.34

Good

1,261.17

2.32%

25,895.06

2.87%

train

16,052.53

2.77%

test

17,675.50

Good

-1,622.97

-10.11%

595,520.47

20,975.55

3.52%

train

1,001,793.43

23,586.08

2.35%

test

21,799.23

Good

1,786.85

7.58%

Low

655,421.89

18,898.00

2.88%

train

Local

High

897,256.60

25,721.36

2.87%

train

Remote

Low

604,357.31

16,041.98

2.65%

train

Remote

High

868,980.86

30,864.91

3.55%

train

Local

Medium

1,054,258.00

27,235.00

2.58%

train

Local

Low

504,023.79

10,858.62

2.15%

train

Remote

Medium

984,726.14

23,456.68

2.38%

train

Remote

Low

914,671.35

23,476.56

2.57%

test

23,342.33

Good

134.24

0.57%

Local

Low

816,984.05

11,520.30

1.41%

train

Local

Medium

1,102,518.04

34,423.06

3.12%

test

32,270.39

Good

2,152.67

6.25%

Local

Medium

1,568,418.96

36,886.89

2.35%

train

Local

Low

866,386.50

16,760.84

1.93%

train

Remote

Low

945,814.91

25,105.58

2.65%

train

Remote

Medium

1,352,496.54

35,615.74

2.63%

train

Remote

Low

1,007,543.31

24,053.10

2.39%

train

Local

Medium

1,585,230.00

43,764.78

2.76%

train

Remote

High

599,627.37

15,533.20

2.59%

test

15,007.94

Good

525.27

3.38%

Local

Medium

1,063,937.52

16,066.53

1.51%

test

19,098.46

Good

-3,031.93

-18.87%

Remote

Low

1,316,509.72

36,345.99

2.76%

train

Local

Low

819,992.37

14,152.46

1.73%

train

Remote

Medium

1,059,271.62

34,132.09

3.22%

test

34,598.62

Good

-466.54

-1.37%

Remote

High

661,598.27

15,388.29

2.33%

train

Local

Low

556,860.84

10,664.73

1.92%

train

Remote

High

1,629,259.58

53,222.48

3.27%

train

Local

Medium

560,885.36

12,533.36

2.23%

train

Remote

Low

1,128,949.92

19,484.84

1.73%

train

Remote

Low

1,140,022.19

32,870.64

2.88%

train

Local

Medium

1,277,998.06

30,023.69

2.35%

test

29,794.51

Good

229.18

0.76%

Local

Low

1,370,381.07

43,360.26

3.16%

train

Remote

Medium

622,821.80

17,231.40

2.77%

train

177

178

Applying Neural Networks and Analogous Estimating to Determine the Project Budget

LOCATION

COMPLEXITY

BUDGET

DURATION

RELEVANT STAKEHOLDER GROUPS

Local

Low

606,852.57

12,634.98

2.08%

train

Remote

Low

951,616.00

38,804.79

4.08%

train

Remote

Medium

617,490.46

12,673.26

2.05%

train

Local

Low

704,413.02

9,723.66

1.38%

test

Remote

Low

580,202.08

11,265.59

1.94%

train

Local

Low

1,283,482.92

23,245.30

1.81%

train

Remote

Low

1,615,066.28

41,172.49

2.55%

test

Remote

Medium

1,221,684.39

28,505.97

2.33%

train

Local

Low

1,554,072.32

33,992.25

2.19%

Local

Medium

1,147,660.40

23,955.13

Remote

Low

1,226,103.02

Remote

Low

514,184.61

Remote

Medium

1,559,320.98

Remote

Medium

904,655.73

Remote

Low

1,304,661.29

Remote

Medium

Remote

High

Remote

PM COST

% PM COST

TAG USED

TEST

GOOD OR BAD

RESIDUAL

9,729.60

Good

-5.94

-0.06%

38,529.89

Good

2,642.60

6.42%

test

32,400.12

Good

1,592.13

4.68%

2.09%

test

25,670.99

Good

-1,715.86

-7.16%

24,945.90

2.03%

train

12,418.34

2.42%

train

39,219.29

2.52%

train

26,704.10

2.95%

train

34,118.87

2.62%

test

32,420.45

Good

1,698.42

4.98%

573,409.51

19,973.76

3.48%

train

545,633.58

16,299.72

2.99%

train

High

503,090.27

13,634.25

2.71%

train

Local

Medium

525,195.05

11,087.45

2.11%

train

Local

Low

894,012.12

24,681.75

2.76%

train

Local

Medium

833,563.20

17,631.12

2.12%

train

Local

Low

535,711.70

11,866.83

2.22%

train

Local

Medium

1,325,009.13

29,002.98

2.19%

train

Remote

Medium

590,318.95

17,799.01

3.02%

train

Local

High

1,770,395.16

40,987.33

2.32%

train

Local

High

1,405,512.56

40,525.61

2.88%

train

Local

Medium

1,286,163.78

31,649.72

2.46%

train

Local

Low

1,103,463.05

27,831.79

2.52%

train

Remote

Medium

885,202.32

27,146.20

3.07%

test

25,689.17

Good

1,457.04

5.37%

Local

Low

1,220,977.54

22,399.66

1.83%

train

Remote

High

679,641.98

16,822.97

2.48%

train

Local

Medium

1,158,479.42

29,734.31

2.57%

train

Remote

High

1,297,008.10

43,882.11

3.38%

train

Local

Medium

595,980.69

9,984.28

1.68%

train

Remote

Low

812,827.47

21,575.58

2.65%

train

Remote

Low

800,720.74

22,953.99

2.87%

train

Local

Low

1,360,528.32

22,792.51

1.68%

test

22,768.65

Good

23.86

0.10%

Remote

Medium

622,078.94

15,137.25

2.43%

train

Local

Medium

1,048,802.19

24,281.36

2.32%

test

25,044.15

Good

-762.79

-3.14%

Local

High

964,150.49

27,638.98

2.87%

train

Remote

Low

1,270,776.17

34,149.59

2.69%

train

Local

Medium

1,236,912.47

26,990.06

2.18%

test

27,084.03

Good

-93.96

-0.35%

ricardo-vargas.com

LOCATION

COMPLEXITY

BUDGET

DURATION

RELEVANT STAKEHOLDER GROUPS

Local

Medium

828,706.86

24,723.09

2.98%

train

Remote

High

946,925.63

34,299.75

3.62%

train

Local

Medium

826,666.64

22,044.44

2.67%

train

Local

Medium

744,008.05

17,968.92

2.42%

train

Local

Medium

1,335,476.56

31,408.43

2.35%

train

Remote

Low

540,059.74

19,862.20

3.68%

train

Remote

Medium

1,937,816.91

53,374.96

2.75%

train

Remote

Medium

769,785.60

23,561.48

3.06%

train

Local

Medium

1,094,632.16

27,000.93

2.47%

train

Remote

High

1,280,061.70

36,035.68

2.82%

train

Remote

High

896,347.09

23,537.41

2.63%

train

Local

Low

704,793.42

17,502.37

2.48%

train

Remote

High

849,940.50

27,940.11

3.29%

train

Local

Low

1,325,031.76

23,724.38

1.79%

train

Remote

Medium

1,493,825.11

44,625.06

2.99%

train

Remote

Low

640,849.31

12,240.87

1.91%

test

Remote

Medium

536,908.21

15,325.43

2.85%

train

Remote

Low

1,167,617.40

37,169.15

3.18%

train

Local

High

1,192,348.18

24,825.30

2.08%

train

Remote

Low

531,703.85

16,600.98

3.12%

test

Remote

Medium

1,510,277.92

41,598.88

2.75%

train

Local

Medium

1,438,409.49

20,891.19

1.45%

test

Remote

Medium

866,217.66

29,977.92

3.46%

train

Local

High

1,830,390.71

37,189.23

2.03%

train

Remote

Medium

993,322.40

41,498.80

4.18%

train

Local

Low

948,143.98

24,862.44

2.62%

train

Local

Medium

1,379,684.09

32,937.22

2.39%

train

Remote

Low

1,120,685.21

40,131.20

3.58%

test

Local

Medium

1,163,330.12

29,715.94

2.55%

train

Local

Medium

1,028,805.86

21,474.28

2.09%

train

Local

Medium

698,116.99

16,436.37

2.35%

train

Local

Medium

520,721.77

11,389.76

2.19%

train

Remote

Medium

1,761,126.50

54,007.88

3.07%

train

Remote

High

1,657,808.50

44,350.83

2.68%

test

Local

Medium

1,458,640.17

31,117.66

2.13%

train

Remote

High

1,143,918.74

34,939.69

3.05%

train

Remote

Low

539,042.04

14,494.24

2.69%

test

Local

Medium

809,443.86

26,082.08

3.22%

train

Local

High

1,767,884.50

37,714.87

2.13%

train

Local

High

771,280.77

19,184.08

2.49%

train

Remote

Low

625,360.95

16,147.14

2.58%

train

Remote

Low

599,119.11

21,102.31

3.52%

train

PM COST

% PM COST

TAG USED

TEST

GOOD OR BAD

RESIDUAL

11,703.26

Good

537.61

4.39%

17,783.95

Good

-1,182.98

-7.13%

19,508.51

Good

1,382.68

6.62%

38,958.02

Good

1,173.18

2.92%

46,338.40

Good

-1,987.57

-4.48%

16,519.13

Good

-2,024.88

-13.97%

179

180

Applying Neural Networks and Analogous Estimating to Determine the Project Budget

LOCATION

COMPLEXITY

BUDGET

DURATION

RELEVANT STAKEHOLDER GROUPS

Local

High

854,967.79

20,648.77

2.42%

train

Local

Low

614,910.44

15,691.60

2.55%

train

Local

High

1,010,812.89

29,145.11

2.88%

train

Local

Low

1,605,359.49

40,490.73

2.52%

train

Local

Low

909,185.58

19,395.96

2.13%

train

Remote

Medium

559,258.89

18,579.82

3.32%

train

Remote

High

575,367.10

19,370.69

3.37%

test

Remote

Medium

565,256.88

14,759.49

2.61%

train

Local

Medium

868,794.53

19,692.68

2.27%

test

Local

Medium

513,426.44

10,211.48

1.99%

train

Local

Medium

565,225.63

9,100.70

1.61%

train

Remote

Low

1,750,698.16

57,481.26

3.28%

train

Remote

Low

777,901.92

21,406.71

2.75%

train

Local

Low

1,485,078.05

24,380.03

1.64%

test

Local

High

785,613.65

14,772.62

1.88%

train

Local

Low

706,311.75

15,216.68

2.15%

train

Remote

Low

739,540.41

21,352.76

2.89%

train

Remote

High

1,342,549.88

39,828.98

2.97%

train

Local

High

1,201,962.84

28,694.48

2.39%

train

Remote

High

735,242.88

24,709.93

3.36%

test

Remote

High

1,712,608.43

57,943.25

3.38%

train

Local

High

1,050,306.81

33,434.77

3.18%

train

Remote

Medium

538,418.45

13,947.60

2.59%

train

Local

Medium

606,669.12

12,386.16

2.04%

train

Local

Low

888,601.69

18,364.43

2.07%

train

Local

High

602,631.37

13,458.77

2.23%

train

Local

Medium

528,769.94

11,582.58

2.19%

train

Remote

Medium

733,381.20

21,186.57

2.89%

test

Remote

Low

615,606.68

14,622.31

2.38%

train

Local

Low

1,038,350.37

14,652.28

1.41%

train

Remote

Low

1,008,605.83

30,482.31

3.02%

train

Remote

Medium

1,537,920.89

42,281.68

2.75%

train

Remote

Medium

1,246,255.59

45,874.07

3.68%

train

Remote

Medium

563,905.89

16,845.57

2.99%

train

Local

High

1,033,174.25

30,497.77

2.95%

test

Remote

Low

658,752.67

16,907.99

2.57%

train

Remote

Medium

829,602.26

22,952.33

2.77%

train

Local

Medium

1,771,365.49

52,845.74

2.98%

train

Remote

Medium

511,380.29

14,907.51

2.92%

test

Local

Medium

1,543,534.66

33,443.25

2.17%

train

Remote

Medium

629,687.82

14,700.49

2.33%

train

Remote

Medium

895,421.00

24,474.84

2.73%

train

PM COST

% PM COST

TAG USED

TEST

GOOD OR BAD

RESIDUAL

18,927.68

Good

443.01

2.29%

19,750.18

Good

-57.50

-0.29%

25,014.55

Good

-634.52

-2.60%

24,688.38

Good

21.55

0.09%

19,471.21

Good

1,715.35

8.10%

28,677.64

Good

1,820.13

5.97%

17,273.01

Good

-2,365.50

-15.87%

ricardo-vargas.com

LOCATION

COMPLEXITY

BUDGET

DURATION

RELEVANT STAKEHOLDER GROUPS

Remote

Low

556,260.00

19,919.41

3.58%

test

Local

Medium

1,817,837.94

31,531.63

1.73%

train

Local

Medium

1,622,698.23

40,026.56

2.47%

test

Remote

Medium

846,257.51

19,756.46

2.33%

train

Local

Medium

1,611,292.85

41,625.07

2.58%

train

Remote

Low

1,262,421.26

38,527.23

3.05%

test

Remote

Medium

612,061.28

12,399.91

2.03%

train

Remote

High

1,129,246.13

46,048.15

4.08%

train

Local

Low

537,807.95

10,935.43

2.03%

train

Local

Low

955,684.84

16,075.11

1.68%

train

Remote

High

1,134,051.12

33,265.50

2.93%

train

Remote

Medium

600,240.51

18,933.90

3.15%

train

Remote

Low

995,130.04

21,241.73

2.13%

train

Remote

Low

1,141,834.26

29,166.85

2.55%

train

Remote

Medium

950,139.68

30,246.11

3.18%

train

Remote

High

1,236,433.40

50,419.01

4.08%

train

Local

Low

1,149,263.50

21,726.55

1.89%

test

Local

High

697,640.20

17,352.42

2.49%

train

Remote

Medium

584,742.74

13,928.87

2.38%

train

Local

High

1,439,365.41

43,500.82

3.02%

train

Local

High

644,115.39

14,507.93

2.25%

train

Remote

Low

875,107.31

23,228.73

2.65%

train

Local

Low

1,471,608.90

26,652.47

1.81%

train

Local

Medium

630,681.97

16,317.64

2.59%

train

Local

Low

1,134,830.22

28,622.94

2.52%

test

Local

Low

1,515,009.77

25,586.83

1.69%

train

Local

Medium

503,379.10

11,521.79

2.29%

train

Local

Medium

1,289,329.63

44,663.70

3.46%

train

Remote

Low

540,092.44

13,862.37

2.57%

train

Remote

Medium

1,506,018.23

33,653.00

2.23%

test

Local

Low

1,223,357.49

24,059.36

1.97%

train

Remote

Low

1,177,260.33

27,255.36

2.32%

train

Remote

Low

567,631.63

14,569.21

2.57%

train

Remote

Low

1,059,977.22

20,257.34

1.91%

train

Remote

High

1,426,212.38

32,946.95

2.31%

train

Remote

Low

1,078,018.55

23,505.03

2.18%

test

Local

Medium

1,642,148.81

36,936.45

2.25%

train

Remote

Medium

568,875.01

13,280.77

2.33%

train

Local

Medium

1,323,716.42

48,683.35

3.68%

test

Remote

Low

1,242,074.92

28,755.92

2.32%

train

Remote

Low

533,466.84

16,294.14

3.05%

train

Remote

High

1,341,511.76

45,164.23

3.37%

train

PM COST

% PM COST

TAG USED

TEST

GOOD OR BAD

RESIDUAL

20,022.91

Good

-103.50

-0.52%

38,438.01

Good

1,588.54

3.97%

37,941.67

Good

585.56

1.52%

22,898.96

Good

-1,172.40

-5.40%

27,401.45

Good

1,221.49

4.27%

36,646.12

Good

-2,993.12

-8.89%

20,705.97

Good

2,799.07

11.91%

45,392.41

Good

3,290.94

6.76%

181

182

Applying Neural Networks and Analogous Estimating to Determine the Project Budget

LOCATION

COMPLEXITY

BUDGET

DURATION

RELEVANT STAKEHOLDER GROUPS

Remote

Medium

1,190,106.86

35,236.50

2.96%

train

Remote

Medium

1,639,194.71

66,842.72

4.08%

train

Local

High

557,365.65

17,617.13

3.16%

train

Remote

Low

840,319.07

28,757.59

3.42%

test

Local

Low

575,092.91

8,690.29

1.51%

train

Local

Medium

865,197.22

20,370.08

2.35%

train

Local

Low

1,283,649.31

28,077.28

2.19%

train

Local

High

1,127,308.76

30,294.19

2.69%

train

Remote

Low

863,172.40

30,402.85

3.52%

train

Local

Medium

822,039.76

18,815.58

2.29%

train

Remote

Low

562,812.48

19,260.69

3.42%

train

Local

Low

502,502.19

10,496.71

2.09%

test

Remote

Low

518,239.43

11,817.89

2.28%

train

Local

High

1,282,007.44

42,591.14

3.32%

train

Remote

Medium

774,354.55

15,892.71

2.05%

train

Remote

Low

589,499.15

16,431.12

2.79%

test

Remote

Medium

1,682,541.00

40,194.04

2.39%

train

Local

Medium

838,064.15

16,668.16

1.99%

test

Remote

Medium

1,197,097.75

38,573.15

3.22%

train

Local

Low

673,022.37

17,648.14

2.62%

train

Local

Medium

989,563.79

32,467.12

3.28%

train

Local

Low

1,314,990.27

20,803.82

1.58%

test

Local

High

1,768,637.41

39,088.67

2.21%

train

Remote

Medium

902,133.76

18,767.92

2.08%

train

Remote

Medium

768,791.62

22,966.12

2.99%

train

Local

Medium

834,143.17

18,245.23

2.19%

train

Local

Low

1,721,279.85

33,851.84

1.97%

train

Remote

Medium

649,359.38

16,821.50

2.59%

test

Remote

Medium

1,292,141.59

32,447.11

2.51%

train

Local

Medium

1,162,828.90

18,317.68

1.58%

Remote

Low

522,425.32

11,957.74

2.29%

Remote

Low

1,259,321.65

38,432.63

3.05%

train

Local

High

753,129.64

14,161.79

1.88%

test

Local

High

1,591,469.31

58,313.07

3.66%

train

Local

High

1,815,026.04

62,114.22

3.42%

train

Remote

Medium

539,535.55

18,793.82

3.48%

test

Remote

Medium

1,012,917.15

29,990.29

2.96%

train

Local

Low

1,191,074.21

20,115.92

1.69%

train

Remote

Medium

1,242,927.66

29,001.65

2.33%

train

Local

Low

866,382.17

14,572.99

1.68%

train

Remote

High

1,809,778.83

66,617.10

3.68%

train

Local

Low

1,200,895.52

28,273.72

2.35%

train

PM COST

% PM COST

TAG USED

TEST

GOOD OR BAD

RESIDUAL

29,966.92

Good

-1,209.33

-4.21%

11,330.66

Good

-833.95

-7.94%

16,242.83

Good

188.28

1.15%

18,249.56

Good

-1,581.39

-9.49%

21,387.48

Good

-583.66

-2.81%

14,796.02

Good

2,025.48

12.04%

test

19,475.00

Good

-1,157.31

-6.32%

test

13,711.25

Good

-1,753.51

-14.66%

14,469.25

Good

-307.46

-2.17%

19,527.43

Good

-733.61

-3.90%

ricardo-vargas.com

LOCATION

COMPLEXITY

BUDGET

DURATION

RELEVANT STAKEHOLDER GROUPS

Remote

High

664,897.90

19,862.51

2.99%

train

Remote

Low

838,060.77

22,245.37

2.65%

test

Local

High

708,604.12

22,124.20

3.12%

train

Remote

High

721,495.49

24,439.23

3.39%

test

Local

Medium

1,455,977.09

39,634.93

2.72%

train

Local

Low

1,111,810.02

17,061.48

1.53%

test

Remote

Low

1,587,492.55

52,122.67

3.28%

train

Local

Low

879,426.14

18,174.81

2.07%

train

Remote

Low

814,569.24

18,644.58

2.29%

train

Remote

Low

550,677.71

11,285.73

2.05%

train

Remote

Low

654,244.47

13,676.82

2.09%

train

Local

Medium

1,142,844.15

30,335.49

2.65%

train

Local

Medium

873,476.34

19,105.56

2.19%

train

Remote

Medium

554,435.96

20,315.10

3.66%

train

Local

Medium

825,566.09

25,776.01

3.12%

train

Remote

Low

633,649.59

13,826.56

2.18%

train

Remote

High

587,307.33

19,772.68

3.37%

train

Remote

Low

641,383.96

11,880.87

1.85%

train

Remote

Medium

1,481,728.02

43,870.77

2.96%

test

Local

Medium

1,007,413.35

18,943.32

1.88%

train

Local

Medium

643,538.40

14,685.88

2.28%

train

Remote

Low

1,827,161.32

52,683.15

2.88%

train

Remote

Low

964,830.02

33,018.63

3.42%

train

Local

Low

632,906.70

12,121.12

1.92%

train

Local

Low

526,027.21

7,261.24

1.38%

train

Remote

Medium

731,439.19

20,236.48

2.77%

train

Local

Low

886,535.29

18,321.73

2.07%

train

Remote

Medium

1,055,346.94

31,308.63

2.97%

test

Local

Low

1,248,941.20

16,362.39

1.31%

train

Local

High

618,518.54

15,384.42

2.49%

train

Local

Low

530,522.61

13,911.48

2.62%

train

Local

Low

1,009,146.22

21,344.97

2.12%

train

Local

High

1,021,674.63

20,786.66

2.03%

train

Remote

Low

1,121,376.46

30,447.07

2.72%

train

Local

Low

638,464.57

11,334.46

1.78%

train

Remote

Medium

1,256,833.06

24,890.22

1.98%

train

Remote

High

546,298.17

15,357.05

2.81%

train

Local

Medium

987,396.42

27,866.52

2.82%

test

Remote

Medium

1,046,307.06

31,256.35

2.99%

train

Local

High

694,023.76

16,305.57

2.35%

train

Local

Medium

1,234,868.04

26,359.10

2.13%

train

Local

Medium

632,587.31

12,581.46

1.99%

train

PM COST

% PM COST

TAG USED

TEST

GOOD OR BAD

RESIDUAL

22,111.30

Good

134.06

0.60%

22,337.15

Good

2,102.08

8.60%

17,956.39

Good

-894.91

-5.25%

42,949.19

Good

921.58

2.10%

31,889.28

Good

-580.66

-1.85%

26,778.88

Good

1,087.64

3.90%

183

184

Applying Neural Networks and Analogous Estimating to Determine the Project Budget

LOCATION

COMPLEXITY

BUDGET

DURATION

RELEVANT STAKEHOLDER GROUPS

Remote

High

1,366,884.27

43,566.72

3.19%

train

Remote

Medium

869,516.65

33,717.92

3.88%

test

Remote

High

924,774.23

31,134.07

3.37%

train

Remote

High

1,729,408.45

49,933.24

2.89%

train

Local

Low

1,633,982.66

25,365.64

1.55%

train

Local

Medium

814,029.10

25,415.80

3.12%

train

Local

High

994,502.93

24,736.29

2.49%

test

Remote

Low

573,443.71

13,471.77

2.35%

train

Local

Medium

586,644.29

12,384.71

2.11%

test

Local

Low

1,194,191.56

23,485.77

1.97%

train

Local

Low

640,851.46

11,852.07

1.85%

train

Local

High

653,026.72

20,388.95

3.12%

train

Remote

Low

1,123,457.64

21,470.52

1.91%

train

Remote

Medium

807,182.36

20,577.30

2.55%

train

Remote

Low

1,367,692.12

41,862.11

3.06%

train

Local

Low

573,685.93

11,098.34

1.93%

train

Local

Medium

567,217.09

12,856.92

2.27%

train

Remote

Medium

1,188,181.44

34,306.38

2.89%

train

Local

Medium

552,218.25

18,652.71

3.38%

train

Local

Medium

575,508.83

15,851.73

2.75%

train

Local

Low

1,022,032.17

24,358.43

2.38%

test

Remote

High

1,245,112.01

40,600.42

3.26%

train

Local

Medium

764,762.97

11,872.03

1.55%

test

Remote

High

534,546.78

20,223.69

3.78%

train

Local

Medium

934,959.92

25,752.40

2.75%

train

Local

High

1,680,626.26

53,120.97

3.16%

train

Local

Medium

1,072,671.58

23,479.59

2.19%

train

Remote

High

1,556,942.09

50,768.52

3.26%

test

Local

Low

1,368,257.92

36,714.92

2.68%

train

Remote

Low

551,086.24

14,144.55

2.57%

train

Local

Medium

860,268.78

20,072.94

2.33%

train

Local

High

584,003.61

19,174.79

3.28%

train

Remote

Low

625,248.12

17,205.90

2.75%

train

Local

Low

1,689,416.73

28,907.80

1.71%

test

Remote

Medium

1,069,349.15

38,734.20

3.62%

train

Remote

Low

627,751.93

21,483.07

3.42%

test

Local

High

1,268,675.38

35,100.02

2.77%

train

Local

High

1,547,340.00

37,671.04

2.43%

train

Local

Medium

1,294,799.07

31,938.38

2.47%

train

Remote

High

1,185,245.00

31,310.22

2.64%

test

Local

Low

524,430.06

7,394.99

1.41%

train

Local

Medium

663,249.69

14,528.33

2.19%

train

PM COST

% PM COST

TAG USED

TEST

GOOD OR BAD

RESIDUAL

34,888.20

Good

-1,170.28

-3.47%

24,108.59

Good

627.70

2.54%

12,332.61

Good

52.10

0.42%

24,273.38

Good

85.06

0.35%

12,294.49

Good

-422.46

-3.56%

52,653.72

Good

-1,885.20

-3.71%

26,945.85

Good

1,961.94

6.79%

20,197.79

Good

1,285.28

5.98%

28,602.72

Good

2,707.50

8.65%

ricardo-vargas.com

LOCATION

COMPLEXITY

BUDGET

DURATION

RELEVANT STAKEHOLDER GROUPS

Local

Medium

1,653,133.09

49,961.36

3.02%

train

Local

Medium

528,820.61

13,573.06

2.57%

train

Local

Medium

519,719.13

8,892.97

1.71%

train

Remote

Medium

743,037.32

18,650.99

2.51%

train

Remote

Medium

856,258.79

21,311.33

2.49%

test

Local

High

620,702.06

14,122.64

2.28%

train

Remote

Medium

1,284,054.66

34,526.80

2.69%

test

Remote

Low

1,221,724.86

41,810.14

3.42%

train

Local

Low

1,713,664.43

45,983.33

2.68%

train

Remote

Medium

1,277,241.72

24,598.73

1.93%

test

Remote

Medium

1,376,535.12

44,355.02

3.22%

train

Local

Medium

1,201,960.23

19,364.91

1.61%

train

Local

Medium

1,048,096.64

30,220.12

2.88%

train

Local

Medium

781,372.88

20,966.84

2.68%

train

Local

Low

889,253.80

24,750.90

2.78%

Remote

Low

635,992.33

14,892.82

Local

Low

510,489.36

9,062.56

Local

Medium

880,766.98

Remote

Medium

843,268.82

Local

High

1,296,821.96

Local

Medium

1,484,991.68

Local

Medium

Local

Low

Remote Local

PM COST

% PM COST

TAG USED

TEST

GOOD OR BAD

RESIDUAL

21,521.34

Good

-210.01

-0.99%

33,189.13

Good

1,337.68

3.87%

25,616.68

Good

-1,017.95

-4.14%

test

22,245.27

Good

2,505.62

10.12%

2.34%

test

13,943.56

Good

949.26

6.37%

1.78%

train

24,857.20

2.82%

train

23,330.44

2.77%

test

23,204.29

Good

126.15

0.54%

34,356.38

2.65%

train

48,721.87

3.28%

train

1,273,825.30

24,061.14

1.89%

train

1,066,325.50

20,971.07

1.97%

train

Low

941,441.39

31,852.10

3.38%

train

Low

546,951.49

10,114.64

1.85%

train

Remote

Low

661,069.81

18,950.67

2.87%

train

Local

Medium

568,661.89

14,595.66

2.57%

train

Remote

Low

1,240,392.01

35,764.64

2.88%

train

Remote

Medium

1,474,071.34

33,614.61

2.28%

train

Remote

High

504,448.03

12,486.44

2.48%

train

Remote

Low

869,608.71

23,038.33

2.65%

train

Remote

Medium

1,484,723.62

44,046.80

2.97%

train

Remote

Low

568,098.78

13,720.45

2.42%

train

Remote

High

809,584.87

27,423.08

3.39%

test

28,005.36

Good

-582.28

-2.12%

Remote

Medium

1,283,825.59

40,496.81

3.15%

train

Remote

Low

568,442.98

10,688.96

1.88%

test

11,691.38

Good

-1,002.42

-9.38%

Local

Medium

543,071.16

16,079.17

2.96%

train

Remote

Medium

1,206,922.14

32,184.59

2.67%

train

Local

Low

1,565,873.75

33,654.94

2.15%

train

Remote

Low

686,957.91

18,234.51

2.65%

train

185

MODELLING LINE OF BALANCE SCHEDULES WITH START-FINISH RELATIONSHIPS

Ricardo Viana Vargas United Nations (UNOPS) / Universidade Federal Fluminense Felipe Fernandes Moreira Universidade Federal do Ceará

Accepted for publication at PMI Global Congress 2015 - EMEA London – United Kingdom – 2015 AACE 2015 Annual Meeting Las Vegas – United States – 2015

188

Modelling Line of Balance Schedules With Start-Finish Relationships

Abstract Related Podcasts

♫

Do you really know the meaning of the different tasks relationships? – Part 1 of 2 http://rvarg.as/dh

♫ Do you really know the

meaning of the different tasks relationships? – Part 2 of 2 http://rvarg.as/8k

♫ Understanding the Effort Driven Task in a Project http://rvarg.as/am

♫ Line of Balance - Part 1 of 2 http://rvarg.as/di

♫ Line of Balance - Part 2 of 2 http://rvarg.as/dj

The use of the Line of Balance Scheduling Method (LBSM) has been increasing, especially by construction industry companies in Brazil, Finland and Australia. The method addresses to the particularities of construction projects more effectively than the Critical Path Method does. One drawback of the method is the unavailability of commercial software featuring the fundamentals of LBSM. Studies pointed this as a factor that prevents a more frequent use. Recognizing the potential of the method for managing construction projects, the objective of this paper is to propose methodologies for modelling Line of Balance schedules using CPM software. In order to model the schedule, the paper demonstrates the use of the “Start-Finish” logic relationship and its contributions for two different approaches for the modelling: Network and Linear Scheduling Approach. The first approach demonstrate the design of a CPM network, which simulates the structure and function of a Line of Balance, with the objective of benefiting from CPM calculations. The second approach drops the CPM calculations and focuses on modelling, tuning and balancing individual workflow lines to and from milestones activities.

ricardo-vargas.com

Introduction Part of the planning effort in a project is directed toward determining the sequence of the activities in such a way that the execution goes on in the most efficient way possible. This sequencing is modeled using relationships between activities, called dependencies, which may be “finish-start” (“FS”), “start-start” (“SS”), “finish-finish” (“FF”) and “start-finish” (“SF”). This link establishes a relationship between activities, where one of them is the predecessor (the activity that comes before logically) and the other is the successor (the activity that comes after logically) [10]. The Line of Balance Scheduling Method (LBSM) is a technique absent from the PMBOK® Guide since its first release in 1996 through the current 2013 edition; CPM network scheduling is the most prominently discussed [12,13]. This omission ignores a current trend in the construction industry. Civil construction companies from Brazil, Finland and Australia are satisfactorily using the LBSM [3]. Employing this technique is related to the efforts to incorporate concepts of Lean Construction into company project management systems, more precisely with the Last Planner® production system, developed by Glenn Ballard and Greg Howell, founders of the Lean Construction Institute® (LCI) [1]. The US Navy initially used the concept of Line of Balance as a technique for planning the execution of activities of the industry in 1942 [5]. General Electric later, working for the US Navy, used it not only as a planning tool but also as a controlling tool. In the United Kingdom, the method was adopted by the Nation Building Agency. According to Shaikh, the increasing usage of LBSM (one that receives the most attention among others techniques) comes in recent years, when an increasing demand for cost control and resource optimization has forced schedules (and schedulers) to focus on more than just the critical path. He attributes the limited usage of the technique to the lack of powerful and user-friendly computer applications. The author insists that the most commonly accepted commercial scheduling software packages are based on the CPM using the precedence diagramming method as the network analysis algorithm [12]. Mubarak, writing about Linear Schedule Methods (LSM), recognizes the same phenomenon of the lack of acceptance in the construction industry despite its analytical advantages. The author considers a drawback the fact that there are no major software packages offering LSM capabilities. Mubarak affirms that the LSM is poised to be make a comeback, since many software packages are becoming available [7]. Zack Jr. and Collins reveal a survey from the UK construction industry that only 1% of the respondent companies use the Line of Balance, while 54% use bar charts [13].

189

190

Modelling Line of Balance Schedules With Start-Finish Relationships

The preference for the Line of Balance scheduling method for developing the project schedule is due to the fact that the “unit of production x time” configuration, instead of the usual Gantt chart configuration (“activities x time”), results in better visualization for the link between the flow of work of the different crews [1]. This allows a different perspective for the control of the project activities – with the Line of Balance, the focus of control is the rate of production of the working crews and not the control of individual discrete activities, which is the focus of the Critical Path Method, largely used [5].

Objectives The LBSM offers a new perspective for construction project management and a potential for improving performance. Taking into account the diagnosis of the authors referenced above, the objective of this paper is to propose the modelling of a Line of Balance schedule using an acclaimed CPM based scheduling software, like Microsoft®1 Project. In order to demonstrate this proposal, it is important to study how this method works. In order to model the Line of Balance with CPM based structures, the most uncommon precedence relationship between activities, the “start-finish” relation may be utilized. Kerzner classifies the “SF” relation as the “least common type of precedence chart” [6]. Referring to the construction industry, Mubarak considers the “SF” logic relationships “uncommon” and “almost nonexistent”, recognizing the other three as “useful” and “common” [7]. The PMBOK® Guide (2013) defines the “SF” logical relationship as “the completion of the successor activity depends upon the initiation of the predecessor activity ” [10]. It is only cited to give a complete representation of all possible links, since this is considered a rare relationship. The first and second editions of PMBOK even advise project managers not to use relationships other than the most usual (finish-start), since they may cause “unexpected results”[9]. It is important to note that, when working with “Start-Finish” logical relationships, the predecessor is not the activity that happens chronologically before, while the successor is not the activity that happens chronologically after. The predecessor (the activity that comes before logically) and the successor (the activity that comes after logically) [10]. The main objective of this paper is to propose how to model a Line of Balance schedule, while the secondary objective is to investigate the “unexpected results” of this sort of modelling. 1 Microsoft® is either registered trademark or trademark of Microsoft Corporation in the United States and/or other countries.

ricardo-vargas.com

Line of Balance Scheduling Method Zack Jr. and Collins [13] define a Line of Balance schedule as a graphical display of scheduled units versus actual units over a given set of critical schedule control points on a particular day. Shaikh defines the Line of Balance as a graphical variant of the linear scheduling methods which considers an activity location as a dimension in the planning, thereby allowing the balancing of operations for continuous and efficient use [12]. This definition is similar to the one given by Kenley and Seppänen [5]. The Line of Balance proposes that the planning of the activities should be according to a rate of production, or cycle, meaning the number of production units delivered by a working crew, over a certain time [4]. Such concept is similar to the concept of takt-time from the Toyota Production System, a measure of time between two outputs in a production system [8]. An example showing a comparison of the regular Gantt chart and the Line of Balance for scheduling three tasks repeating itself continuously along four floors is presented as Exhibits 1, 2 & 3. The assumption is that the next task initiates when the working crew ends the task on the preceding production unit.

Exhibit 1 – List of Activities

Exhibit 2 – Schedule Using the Gantt Chart

191

192

Modelling Line of Balance Schedules With Start-Finish Relationships

Exhibit 3 – Schedule Using the LBSM

The rate of production, or cycle, is the slope of each line (Exhibit 2 and 3). For Task 1 the cycle is 0.25 units per unit of time, for Task 2 the cycle is 0.50 units per unit of time and for Task 3 the cycle is 0.33 units per unit of time. The analysis of these cycle times allows the management team to balance the lines to optimize the use of resources and achieve a reduction of the time needed to complete the project. Exhibit 4 shows the result of reducing the amount of resources of Task 2 by half (in other words, reducing its cycle time to 0.25, the same value of Task 1, and increasing the Task 2 duration from two days to four days per floor).

Exhibit 4 – Balancing Lines to Achieve a Schedule Reduction

The line balancing of these two activities allowed the project completion to happen one day earlier with the reduction of the resources used on Task 2 only – the remaining tasks were left untouched. The comparison of the Gantt chart and the Line of Balance also shows a significant reduction in the number of lines displayed on the schedule (from twelve in the Gantt chart to four in the Line of Balance). The large reduction on the example shows the potential to simplify the schedule chart of a real project that contains several repetitive processes like, for example, a building with 100 floors with “n” different kinds of tasks in each floor. The simplification factor is the number of tasks inside each repetitive process (also called production unit). If you have 100 km of road and 20 tasks for each km, by using the Line of Balance, you are reducing the number of lines of your Gantt chart by 20 (Eq. 1).

𝑺𝑺𝑺𝑺𝑺𝑺𝑺𝑺𝑺𝑺𝑺𝑺𝑺𝑺𝑺𝑺𝑺𝑺𝑺𝑺𝑺𝑺𝑺𝑺𝑺𝑺𝑺𝑺 𝑭𝑭𝑭𝑭𝑭𝑭𝑭𝑭𝑭𝑭𝑭𝑭 = 𝑺𝑺𝑺𝑺𝑺𝑺𝑺𝑺𝑺𝑺𝑺𝑺𝑺𝑺𝑺𝑺𝑺𝑺𝑺𝑺𝑺𝑺𝑺𝑺𝑺𝑺𝑺𝑺 𝑭𝑭𝑭𝑭𝑭𝑭𝑭𝑭𝑭𝑭𝑭𝑭 =

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𝐍𝐍𝐍𝐍𝐍𝐍𝐍𝐍𝐍𝐍𝐍𝐍 𝐨𝐨𝐨𝐨 𝐋𝐋𝐋𝐋𝐋𝐋𝐋𝐋𝐋𝐋 𝐢𝐢𝐢𝐢 𝐚𝐚 𝐆𝐆𝐆𝐆𝐆𝐆𝐆𝐆𝐆𝐆 𝐂𝐂𝐂𝐂𝐂𝐂𝐂𝐂𝐂𝐂 𝐍𝐍𝐍𝐍𝐍𝐍𝐍𝐍𝐍𝐍𝐍𝐍 𝐨𝐨𝐨𝐨 𝐋𝐋𝐋𝐋𝐋𝐋𝐋𝐋𝐋𝐋 𝐢𝐢𝐢𝐢 𝐚𝐚 𝐋𝐋𝐋𝐋𝐋𝐋

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EQ 1 – Simplification Factor

= 20

It is important to note that even though the example used cycle of tasks, it is possible to model a Line of Balance with process cycles, deliverables and sub-nets of precedency diagrams [5]. In addition, the example used floors, but it can also be used for houses, apartments, locations of a building floor, linear sections of a highway or pipeline, etc. In the civil construction industry, the repetition of activities is commonly scheduled continuously [5]. Exhibit 6 shows the activities listed in Exhibit 1 modeled without the constraint of continuous repetition.

Exhibit 5 – Line of Balance Without the Continuity of Repetition

On the resulting Line of Balance, the sequence of tasks is finishing on the 21st day (3 days earlier). This happens because there is now a lack of continuity in the Task 2 and Task 3 lines. The new configuration presents a rate of production of 0.250 units/day for Task A, 0.286 units/day for Task B, 0.267 units/day for Task C. As presented earlier, the balancing of the lines result in a shorter overall duration for the project. However, it is important to notice that this is not always a good practice. For example, in a construction project where a skyscraper is the product, or in a smaller scale real estate project, the deliverables cannot be transferred in a “first in-first out” routine. They are transferred to the client in a single batch. Therefore, the 1 measure showed on Exhibit 5 would increase the inventory of completed work. It is possible to see, while examining Exhibit 5 that the first floor would wait 12 days, while the second and third would wait 8 and 4 days respectively, which results in a total of 24 days. The Line of Balance of Exhibit 3 has a total of 18 days,

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Modelling Line of Balance Schedules With Start-Finish Relationships

25% less. Another relevant consequence of breaking the continuity is that resources will remain for a longer amount of time allocated on the project. On Exhibit 5, the resources of Task 2 would be dedicated to this project for 14 days, while at Exhibit 3 the total time is only 8 days (about 43% less). For Task 3, the modeling pattern of Exhibit 6 results in 15 days of its resources, while the modelling of Exhibit 3 results in 12 days (20% less). This would imply a larger human resources cost for the project due to two main reasons:

• Underutilization of resources, since the crew assigned to task 2 would be idle for 6 days in total. In Brazil, the average labor cost for residential buildings is about 50%, according to indexes published by the Brazilian Chamber of Construction Industry [2]. POPESCU, PHAOBUNJONG and OVARARIN inform that construction projects’ labor costs range from 30 to 50% [11];

• Demobilization and remobilization of crews, that is described by KENLEY

& SEPPÄNEN [5] as a source of cost (and mostly schedule) uncertainty in construction projects, especially when the amount of time between demobilization and remobilization is short (one or two weeks). This inefficiency can results in a riskier schedule.

The management team has to study carefully the consequences of breaking the continuity in order to decide what the best option for the project schedule is. The tradeoff between shorter durations and the increase of risk and costs must be taken into consideration. It is relevant to point out that describing the entire potential of the LBSM is not the purpose of this paper. The examples presented here are extremely simplified usages in order to demonstrate a still considerably unknown scheduling method and how it can optimize schedules. Now that we have evaluated how the LBSM functions, we are able to propose two directions for the modelling to occur: the network approach and the linear scheduling approach. The difference between these approaches is the use of the CPM network calculations – the first, Network Approach, accepts CPM, while the second, Linear Schedule Approach, does not.

The Network Approach It is important to observe the situation shown on Exhibit 3, where two sequential tasks show different rates of progress. Task 1 has 4 days of duration and its successor, Task 2, has 2 days, which means that the Task 1 progression has a smaller slope than the Task 2 progression (meaning that it is slower). Modeling the cycle of the continuous repetition of Task 1 can be done by using the standard “FS” (finish-start) relation, since the next task initiates immediately after the working

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crew ends the task on the preceding production unit. To allow the same continuity for Task 2, it is necessary to base the modeling of its cycle at the end of the last repetition of Task 1. Task 1 on the fourth floor already has a “FS” relation with Task 2 on the fourth floor. This way, the start date of Task 2 at the fourth floor is well defined on time. The described sequence is detailed in Exhibit 6.

Exhibit 6 – Network Diagram With the Logical Relationship Between Tasks

Modeling the preceding repetitions of Task 2 on the lower floors using the “FS” relationship is not sufficient to ensure that the work can be performed continuously and efficiently. The only way to ensure this is by using a different logical linkage, one that can transfer the constraint defined by the cycle of repetition of Task 1 “downward” from Task 2 on the fourth floor to Task 2 on the first floor. Therefore, with the definition by the Project Management Institute® (PMI®), the predecessor in this case is Task 2 on the fourth floor, despite the fact that this is currently the final repetition in the schedule; it offers the time constraint for the sequence of tasks. The successor is, then, Task 2 on the third floor – a task that happens earlier than its predecessor does, but has its timing defined by the task that comes next on the time schedule [10]. This relationship between these two tasks is different; the start of the task that happens later in time is connected to the end of the task that happens immediately before. This represents a legitimate “start-finish” relation. The result is that the start of Task 2 on the fourth floor is linked to the end of Task 2 on the third floor that has its start linked to the end of Task 2 on the second floor that has its start linked to the end of Task 2 on the first floor (Exhibit 8). Note that Task 2 on the second floor has two predecessors, a “finish-start” link from Task 1 on the second floor, and a “start-finish” link from Task 2 on the third floor.

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Modelling Line of Balance Schedules With Start-Finish Relationships

Exhibit 7 – SF Relation Between the Repetitions of Task 2

As we continue to model the network diagram, the fourth floor is no longer the time constraint for the cycle of the next set of tasks. From Exhibit 3, it is possible to perceive that the Task 3 progression has a slope that is smaller than the Task 2 progression (in other words, it is slower). To maintain a continuous, uninterrupted progression for Task 3, it must proceed from the end of Task 2 on the first floor “upwards” (from the first until the fourth floor). This sequence can therefore be modeled by using the “FS” relation, connecting the end of Task 3 on the first floor with the start of Task 3 on the second floor, which has its end linked with the start of Task 3 on the third floor. Finally, Task 3 on the third floor has its end linked with the start of Task 3 on the fourth floor.

Task 1 1st Floor

Task 1 2nd Floor Task 2 1st Floor

Task 1 3rd Floor Task 2 2nd Floor Task 3 1st Floor

Task 1 4th Floor Task 2 3rd Floor Task 3 2nd Floor

Task 2 4th Floor

Task 3 4th Floor

Task 3 3rd Floor

Exhibit 8 – Complete Network Diagram for the Example

A peculiarity can be observed when modeling this network diagram using CPM schedule management software in order to identify the critical path. The result displayed by Microsoft® Project states that every single task on the network is part of the critical path. Analyzing the progression of Task 2, it is true to say that every delay on a task will delay the following repetition. Although, we can observe in Exhibit 9 that there will be no delay on the project end date if Task 2 on the second floor has a one-day delay. In the same way, Task 2 on the third floor can absorb a two-day delay and Task 2 on the fourth floor can absorb a three-day delay without affecting the project completion date.

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Exhibit 9 – Total Float for the Repetitions of Task 2

On the PMBOK (9), the critical path is “normally characterized by zero total float”, while the definition of “total float” is “the amount of time that a schedule activity can be delayed or extended from its early start date without delaying the project finish date or violating a schedule constraint”. Mubarak (7), in turn, consider the critical path as a “continuous chain of critical activities from the start to the end of the project”, defining “critical activities” as activities that can suffer no delay without delaying the project as well. The same is present on the work of Kerzner (6): “there is no slack time in any of the events on this (critical) path”. There is no doubt that the path shown by the software is, indeed, the critical path, since the path duration is equal to the project duration (this is also a characteristic of the critical path recognized by the authors cited above). Still, it should be noted that the inclusion of the “SF” relation on network diagrams reveals an “unexpected result”. Although the Microsoft® Project (the software used) display a clear critical path in terms of project duration, there are “hidden” floats on the network. Thus, the downside of this approach is that the CPM calculations regarding floats will not suffice, demanding a manual analysis of the floats present at the schedule and how it can be used There is a difference between modelling this schedule with the “FS” relationship and the constraint “As Late As Possible” – “ALAP”. The Exhibit 10 shows the schedule designed only with “FS” relationships and the “As Late as Possible” constraint.

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Modelling Line of Balance Schedules With Start-Finish Relationships

Task 1 Task 2 Task 3 UND

4th Floor 3rd Floor 2nd Floor 1st Floor Exhibit 10 – Line of Balance resulted from “FS” relation and“FS” “ALAP” Exhibit 10 – Line of Balance Resulted from Relation and “ALAP”

It is possible to perceive that the Line of Balance is similar to the one at the Exhibit 5, in terms of duration, but with only one discontinuous task progression. The risk of schedule and cost related to the discontinuity remains and the resources for Task 2 progression remain assigned for a longer period. In addition, the Line of Balance above has the same inventory of completed work that the one presented on Exhibit 3. It is important to note that the logical structures applied resulted in Line of Balances suitable to different set of contexts. The structure constructed with “SF” relationships will result in continuous lines, not susceptible to any risks related to discontinuity. The other logical structure, with “FS” relationships and “ALAP”, will result in a shorter duration for the project with a higher risk exposure trade-off. In sum, both “SF” and “FS” + “ALAP” structures are valid for modelling a LB with a CPM software. The management team, knowing the particularities of each model, will have to decide which one fits more properly to the project’s context. If the risks associated with task discontinuity are worth taking in order to achieve scheduled duration reduction, the “FS” + “ALAP” structure should by applied. In cases where those risks are not worth taking, the “SF” structure should be preferred. In fact, a larger and more complex schedule may offer situations where both structures are present.

The Linear Schedule Approach This approach uses the CPM software as a graphical tool for designing the lines and controlling the project along its life cycle, focusing only on the fundamentals of the method: the rate of production of each line and the balancing of all of them. The result, instead of a network, is a graph similar to the one presented on the Exhibit 11.

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Exhibit 11 – The Linear Schedule Approach

Exhibit 11 shows each line as the representation of the progression of six repetitive tasks in a construction project. This methodology ignores the creation of a completely connected CPM network, focusing only on the design of the lines and then tuning them to an efficient configuration – meaning adjusting the distance between the lines and further balancing them. This proposition is not at all disconnected from the reality of project management in the construction industry, given that only 14% of respondents work with a fully linked CPM schedule, according to a survey performed in the UK [13]. This utilization is, in truth, a consequence of the definition of Line of Balance per se. Additionally, it benefits from the main idea behind the first methodology, that is to subordinate a sequence of tasks to an imposed time constraint. In the example given, the finishing date of the cycle of Task 1 imposes a fixed date to the end of the cycle of Task 2 (both lines becoming “linked at the top”). This approach focus on subordinating the sequence of repetition to a milestone. On the subject of milestones, a practical example would be finishing a sequence of painting activities for a building by the end of the sixth month of the project, or the completion of the management plan should be by the third month after the project approval. Assuming that milestones in a network diagram are tasks with zero duration and with a fixed start date, the modeling of one of the examples is presented on Exhibit 12.

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Modelling Line of Balance Schedules With Start-Finish Relationships

Exhibit 12 – Milestones and SF Relation

Exhibit 12 shows that Milestone 1 subordinates the end of the workflow of the painting activities. This way, the continuous use of SF relationships between the tasks’ repetitions moving “downwards” will result in the necessary start date for the workflow. Equally, the sequencing may take its start on a milestone representing the beginning of the painting tasks, when the sequence is modelled “upwards” with the usual “FS” relationship. The example of the following is presented on the next Exhibit, where a sequence of sections of earthwork is subordinated to a milestone.

Exhibit 13 – Milestones and FS Relation

If the management team decides to schedule a non-continuous task progression, after considering the uncertainties and negative consequences, the line can be modelled using lags or leads. If the management team was to model the Earthwork task progression with a two-day lag between each section, it could be done like in the Exhibit 14.

Exhibit 14 – Non-continuous Task Progression

Thus, a milestones chart, defined by the experience and lessons learned from previous projects or with expert judgment, for instance, can be used to initiate the process. The proposed methodology deals with either starting dates mile-

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stones and delivery dates milestones. At this approach, the schedule control’s main goal is to prevent the clash between lines. The clash represents a rupture on the project workflow. Despite the fact that this modelling rejects the assignment of predecessors, the hard-logic relationships among the tasks remains. Accordingly, if a line “invades” another, one of the work crews will have to become idle until the situation is remediated or will have to be transferred. This can happen for either a line that is progressing with a higher speed than anticipated or a line that is progressing slower. Without the CPM calculations, the control is done, in other words, only by the flow of work of the construction project as a whole. The clash is a consequence of an unbalanced execution, with task progressions going off track. Other consequence of lacking the CPM calculation is, evidently, that the management team experiences a paradigm shift. The CPM method delivers a clear priority for team: the critical path. The workflow perspective assigns to every single line the same rank of priority. The focus of the management team will derive from the information gathered from the execution.

Exhibit 15 – Lines Clashing

Conclusion This paper proposed the utilization of CPM software (as seen, the most largely used) as a tool for the Line of Balance Scheduling Method. Inherently, the CPM and the LBSM are different from each other. CPM, according to Kenley and Seppänen (5), focuses on how discrete activities connect to each other in order to identify the critical path. LBSM focuses on the perspective of flow of work, when the scheduling planning concerns with the production process of the particular production units (seemingly to designing a “factory”). Still, the scarcity of commercial software that covers its fundamentals is pointed as a barrier to its implementation and the CPM commercial software may supply a preliminary solution.

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Modelling Line of Balance Schedules With Start-Finish Relationships

On the subject of the model, the paper proposed that the use of the “SF” relation could be of value for the modelling. The LBSM, its behavior and its peculiarities were discussed first and then two approaches for the modelling were proposed: the Network Approach and the Linear Scheduling Approach. Both were discussed and exemplified. The first approach allowed the Line of Balance to be modelled as a CPM network. The continuous progression of tasks, as demonstrated, can be represented with the use of the “SF” relationship when the lines become “linked at the top”. On the other hand, this usage implies on some “unexpected results” regarding the critical path, demanding further investigation from the management team for the situation of floats on the lines. The lines can be modelled with “FS” relationships and the “As Late as Possible” constraint as well, but the result does not guarantee the continuity of the task repetitions, exposing the project to risks related to work discontinuity. On the other hand, the resulting LB presented a shorter duration than the one structured with “SF” relationships. Both structures generated consistent results that may be valuable for different project contexts and even can be combined for more complex project schedules. Thus, it is the management team’s decision which structure is more suitable for the project. The second approach behavior accordingly with the definition of LBSM. The CPM calculation routine were dropped completely and the CPM software was used as a graphical tool for designing the lines that represent the task progressions and their control over the project life cycle. The “SF” relation employed a mean to subordinate the tasks to a delivery milestone, while the common “FS” relation was used to build the sequence from a start milestone. Connecting the task sequences to a milestone allows the scheduler to move them on the time scale with the goal of tuning the schedule and further balancing.

Bibliography NO.

DESCRIPTION

Bernardes, M, M, S, 2011 1

Planejamento e Controle da Produção Para Empresas de Construção Civil First Edition Pages(s)190 LTC, Rio de Janeiro

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NO.

DESCRIPTION

CBIC 2014, 24 January. 2

CUB Médio Brasil - Custo Unitário Básico de Construção por m² Retrieved on 14 January of 2015 https://www.dropbox.com/s/ntc7zvdtthq8ocg/CBIC%20-%20%5B2%5D. pdf?dl=0 Henrich, G, and Koskela, L, 2005

Production Management in Construction – Requirements and Methods Second Scottish Conference for Postgraduate Researchers of the Built & Natural Environment-PRoBE Glasgow, Scotland Henrich, G, and Koskela, L, 2006

Evolution of Production Management Methods in Construction Construction in the XXI Century: Local and Global Challenges ARTEC, Rome, Italy Kenley, R, and Seppänen, O, 2010

Location-Based Management System for Construction: Planning, Scheduling and Control First Edition Pages(s) 584 Spon Press, New York. Kerzner, H 2009

Project Management: a systems approach to planning, scheduling, and controlling Tenth Edition Pages(s) 1122 John Wiley & Sons, Inc., New York Mubarak, S. 2010

Construction Project Scheduling and Control Second Edition Pages(s) 456 John Wiley & Sons, Inc, New Jersey

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NO.

DESCRIPTION

Ohno, T, 1997 8

O Sistema Toyota de Produção: Além da produção em larga escala First Edition Pages(s) 149 Bookman, Porto Alegre PMI 1996-2008

A guide to the project management body of knowledge First through Fourth Edition Pages(s) 182-506 Newtown Square, PA: Author PMI 2013

A guide to the project management body of knowledge Fifth Edition Pages(s) 589 Newtown Square, PA: Author Popescu, C, M, Phaobunjong, K, and Ovararin, N, 2003

Estimating Building Costs First Edition Pages(s) 707 Marcel Dekker, Inc., New York Shaikh, G M

2010 Line of Balance (LOB) Scheduling for Projects with Repetitive Work AACE International Transactions Zack Jr, J G and Collins, S A, 2013

Forensic Schedule Analysis – Chapter 2: Delay Analysis on Non CPM Scheduled Projects Cost Engineering, pp 4-14, vol. 55, n. 6/November/December Morgantown, WV

DETERMINING THE MATHEMATICAL ROI OF A PROJECT MANAGEMENT IMPLEMENTATION

Accepted for publication at PMI Global Congress 2013 – North America New Orleans – Louisiana – USA – 2013 PMO Summit Brazil Rio de Janeiro – RJ – Brazil – 2012

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Determining the Mathematical Roi of a Project Management Implementation

Abstract Related Podcasts

♫

Calculating the Return on Investment of the PMO – Part 1/2 http://rvarg.as/2u

♫ Calculating the Return on

Investment of the PMO – Part 2/2 http://rvarg.as/2v

♫ The Cost of Project Management http://rvarg.as/bg

♫ The cost of Project Management and the PMO http://rvarg.as/bj

The objective of this paper is to present, discuss and apply a mathematical model based on the use of Monte Carlo simulation in conjunction with researches on project success/failure rates of projects to develop a 10 step model to calculate the mathematical return on investment (ROI) for the Project Office implementation. The paper aims to provide guidance on how intangible results resulting from the project planning and control can be linked to potential savings in time and cost comparing with projects poorly managed (KWAK & IBBS, 2000). It is not the scope of this paper to demonstrate the positive impact of good project management practices. The main objective is to discuss possible ways of measuring results in order to have a more clear cost benefit analysis regarding the value of a PMO Setup (HUBBARD, 2010). This paper also discusses the main challenges to quantify benefits considering cultural, social and value perception dimensions in order to translate benefits into clear and measurable numbers.

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The Importance of Clear Benefit Measurement Business improvement processes like the Project Management Office implementation are, most of the time, linked to indirect benefits achievement. In the past, program, project, or process success was measured by activity: number of people involved, money spent, days to complete. Little consideration was given to the benefits derived from these activities because they were considered impossible to be clearly measured (PHILIPS & PHILIPS, 2007). The intention of clear measurement of benefits can be based in the following arguments:

• Price/money is a proxy for value. • Measurable outcomes contribute to a better alignment and integration with financial systems performance.

• More tangible results support the identification of critical sources of value. • Promotes communication and makes results quantitatively tangible. Understand the clear impact on project results of the project management processes, tools and existing support and how this structure contributes to better project results became a key driver to understand the value of project management (EIU, 2009)

Model Overview The proposed model is based on 10 (ten) processes that are organized into 6 (six) groups (Exhibit 01). Both the processes and the groups are interrelated in order to produce the needed steps to understand the real costs and benefits brought by the Project Management Implementation.

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Determining the Mathematical Roi of a Project Management Implementation

Exhibit 1 – 10 Processes to calculate the Return on Investment of a Project Management Office.

The processes are defined following the structure proposed by the PMBOK Guide (PMI, 2013) with Inputs, Tools and Techniques and Outputs.

Project Portfolio The Project Portfolio group describes the process that should be in place to understand the scope of what should be managed by a potential Project Management Office (PMO). The intend is to make sure that the potential projects that will be supported by the project management office are identified and the cost, time frame and benefits (value) of these projects are calculated. The Project Portfolio group is divided in the following processes

• Create the portfolio of projects • Calculate financial return of projects in the portfolio • Categorize projects Create the Portfolio of Projects This is process is responsible for the creation of the portfolio of projects. Based in working groups and the support of experts, it aims to create a list of the projects that will be managed by the PMO including some preliminary information like the Project objectives, estimated duration and budget (Exhibit 2).

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Exhibit 2 – Create Portfolio of Projects

The Project Portfolio can be presented in different ways but the most suitable to support the upcoming process is a list with the name of the project, estimated duration and budget (Exhibit 3). If the Project Office will support all kinds of projects at the corporate level, the list of projects can include a very different set of initiatives.

Exhibit 3 – Example of basic project list

The preliminary project information can include all supporting information about the project, including main objectives, outputs, expected benefits and basic scope (Exhibit 4). This preliminary information can be also called Project Brief or Outline Business Case (UK CABINET OFFICE, 2011)

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Determining the Mathematical Roi of a Project Management Implementation

Exhibit 4 – Example of project preliminary information

Calculate Financial Return of Projects in the Portfolio After identifying the potential portfolio of projects to be managed, it becomes important to calculate the benefits in terms of financial results of each project. This is one of the most challenging steps towards the calculation of the ROI of a Project office. Using the preliminary information, all proposed benefits will be measured in order to find clear outcomes (Exhibit 5).

Exhibit 5 – Calculate Financial Return of Projects in the Portfolio

In some cases, this is easily measurable in terms of increase in the production, market place, etc. In other projects, the intangible results must be converted into an estimated final outcome. As an example, one main outcome of the “University Tiger Screws” project is to develop new capabilities in the current staff in order to reduce the investments with additional hiring of personnel. A range of potential

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211

savings is defined and through the pairwise comparison using the Analytic Hierarchy Process (SAATY, 1980, SAATY, 2009 and VARGAS, 2010), the Expected Value of the benefit could be estimated (Exhibit 6). DATE:

UNIVERSITY TIGER SCREWS

19-ago-13

ANALYTICAL HIERARCHY PROCESS - INTAGIBLE BENEFITS CALCULATION

% Economy in the hiring process in the next 5 years

NAME OF THE GROUP TO BE EVALUATED

IMPACT

10% ECONOMY USD

871.670,43

7% ECONOMY USD

610.169,30

5% ECONOMY USD

435.835,22

2% ECONOMY USD

174.334,09

NO ECONOMY USD

EXPECTED VALUE

USD

608.110,28

PROBABILITY

10% ECONOMY

7% ECONOMY

5% ECONOMY

2% ECONOMY

NO ECONOMY

is just as likely

is most likely

is much more likely

is just as likely

is most likely

is much more likely

is just as likely

is most likely

is just as likely

10% ECONOMY

7% ECONOMY

5% ECONOMY

2% ECONOMY

NO ECONOMY

39,75%

28,43%

16,57%

9,16%

6,10%

INCONSISTENCY INDEX

5,4%

Exhibit 6 – Example of the use of AHP to estimate the Expected Value of the benefit of a project

The main output of this project is an updated list of projects including the estimated financial benefits.

Categorize Projects (Optional) For organizations with a wide range of projects, the categorization of projects could add value in the analysis and stratification of efforts (Exhibit 7). This optional process group the projects into different categories (Exhibit 8) like

• Departments • Risk • Value • Sponsoring group • Geographic location

1"/"1

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Determining the Mathematical Roi of a Project Management Implementation

Exhibit 7 – Categorize Projects

Exhibit 8 – Example of a categorized list of projects with the calculated benefits highlighted

Data Distribution Profile The Data Distribution Profile aims to determine the best “risk profile” of the portfolio to archive the benefits and it contains the process Determine Optimistic, Most Likely and Pessimistic Profile for Schedule, Investments and Results. Using market research, historical information from previous projects and benchmarking, the objective of this process is to define the optimistic, pessimistic and most likely scenarios for the duration, costs and financial results of each project (Exhibit 9).

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Exhibit 9 – Determine Optimistic, Most Likely and Pessimistic Profile for Schedule, Investment and Results

Different external sources can be used to support the decision as follows

• Standish Group Chaos Manifesto (STG, 2013) • The IPA Institute Database of Capital Projects (IPA, 2013) • PMI Pulse Report (PMI, 2013) • Reports and researches from management consulting companies This process requires a lot of negotiation to set the right thresholds for the project without being biased by individuals with over optimistic or over pessimistic behaviors. The determination of the profiles can be done considering that the project duration, costs and financial results follow the same distribution (Exhibit 10) or a different set of distributions for each element.

Exhibit 10 – Example of probabilistic forecasting based on project complexity level. In this case, a high complexity project with a value of $1,000,000 will cost between $1,250,000 and 1,750,000 without PM support and $1,000,00 and 1,150,000 with proper PM support.

Simulation The Simulation group describes the process associated with the Monte Carlo simulation of the duration of the projects in the portfolio, associated costs and financial results. “Monte Carlo” was a nickname of a top-secret project related to the drawing and to the project of atomic weapons developed by the mathematician

213

214

Determining the Mathematical Roi of a Project Management Implementation

John von Neumann (POUNDSTONE, 1993). He discovered that a simple model of random samples could solve certain mathematical problems which couldn’t be solved up to that moment. The simulation refers, however, to a method by which the distribution of possible results is produced from successive recalculations of project data, allowing the development of multiple scenarios. In each one of the calculations, new random data is used to represent a repetitive and interactive process. The combination of all these results creates a probabilistic distribution of the results (Exhibit 11).

Start

Finish

Forecast of Final Cost

Exhibit 11 – Construction of model of distribution of costs and activities or work packages making up a final distribution from random data of the project (PRITCHARD, 2001).

The feasibility of outcoming distribution relies on the fact that, for a high number of repetitions, the model produced reflects the characteristics of the original distribution, transforming the distribution into a plausible result for analysis. The simulation can be applied in schedules, costs and other project indexes. The Simulation group is divided in the following processes

• Simulate Portfolio of Projects • Identify Gains in Schedule, Investment and Results with the Implementation of the PMO

Simulate Portfolio of Projects This is process is responsible for the simulation of the schedule gains, investment savings and improvements of financial results (Exhibit 12).

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Exhibit 12 – Simulate Portfolio of Projects

The simulation is produced using simulation software and the results are a range of improvements in duration, budget and financial results with their respective confidence level (Exhibit 13, 14 and 15).

Exhibit 13 – Example of simulation output for the savings in the total time of the projects for using the PMO. In this case there is a 90% confidence that the savings will be above 59,87 months.

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Determining the Mathematical Roi of a Project Management Implementation

Exhibit 14 – Example of simulation output for the savings in the budget of the projects for using the PMO. In this case there is a 90% confidence that the savings will be at least 7,079,051.85 months.

Exhibit 15 – Example of simulation output for the gains in financial results of the projects for using the PMO. In this case there is a 90% confidence that the savings will be at least 3,040,938.53 months.

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Identify Gains in Schedule, Investment and Results with the Implementation of the PMO After the simulation is concluded, the results are collected for a predefined confidence level in order to identify the measurable improvements (Exhibit 16 and 17).

Exhibit 16 – Identify Gains in Schedule, Investment and Results with the Implementation of the PMO

Exhibit 17 – Example of gains based on the simulation results (Exhibit 13, 15 and 15)

217

218

Determining the Mathematical Roi of a Project Management Implementation

Investments in the PMO The other aspect that must be considered when evaluating the ROI of a project implementation is to calculate the amount of costs the organization will incur to create and maintain the Project Office. Different costs can be associated with the PMO (AUBRY, HOBBS, MÜLLER & BLOMQUIST, 2010). The most common elements are

• Personal cost • Software and hardware • Advisory services • Training • Others

Exhibit 18 – Calculate Investment and Operational Costs of the PMO/Project Structure

The main output of this process is the total cost of the PMO setup and operation for a predefined time frame (Exhibit 19).

Exhibit 19 – Example of a PMO Setup and operation cost in a 5 (five) year time frame. All values should be adjusted to Present Value

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Influence of the PMO in the Results Based on the studies discussed in the step 4 of the process, it is important to highlight that not all benefits and positive results exclusively stem from the very existence and operation of the PMO. Many other external factors can benefit from those results and are beyond the control of the project manager and his/ her team. Some examples of benefit / dis-benefit drivers not related to project management implementations are (UK CABINET OFFICE, 2011)

• External economic factors like currency exchange rate, interest rates • Market changes • Changes in the legislation • Changes in the senior leadership • Others In this process (Exhibit 20), it is proposed the use of the Analytical Hierarchy Process (AHP) to compare the likelihood of benefits coming from the PMO, in comparison with other sources of benefits (SAATY, 1980 and VARGAS, 2010).

Exhibit 20 – Determine the Influence of the PMO on Results

The output of this process is the weight of the PMO in relationship with other sources of benefits (Exhibit 21).

219

220

Determining the Mathematical Roi of a Project Management Implementation

Exhibit 21 – Example of AHP comparing different sources of benefits with project management implementation. In this case, it is suggested that 52,2% of the benefits could be justified by the setup and operations of a PMO

Results The final group of process intends to calculate the return on investment and also analyze and discuss the final results. The Results group is divided in the following processes

• Calculate the Return on Investment (ROI) of the PMO • Analyze Final Results Calculate the Return on Investment (ROI) of the PMO This process compare the results obtained in the simulation and compare them with the investments related to the PMO and the percentage of results attributable to the PMO (Exhibit 22).

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Exhibit 22 – Calculate the Return on Investment (ROI) of the PMO

The output of this process is the calculated return on investment with complementary information (Exhibit 23).

Exhibit 23 – Example of financial calculation based on the simulation results and the cost / relevance of the PMO in the results.

221

222

Determining the Mathematical Roi of a Project Management Implementation

Analyze Final Results After receiving the final ROI Report, working groups and the PMO sponsoring group need to meet in order to analyze and discuss the results to make the final decision (Exhibit 24).

Exhibit 24 – Analyze Final Results

Conclusions The proposed model is a “master line” of the value calculation and can thus be customized and adapted to different scenarios. It is important to highlight that this model is a simplification of very complex environment, where different perceived values can provide different directions to different stakeholders. In order to avoid resistance and criticism during the simulation of intangible results, it is important to do this work in teams to avoid personal biases in the process. Finally, it important to understand the challenge of determining ROI without knowing which projects are selected and the strategy that supports them. A project office that takes care of several different multi million projects is a very different effort from a project office that has simple work packages to be controlled.

References AUBRY, M., HOBBS, B., MÜLLER, R. & BLOMQUIST, T (2010). Identifying Forces Driving PMO Changes. Project Management Journal, Vol. 41, No. 4, pp. 30-45, 2010. Newtown Square: Project Management Institute. EIU (2009). Closing the Gap - The Link Between Project Management Excellence & Long-Term Success. London: Economist Intelligence Unit. HUBBARD, D. (2010). How to Measure Anything: Second Edition. Hoboken: John Wiley & Sons. IPA (2013). Research as a Foundation. Ashburn: Independent Project Analysts. Available at http://ipainstitute.com/Home/About/Research

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KWAK, Y.H. & IBBS, C.W. (2000). Calculating Project Management’s Return on Investment. Newtown Square: Project Management Journal, Volume 31, Issue 2. Available at http://www.lamarheller.com/projectmgmt/calculatingpmroi.pdf PHILIPS, J. J. & PHILIPS, P. P. (2007). Show me the Money: How to Determine the ROI in People, Projects and Programs. San Francisco: Berrett-Koehler Publishers. PMI (2013). Pulse Report of the Profession. Newtown Square: Project Management Institute. Available at http://www.pmi.org/Knowledge-Center/Pulse.aspx PMI (2013). The Project Management Body of Knowledge: Fifth Edition. Newtown Square: Project Management Institute. POUNDSTONE, W (1993). Prisoner’s Dillema. Flushing: Anchor Publishing Group. PRITCHARD, C. L. (2001). Risk Management: Concepts and Guidance. 2ª Ed. Arlington: ESI International. SAATY, T. L (1980). The Analytic Hierarchy Process. New York: McGraw-Hill International. SAATY, T. L. (2009). Extending the Measurement of Tangibles to Intangibles. International Journal of Information Technology & Decision Making, Vol. 8, No. 1, pp. 7-27, 2009. Available at SSRN: http://ssrn.com/abstract=1483438. STG (2013). The Chaos Manifesto. Boston: The Standish Group. Available at https:// secure.standishgroup.com/reports/reports.php UK CABINET OFFICE (2011). Managing Successful Programmes: Forth Edition. London: The Stationery Office. VARGAS, R (2004). Earned Value Probabilistic Forecasting Using Monte Carlo Simulation. Washington: AACE - Association for Advancement of Cost Engineering 48th Annual Meeting. VARGAS, R. V. (2010). Using Analytical Hierarchy Process to Select and Prioritize Projects in Portfolio Management. Dallas: PMI Global Congress North America. Available at http://www.ricardo-vargas.com/articles/analytic-hierarchy-process/

223

ADOPTING THE QUADRATIC MEAN PROCESS TO QUANTIFY THE QUALITATIVE RISK ANALYSIS

Accepted for publication at PMI Global Congress 2013 – North America New Orleans – Louisiana – USA – 2013

226

Adopting the Quadratic Mean Process to Quantify the Qualitative Risk Analysis

Abstract Related Podcasts

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Using the Quadratic Average (Mean) in the Qualitative Risk Analysis http://rvarg.as/bd

♫ Understanding the Risk Attitude http://rvarg.as/6j

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♫ Risk Management Special Playlist http://rvarg.as/bc

The objective of this paper is to propose a mathematical process to turn the results of a qualitative risk analysis into numeric indicators to support better decisions regarding risk response strategies. Using a five-level scale for probability and a set of scales to measure different aspects of the impact and time horizon, a simple mathematical process is developed using the quadratic mean (also known as root mean square) to calculate the numerical exposition of the risk and consequently, the numerical exposition of the project risks. This paper also supports the reduction of intuitive thinking when evaluating risks, often subject to illusions, which can cause perception errors. These predictable mental errors, such as overconfidence, confirmation traps, optimism bias, zero-risk bias, sunk-cost effect, and others often lead to the underestimation of costs and effort, poor resource planning, and other low-quality decisions (VIRINE, 2010).

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Qualitative X Quantitative Risk Analysis One of the main challenges during the analysis of a risk is to define the right approach to assess the amount of the exposure/opportunity. The two basic steps to determine the right level of risk are based on the qualitative and quantitative analysis (Exhibit 1). Output

Qualitative Assessment of Risk Level

Risk Prioritisation

Risk Analysis

Updated Risk Register

Quantitative Exhibit 1 – Analysis of Risk Process Flow (ROSSI, 2007).

A qualitative risk analysis prioritizes the identified project risks using a predefined scale. Risks will be scored based on their probability or likelihood of occurrence and the impact on project objectives if they occur (Exhibit 2 and 3). LEVEL

SCORE

High Medium Low

Very High

High

Medium

Low

Very Low

FOR THREATS

Red Yellow Green

Exhibit 2 – Example of scales used in the qualitative risk analysis

Qualitative Risk Matrix

High

Medium

3’

4’

2’

3”

Low

Frequency

Risk Levels are Relative to Regions Connected by Arrows

5 Highest Risk

Lowest Risk

Low

Medium Consequence

High

Exhibit 3 – Example of Qualitative Risk Matrix with 3 x 3 Levels (ALTENBACH, 1995)

227

228

Adopting the Quadratic Mean Process to Quantify the Qualitative Risk Analysis

A quantitative risk analysis is based on simulation models and probabilistic analysis, where the possible outcomes for the project are evaluated, providing a quantitative, numeric and many times financial risk exposure to support decisions when there is uncertainty (PMI, 2013). Some quantitative processes are simple and direct like rolling a dice (Exhibit 4), but most of them involve very complex simulation scenarios like the Monte Carlo Simulation.

Exhibit 4 – Diagram showing the deterministic probability of rolling a dice

“Monte Carlo” was a nickname of a top-secret project related to the drawing and to the project of atomic weapons developed by the mathematician John von Neumann (POUNDSTONE, 1993 and VARGAS, 2013). He discovered that a simple model of random samples could solve certain mathematical problems, which couldn’t be solved up to that moment. The simulation refers, however, to a method by which the distribution of possible results is produced from successive recalculations of project data, allowing the development of multiple scenarios. In each one of the calculations, new random data is used to represent a repetitive and interactive process. The combination of all these results creates a probabilistic distribution of the results (Exhibit 5 and 6). Start

Finish

Forecast of Final Cost

Exhibit 5 – Construction of model of distribution of costs and activities or work packages making up a final distribution from random data of the project (PRITCHARD, 2001).

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Exhibit 6 – Example of Monte Carlo simulation to assess the cost impact of a potential threat to the project

Because quantitative analysis is based on mathematics and statistics supported by objective metrics, such analyses are considered to be more rigorous (SMOCK, 2002). The main challenges of a solid quantitative analysis are the time and effort it requires to be executed and the required technical background in statistics to make the proper parameterization of the data. The main advantages and disadvantages of each method are presented in the Exhibit 7. QUANTITATIVE METHODS

Facilitates the cost benefit analysis Advantages

Gives a more accurate value of the risk More valuable Results of the method may not be precise

Disadvantages

Numbers can give a false perception of precision More expensive and time consuming

QUALITATIVE METHODS

Relatively simple to be implemented Easily determine risk categories with greater impact in the project Visually impactful A lack of understanding of the parameters used in the scale can lead to different interpretations Results can be biased Less valuable

Exhibit 7 – Example of Monte Carlo simulation to assess the cost impact of a potential threat to the project (based on ROT, 2008)

The risk model proposed hereafter is a qualitative process with numerical results, reducing the ambiguity of the qualitative process without adds the time and effort to determine with precision the probability and the impact of uncertain events in the project.

Assessing Probability The proposed qualitative probability assessment is based on a scale with their respective scores (Exhibit 8).

229

230

Adopting the Quadratic Mean Process to Quantify the Qualitative Risk Analysis

LEVEL

SCORE

DESCRIPTION

Very High

It is expected that the event will occur. If it does not occurs it will be a surprise.

High

The event has a great chance of occurring.

Medium

The event can occur.

Low

It will be a surprise if the event occurs.

Very Low

Very remote chance of the event to happen. Practically impossible.

Exhibit 8 – 5 Scales to assess the risk probability

For each identified risk a score from 1 (one) to 5 (five) should be determined.

5 Dimensions of the Impact The impact of the event, in case it occurs, can be perceived in different dimensions of the project objectives. For example, one risk can have a major impact on costs but not necessarily an important impact in quality. It is very important to highlight that threats and opportunities should be analyzed separately. The basic groups where impact should be evaluated are (Exhibit 9):

• impact on time and deadlines • impact on costs • impact on quality • impact in safety and security • other impacts TIME

OTHER

SAFETY & SECURITY

COST

QUALITY

Exhibit 9 – Basic impact groups showing the different impact dimensions of one specific risk

Each project may develop different impact groups based on the nature of the project, including groups like: impact on reputation, regulatory impact, environmental impact, social impact, and stakeholder’s impact, among several others. Following is the presentation of the 5 basic groups.

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Impact on Time and Deadlines One should assess the level of impact on the conclusion of the project. It can be positive or negative for opportunities and threats, respectively. Threats that impact the conclusion of the project must be considered as a priority if compared to other events. Because each project differs in size, complexity and several other factors, the project team needs to agree on the level of tolerance that they consider appropriate for each level of impact, like the example shown in Exhibit 10. LEVEL

SCORE

DESCRIPTION

Very High

Delays/Anticipation above 180 days or 6 months.

High

Delays/Anticipation between 120 and 180 calendar days.

Medium

Delays/Anticipation between 60 and 120 calendar days.

Low

Delays/Anticipation between 15 and 60 calendar days.

Very Low

Less than 15 calendar days of delays/ anticipation.

Exhibit 10 – Example of impact scale and score for time and deadlines

Impact on Costs One should also assess the level of impact that the event may bring to the total project cost. It can be positive (savings) or negative (additional expenditures) for opportunities and threats, respectively. Like mentioned for time and deadlines, the project team needs to agree on the level of tolerance that they consider appropriate for each level of impact, like in the example for costs presented in the Exhibit 11. LEVEL

SCORE

DESCRIPTION

Very High

Variation (positive or negative) above $1,000,000.

High

Variation (positive or negative) between $500,000 and $1,000,000.

Medium

Variation (positive or negative) between $250,000 and $500,000.

Low

Variation (positive or negative) between $100,000 and $250,000.

Very Low

Variation (positive or negative) lower than $100,000.

Exhibit 11 – Example of impact scale and score for costs

231

232

Adopting the Quadratic Mean Process to Quantify the Qualitative Risk Analysis

Impact on Quality Assesses the level of impact on the quality required for the project. It can be positive or negative for opportunities and threats, respectively. As presented in the other groups, the project team needs to agree on the level of tolerance that they consider appropriate for each level of impact like in the example in Exhibit 12 for negative risk events. LEVEL

SCORE

DESCRIPTION

Very High

Client rejects the delivery or product.

High

Client asks for immediate corrective actions.

Medium

Client perceives and asks for action/ information.

Low

Client perceives but forgives and no action is needed.

Very Low

Imperceptible impact (most of the time not even perceived by the stakeholders).

Exhibit 12 – Example of impact scale and score for quality (only negative events)

Impact in Safety and Security Assesses the level of impact that the event can incur in safety at work and security. This impact group could include or not aspects related to environment, physical security of the work in the project, data security (IT), and reputation, among others. In the Exhibit 13, an example of scale is presented to assess impacts in safety and security. LEVEL

SCORE

DESCRIPTION

Very High

Crisis. Impact is so evident and public that the project could not proceed as planned.

High

Evident impact on environment/reputation.

Medium

Impact is perceived and raises concerns.

Low

Perceived impact on environment/ reputation but without relevance.

Very Low

No impact on environment and reputation.

Exhibit 13 – Example of impact scale and score for safety and security, with focus in environment and reputation

Other impacts This group is an optional group and aims to include any other specific impact of a risk that was not covered in the previous groups. It is important that the score of the other impacts, if it exists, should be from 1 to 5 like the other impact groups.

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Proximity: The 6th Impact Dimension Another dimension of the impact is the time horizon or proximity of the event (Exhibit 14). An event that may happen in hours requires different actions than another event that could impact the project in 2 years. If an event is close to happen, it has a higher priority if compared with future events (in the proximity aspect).

M ER

NEAR

MEDIUM T

IM EDIATE M

ONGͳTER RY L M E V

LONGͳTERM

Exhibit 14 – Understanding the time horizon

The proximity scale should be compatible with the other impact groups (1 to 5 score for different time horizons). It is important that the project team defines what are immediate events, short-term events, medium-term events, long-term events and very long-term events (Exhibit 15). It is important to highlight that immediate events will score higher than very long-term events when assessing their proximity. LEVEL

SCORE

DESCRIPTION

Very High

Event can happen anytime in the next 15 days.

High

Event can happen between 15 days and 3 months.

Medium

Event can happen between 3 and 6 months.

Low

Event can happen between 6 months and 1 year.

Very Low

Event can happen more than 1 year ahead.

Exhibit 15 – Example of proximity scale and score

233

234

Tempo

Custos

Qualidade

Proteção e Segurança

Outras

Proximidade

impactada Adopting the Quadratic Mean Process to Quantify the Qualitative Risk Analysis Risco A

Tabela 1. Exemplo dos resultados do impacto e a fórmula de cálculo.

Calculating the expected value and final risk assessment

Área impactada Risco A

√

Tempo

Custos Qualidade 3 Proteção 32 + 2 + e1Segurança +used 1 + to 1 assess +Outras 4 and Proximidade The expected value is a = risk measurement prioritize = 2, 31risk events Impact = 6 6 (Exhibit 16).2 3 1 1 1 4

Impact =

Expected Value = Probability x Impact 32 32 + 2 2 + 1 2 + 1 2 + 1 2 + 4 2 = 2, 31 = 6 Mean2 Exhibit 16 6 Quadratic = Arithmetic Mean2 + Variance

√

Using the qualitative the probability Expected method, Value = Probability x Impactwill range from 1 to 5 (Exhibit 8).

Impact =

Quadratic Mean Arithmetic + Variance The impact is based on=the impactMean in different aspects of the project and the 2 + Imp. on Quality 2 + Imp. on S&Security 2 + Imp. on Other 2 + P roximity Imp. on Tproximity ime2 + Imp. on Costs using a quadratic mean (root square mean) calculation (Exhibit 17). 2

Tabela 1. Exemplo dos resultados do impacto e a fórmula de cálculo. Imp. on T ime2 + Imp. on Costs2 + Imp. on Quality 2 + Imp. on S&Security 2 + Imp. on Other2 + P roximity 2 Impact = Área de cálculo da média quadrática. 6 Figura 3. Fórmula Tempo Custos Qualidade Proteção e Segurança Outras Proximidade

impactada

A da média quadrática. 3 Figura 3. FórmulaRisco de cálculo

Exhibit 17

Thequantitativas decision for the quadratic insteadcomo of therolar arithmetic mean(Figura is based??), on mas a maioria e Alguns processos são simplesmean e diretos, um dado √ 2+ 2 + 1add 2 + 1additional 2 + 42 the concept different levels impact exposure toa the project 32 32 + como 2of 1rolar Alguns processos quantitativas sãothat simples e=diretos, dado (Figura maioria envolve uma complexa simulação de cenários como aum Simulação Monte "Monte Carlo" foi um =mas 2, 31Carlo. Impact = de ??), and this variance should be considered6 as a risk factor to the6 project. volvealcunha uma complexa simulação cenários comorelacionado a Simulação ao de Monte Carlo. Carlo" foi umade armas atômic para um projetodeultra-secreto desenho e ao"Monte desenvolvimento Theultra-secreto relationship relacionado between theaoquadratic mean and the arithmetic alcunha para um projeto desenho e ao desenvolvimento de mean armas isatômicas Value = Probability x Impact criadas pelo matemático John vonExpected Neumann (POUNDSTONE, 1993 e VARGAS, 2013). Ele descobriu q

criadas pelo matemático John von Neumann (POUNDSTONE, 1993 e VARGAS, 2013). Ele descobriu que Quadratic Mean2 poderia = Arithmetic Mean2 + Variance um simples modelo de amostras randômicas solucionar certos problemas matemáticos, que n um simples modelo de amostras randômicas poderia solucionar certos problemas matemáticos, que não podiam ser solucionados até aquele momento. Exhibit 18 podiam ser solucionados até aquele momento.

where the variance is a measure of how far a set of numbers is spread out A simulação se refere, no entanto, a um método pelo qual ados distribuição dos resultados possíveis é p A simulação se refere, no entanto, a um método pelo qual a distribuição resultados possíveis é proImp. on T ime2 + Imp. on Costs2 + Imp. on Quality 2 + Imp. on S&Security 2 + Imp. on Other2 + P roximity 2 Impact = The variance concept is directly related to of the different impact duzidas através de sucessivos recálculos dos dados do projeto, o desenvolvimento de múltiplos 6 dispersion duzidas através de sucessivos recálculos dos dadospermitindo dothe projeto, permitindo o desenvolvimento de múltip Copyright 2014 by Ricardo Viana Vargas. All Rights Reseved groups. If the impact ranges are very wide, the variance will also be high and the cenários. Em cada um dos cálculos, novos dados randômicos são usados para representar um processo

cenários. Em cada um dos cálculos, novos dados randômicos são usados para representar um proces

difference between the proposed quadratic mean and the traditional arithmetic Figura 3.e interativo. Fórmula de cálculo da média quadrática. repetitivo A combinação de todos estes resultados cria uma distribuição probabilística dos

repetitivo e interativo. combinação de the todos resultados cria uma distribuição probabilística d mean will A increase, increasing riskestes impact.

resultados e 6). Tabela 1. (Figuras Exemplo 5dos resultados do impacto e a fórmula de cálculo.

resultados (Figuras 5 e 6). of the impact results is presented in the Exhibit 19. One example Alguns processos quantitativas são simples e diretos, como rolar um dado (Figura ??), mas a maioria en Área impactada

Tempo

volve uma complexa

Custos

IMPACT TIMEde simulação

QualidadeIMPACT Proteção e Segurança IMPACT OTHER

IMPACT COST cenários

Outras

Proximidade

QUALITY S&S IMPACT como a Simulação de MontePROXIMITY Carlo. "Monte Carlo" foi um

Risco A 3 2 1 1 1 4 A 3 2relacionado 1 1 4 alcunha umRisk projeto ao desenho e 1aooudesenvolvimento de armas atômica **Figura 5** para – Construção de umultra-secreto modelo de distribuição de custos e atividades, pacotes de trabalho,

e2001). √ aleatórios gerando umapelo distribuição ﬁnal a partir do projeto (PRITCHARD, criadas John von Neumann (POUNDSTONE, 1993 2013). Ele descobriu qu 2+ **Figura 5**matemático – Construção de de umdados modelo distribuição atividades, ou pacotes de trabalh 32 + 2de 12 + 12 + 12 +de 42custos 32eVARGAS, Impact = = = 2, 31 6 6 problemas matemáticos, que nã um simples modelo amostras poderia solucionar certos **Figura 6** – uma Exemplo dade simulação dearandômicas Monte para avaliar o impacto no custo de uma possível gerando distribuição ﬁnal partirCarlo de dados aleatórios do projeto (PRITCHARD, 2001). Exhibit 19 ameaça ao projeto. podiam ser solucionados até aquele Expected momento. Value = Probability x Impact

**Figura 6** – It Exemplo da simulação Carloand para avaliar o can impacto no custo de uma possív is important to highlightde thatMonte the threats opportunities be calculated

2 Em razão da análise quantitativa ser baseada na matemática e na estatística Quadratic Mean2 = Arithmetic Meansuportadas + Variancepor métricas obje-

Aameaça simulação se refere, nosame entanto, a um pelo signals qual a (+ distribuição dos resultados using the formula, butmétodo with different for opportunities and – for possíveis é pro ao projeto. tivas, tais análises são threats). consideradas mais qualitative rigorosas (SMOCK, 2020). of Os the principais desaﬁos de uma by análise The total exposure project is determined the duzidas através de sucessivos recálculos dosrisk dados do projeto, permitindo o desenvolvimento de múltiplo Em razão da são análise quantitativa ser baseada na matemática e na estatística suportadas quantitativa sólida o tempo e o esforço requeridos para a sua execução e também o requerido conhecsum of the expected values of all threats and opportunities. An example of this por métricas ob cenários. Em cada um dos cálculos, novos dados randômicos são usados para representar um process process is presented on the Exhibit 20. tivas, Imp. tais análises consideradas Os principais desaﬁos deP uma anál 2 + Imp. on 2 on T ime2são + Imp. on Costs2 +mais Imp. rigorosas on Quality 2(SMOCK, + Imp. on2020). S&Security Other2 + roximity 3 Impact = repetitivo e interativo. A combinação de todos estes resultados cria uma distribuição probabilística do quantitativa sólida são o tempo e o esforço requeridos6para a sua execução e também o requerido conh resultados (Figuras 5 e 6). Figura 3. Fórmula de cálculo da média quadrática.

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TYPE

PROBABILITY

PROXIMITY

IMPACT TIME

IMPACT COST

IMPACT QUALITY

IMPACT SAFETY AND SECURITY

OTHER IMPACTS

TOTAL IMPACT

EXPECTED VALUE

Threat

2,31

(2,31)

Threat

3,54

(7,07)

Threat

3,92

(7,83)

Opportunity

3,51

10,54

Opportunity

2,68

10,71

Threat

1,83

(9,13)

Total Risk Expected Value Exhibit 20 – Example of a project expected risk value considering opportunities and threats.

The results from the process will be always a number between 1 and 25. In the example of Exhibit 20, the value -5,10 is equivalent to 20,4% negative exposure (5,10/25) for the Project. Based on this result and the tolerance thresholds (HILSON & MURRAY-WEBSTER, 2007), the total exposure can be compared with other projects and the corporate limits to define potential risk response plans.

Conclusions The qualitative risk method is always a simplified model if compared with the quantitative methods. The approach of this paper suggests an alternative model that can be tailored to include different kinds of impacts and scales in order to produce a reliable quantitative result. This result allows opportunities and threats to be compared in order to determine the total risk exposure. The concept that an opportunity can cancel a threat of the same level is not possible with the traditional qualitative risk management approach.

References ALTENBACH, T. J. (1995). A Comparison of Risk Assessment Techniques from Qualitative to Quantitative. Honolulu, ASME Preassure Vessels and Piping Conference. Available at http://www.osti.gov/bridge/servlets/purl/67753-dsZ0vB/webviewable/67753.pdf HILSON, D. & MURRAY-WEBSTER, R. (2007). Understanding and Managing Risk Attitude. London: Gower Publishing. PMI (2013). The Project Management Body of Knowledge: Fifth Edition. Newtown Square: Project Management Institute.

(5,10)

235

236

Adopting the Quadratic Mean Process to Quantify the Qualitative Risk Analysis

POUNDSTONE, W (1993). Prisoner’s Dillema. Flushing: Anchor Publishing Group. PRITCHARD, C. L. (2001). Risk Management: Concepts and Guidance. 2ª Ed. Arlington: ESI International. ROSSI, P. (2007). How to link the qualitative and the quantitative risk assessment. Budapest: Project Management Institute Global Congress EMEA. ROT, A. (2008). IT Risk Assessment: Quantitative and Qualitative Approach. San Francisco: World Congress on Engineering and Computer Science. SMOCK, R. (2002). Reducing Subjectivity in Qualitative Risk Assessments. Bethesda, SANS Institute. Available at http://www.giac.org/paper/gsec/2014/reducing-subjectivity-qualitative-risk-assessments/103489 VARGAS, R. V. (2013). Determining the Mathematical ROI of a Project Management Implementation. New Orleans, Project Management Institute Global Congress North America. VIRINE, L. (2010). Project Risk Analysis: How Ignoring It Will Lead to Project Failures. Washington: Project Management Institute Global Congress North America.

URGENCY: A CRITICAL FACTOR IN PROJECT PLANNING

Accepted for publication at PMI Global Congress 2011 - North America Dallas – Texas – USA – 2011

238

Urgency: A Critical Factor in Project Planning

Abstract Related Podcasts

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Understanding the Rolling Wave Planning http://rvarg.as/6z

♫ When Much sometimes is Too Much http://rvarg.as/bn

♫ Understanding the Effort Driven Task in a Project http://rvarg.as/am

♫ Using Generic Resources in the Resource Allocation http://rvarg.as/3e

Given the natural speed and dynamism of the world, agility and sense of urgency has become preponderant in all projects. Challenging deadlines and budget make the management of these projects a risky activity. The more time and cost become challenging, the need for a more meticulous and detailed planning becomes fundamental. On the other hand, the urgency in the planning of these activities often directly affects the quality of the developed plans. This article aims to discuss the costs and benefits of speed in developing a project plan and proposes a basic process that consists of 10 steps to plan and 10 steps to track a project in a short time. The process aims to simplify and prioritize critical documents to be developed in order to ensure the purpose, scope, deadlines and budgets, as well as direct restrictions of the project to be developed. Finally, the article presents a list of success factors to be observed to handle and quickly develop effective project plans.

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Urgency: the Costs and Benefits of Speed A project is carried out to produce a beneficial change in the environment and it has three features (TURNER & MÜLLER, 2003):

• It is unique: there are not equal previous projects. • It is new: previous projects did not use the same approach. • It is temporary: it has a beginning and an end. These features produce certain pressures, like the sense of urgency, the uncertainty and the need of integration. The urgency is directly related to the production of results within the shortest time. OBJECTIVE

FEATURES

PRESSURES

Unique

Uncertainty

To deliver Beneficial Change Novel Temporary

Integration Urgency

Exhibit 01 – Projects’ features (TURNER & MÜLLER, 2003)

According to Betty Sue Flowers (MARCUS, 1998), people must have a sense of urgency even when they are facing a good situation. The sense of urgency doesn’t come only from an emerging crisis, but also from the need to be ready for any situation, including opportunities. Given this scenario, it is essential that the project manager respond immediately to requests from customers and from other interested with a legitimate sense of urgency (KERNION, 1999). Thus, the challenge becomes balancing the sense of urgency and pressure with time for reflection, experimentation and innovation that a unique product or service will require to be developed (EPPLER & SUKOWSKI, 2000).

Simplified Flow for the Development of Project Plan In order to directly attend the need, we need to simplify the management process. Simplification occurs through careful analysis of the processes that may be deemed fundamental and essential. Importantly, only the processes considered crucial must be carried on, since we consider the speed of development as a priority, but it does not mean that other processes that are not listed cannot bring results in project planning. The proposed flow is based on the PMBOK ® Guide (PMI, 2008), highlighting the sequence of activities that make up the process, starting from a assumption that there is already an assigned project manager. From the detailed processes in the guide, we set up a flow with 10 processes, as highlighted in Exhibit 01 and detailed below.

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Develop Project Charter

Create WBS

Develop Schedule

Determine Budget

Develop Responsibility Assignment Matrix 06

Develop Communication Plan

Develop Preliminary Risk Plan 08

Consolidate Project Plan

Approve Project Plan

Hold Project Kick-off Meeting Exhibit 02 – Simplified flow for the development of Project Plan

1. Develop Project Charter – This process aims to develop the Project Charter that documents the business needs that will be attended by the projects, in addition to obtaining the commitment of areas / people involved and disseminate the official birth of the project to all interested. The Project Charter should be kept unchanged throughout the project. Its update is done in case of extreme change in the project, for example, changing the sponsor or the project manager, or a substantial change in the budget or schedule. The “urgent” Project Charter should also incorporate some elements that traditionally should be in the Scope

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Statement. In this case, what is proposed is the development of a single document that brings together the main points of the Statement of Scope to the Project Charter. 2. Create the Work Breakdown Structure – Process that aims to develop the main tool of design of the project scope. The project WBS is a hierarchical structure that presents a visual decomposition of the project into smaller, more manageable parts, called “work packages”. It must be constructed as “top-down” and detailed initially up into approximately 3 levels. The other levels will be updated and detailed with the development of projects through rolling waves planning models (GITHENS, 1998).

Amount of detail in “Open Work Packages”

Time Exhibit 03 – Rolling Wave planning (GITHENS, 1998)

3. Develop Schedule – Process that assigns durations to work packages (lower level of WBS) and the precedence relationship between these packages, resulting in the project Network Diagram and Gantt chart. At this stage, the estimated duration of the project is determined. 4. Determine Budget – The objective of this process is developing the estimated cost of the project works that will consolidate the project budget and the baseline costs. The project budget should be developed at the level of detail that is compatible with the actual details of the work and can / should be refined with project updates. 5. Develop Responsibility Assignment Matrix – Process that aims to develop the spreadsheet that defines the responsibilities within the project. It lists the supplies and / or large blocks of WBS with the human resources responsible for implementation and approval of work, as well as the stakeholders to be informed and consulted. It is also known as RACI matrix (ARMSHAW, 2005).

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6. Develop Communication Plan – Process that aims to develop a simplified spreadsheet highlighting who will receive the information (identified stakeholders), what is going to be informed, when communication is made, where the information will be collected, the reason why the communication is being performed, who is responsible for communication and how it is done and the cost of production of the information (5W and 2H). 7. Develop Preliminary Risk Plan – The objective of this process is to identify potential project risks using a structured approach to collect and document the identified risks, such as the Nominal Group Technique (NGT), Delphi and Brainstorming (ADAMS & MEANS, 2006). It is suggested that only threats are identified, ruling out opportunities for the process to be developed faster. Then, the identified risks are analyzed in terms of probability, impact and urgency, allowing that action plans can be developed in response to major risks. The risk plan will be updated throughout the work. 8. Consolidate Project Plan – Process that groups the documents previously produced in the Project Plan. Any presentations and supporting documents can also be consolidated into the plan to facilitate the process of presenting the project for approval. 9. Approve Project Plan – The objective of this process is to ensure that the responsible for the approval can review the documents and the analysis developed in the project plan, ensuring that all deliveries are planned in accordance with the stated objectives. The approval authorizes the commencement of work and turns the project plan approved at the baseline assessment of performance. 10. Hold Project Kick-off Meeting – The Project kick-off meeting is an extremely important event because it aims to promote the start of project activities and how it should contribute to achieving the Organization’s strategic objectives. In addition to constitute itself as an opportunity that seeks to ensure the Organization’s commitment to the project, it is considered the first work meeting of the core project team, in which the plan is presented, always seeking the involvement of the stakeholders.

Simplified Flow for Project Monitoring and Control The update of the project plan developed according to the previous process can also be presented by 10 (ten) simplified procedures, including the approval process and implementation of changes. The simplified procedure for updating the plan is carried out repeatedly for each monitoring cycle.

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The cycle time is determined by a function of the duration of the project and organizational planning parameters (ROSENHEAD, 2008). Usually, a project must have its monitoring cycle every 10% of the projected length; the minimum interval between cycles is 1 day and the maximum interval between cycles is 30 days. As an example, a project of 10 weeks suggests a break between cycles of 1 week as a project of 20 weeks suggests a break between cycles of 2 weeks. The simplified flow for project monitoring and control is shown in Exhibit 04. START 01

Collect Performance Information 02

Update WBS 03

Update Schedule 04

Update Budget

Revise Responsibility Assignment Matrix and Communication Plan

Update Risk Plan and Risk Response Plan 07

Develop Project Status Report 08

Hold Change Control Meeting

There were changes?

Yes

Implement Approved Changes

No 10

Document Lessons Learned

END

Exhibit 04 – Simplified Flow for the Project Monitoring and Control

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1. Collect Performance Information – The goal of this process is to obtain information on the performance of the project with the team, the suppliers, etc. The collection can be done in a structured way or through adaptations and simplifications of agile models, such as parts of the dynamics model for the collection and exchange of information taken at meetings of Daily Scrum of the Scrum model, for example (SCHWABER, 2010). It is important to emphasize that the goal of the process is the collection of information and not decision making. 2. Update WBS – The objective of this process is to update the Work Breakdown Structure (WBS) so that it continues to reflect all deliveries made in the cycle. The remaining work should be evaluated, and the drawing of future deliveries should be performed if necessary. We must pay important attention to the difference between detailing future deliveries and creating new deliveries. The creation of new deliveries that are not expected is a classic case of a sprawl of scope (scope creep) (KUPRENAS & NASR, 2003). 3. Update Schedule – Process that aims to identify the work already done and their deadlines, as well as updates on the WBS, seeking to update the schedule and determine the project deadline. The new timing and deadline will be compared with the approved schedule (baseline) to assess the performance of the project. 4. Update Budget – The objective of this process is to assess the outlay for carrying out the work cycle and update the remaining budget. The new budget will be compared with the approved budget (baseline) to evaluate the performance of the project. 5. Revise Responsibility Assignment Matrix and Communication Plan – The objective of this process is to update the Responsibility Assignment Matrix and Communication Plan. During the implementation of the project changes beyond the responsibilities inherent to the project, there are often roles exchanging and refinements in responsibilities, that causes changes in the Responsibility Matrix. The communication results are evaluated in this process to check if any element of communication needs to be created, deleted or amended in accordance with the behavior of the stakeholders. It aims to ensure that only valid information that supports the decision and the need for information will be produced, avoiding unnecessary stress on the production of useless information. 6. Update Risk Plan and Risk Response Plan – The objective of this process is to update the Risk Plan by identifying new risks and reviewing the already identified risks. The status of existing action plans and the evaluation of their effectiveness are also performed in this process.

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7. Develop Project Status Report – The objective of this process is to consolidate all executive information in a simple and straightforward report. The target audience of the report is defined in the Communication Plan and its contents present summary information about the performance of the project cycle and recommendations for change. 8. Hold Change Control Meeting – The objective of this process is to communicate the status of the project cycle, analyze the proposed change requests and decide on their incorporation (or not) to the projects. 9. Implement Approved Changes – Process that aims to incorporate the approved changes to the project plan, including quick review of the documents already developed and appropriated communications about the implemented changes to the stakeholders. 10. Document Lessons Learned – Process that aims to consolidate the lessons learned collected during the last cycle of the project. The lessons contain the record of positive experiences, such as improvements in processes and good management decisions, in addition to the negative experiences that have occurred and the points that should be improved identified during the project.

Assumptions and Success Factors Developing project plans quickly requires a different environment from the conventional planning. It is crucial to understand some assumptions and success factors to proper understand not only the process but also the results. Initially, it is important to note that the results obtained with this model are less detailed than those of conventional planning based on the PMBOK ® Guide. This model assumes a reduction in the existing procedures in order to accelerate the development process, and areas of knowledge related to the scope, time, cost, risk and communications were prioritized. This does not mean that other areas are less important. The documents produced must be simple and straightforward, if there are document templates in the Organization, only their essential fields should be used. It is important to advice that essential is different from important. Essential fields and information are the kind of information that can make the planning not viable if they are not provided. Another advice is to produce the documents using the usual market software such as spreadsheets and texts processors. Integrated and interrelated complex systems increase the ability to control and have many benefits; however, they may not provide the mobility and flexibility required for the accelerated development of the plan.

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Urgency: A Critical Factor in Project Planning

It is suggested that project planning is performed using the concept of rolling waves (GITHENS, 1998), in order to detail with precision the immediate work and with less accuracy the medium and long term work. These works of medium and long term will be detailed in future update cycles. Also, quick planning requires a degree of tolerance to risks bigger than the required by the conventional planning (HILSON & MURRAY-WEBSTER, 2005). We can observe, in Exhibit 05, that profiles that have a high degree of discomfort with the uncertainty (Paranoid and Averse) present more difficult to plan, execute and decide on a scenario of urgency due to the high degree of discomfort found in these occasions. Therefore, the proposed process might not fit for all organizations in all cases. Risk Addicted

Risk Seeking

Extreme

Low

Confort Level

High

Zero

Disconfort Level

246

Low

Risk Tolerant

High Risk Averse

Extreme Risk Paranoid

Exhibit 05 – Response to Uncertainty (HILSON & MURRAY-WEBSTER, 2005)

Finally, it is suggested that planning work should be done as a team, following classical models of co-location ( or war room), in which the project team works most of the time in the same physical space and keeps in touch face to face (MEARMAN, 2004). This type of work allows a better communication, a reduction in business “silos”, an increase in capacity and knowledge sharing in an emergency scenario, and makes the decision process more responsive and effective.

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Conclusions Quick planning aiming to attend the continuous need and the sense of urgency of the organizations is a clear trend in working with projects. In order to satisfy this critical sense of urgency, many projects are implemented without any planning because planning takes time and affects the sense of urgency required. The proposed model aims to attend this specific scenario, it is a simplification of the planning reality and it does not intended to replace the conventional model of project planning, in which concepts, methods and market standards must be evaluated and structured in the project planning. When there is a minimum acceptable time for the development of a structured plan, this plan becomes essential and should address in greater detail the knowledge areas outlined in PMBOK ® Guide (PMI, 2008), as well as other concepts and market standards. The use of the proposed model is only recommended when there is no possibility of building a structured plan for the project.

References ADAMS, T & MEANS, J. (2006). The Project Meeting Facilitator. Seattle: PMI Global Congress North America. ARMSHAW, D. (2005). There has to be a Better way than this! How to get big benefits from Project management basics. Edimburg: PMI Global Congress EMEA. EPPLER, M. J. & SUKOWSKI, O. (2000). Managing Team Knowledge: Core Processes, Tools and Enabling Factors. London: European Management Journal Vol. 18, No. 3 GITHENS, G D. (1998). Rolling Wave Project Planning. Long Beach: PMI Annual Symposium and Congress. HILSON, D. & MURRAY-WEBSTER, R. (2005). Understanding and Managing Risk Attitude, Burlington: Gower. KERNION, D. M. The Project Manager—A Key Player in the Consulting Engineering Firm’s Marketing Plan. Long Beach: PMI Annual Congress and Symposium. KUPRENAS, J. A. & NASR, E. B. (2003). Controlling Design-Phase Scope Creep. Morgantown: AACE Transactions. MARCUS, G. (1998). Corporate Futures, Vol. V. Late Editions Series. Chicago: University of Chicago Press. MEARMAN, M. (2004). Implementation Project Management: Now that You Bought it What Do You Do? Anaheim: PMI Annual Symposium and Congress.

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PMI (2008). PMBOK: A Guide to the Project Management Body of Knowledge: Forth Edition. Newtown Square: Project Management Institute. ROSENHEAD, R. (2008). Let’s Make Those Project Meetings More Effective. Available at http://www.projectsmart.co.uk/pdf/make-those-project-meetings-more-effective.pdf SCHWABER, K. (2010). Agile Project Management with Scrum. Redmond: Microsoft Press. TURNER, J. R. & MÜLLER, R. (2003). On the nature of the project as a temporary organization. Amsterdam: International Journal of Project Management.

AVOIDING MISTAKES DURING THE TEAM ACQUISITION: ASSIGN THE RIGHT PEOPLE TO THEIR RIGHT FUNCTIONS USING MBTI

Accepted for publication at PMI Global Congress 2005 - EMEA Edinburgh – Scotland – 2005

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Avoiding Mistakes during the Team Acquisition: Assign the Right People to Their Right Functions using MBTI

Abstract Related Podcasts

♫

Project Team Management http://rvarg.as/bf

♫ Understanding the 3 Factors to improve the Project Team sizing http://rvarg.as/4a

♫ Getting to know the World of Megaprojects: Communications and Teams – Part 3 of 3 http://rvarg.as/4j

This article presents a vision of the models of preferences presented by the Myers Briggs Type Indicator© (MBTI) and its application for the Staff Acquisition. Using the comprehension of the relation between the 16 MBTI Types with the main project Management functions, is possible to attribute functions inside the project in concordance with the individual preferences, increasing the motivation of the team and reducing potentials conflicts and problems in the project.

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Team Acquisition in the Project The project human resources area is one of the PMBOK Guide 3rd (PMI 2004) knowledge areas that the manager and project team have requested more attention. As reported in the Guide, the Project Human Resources Management includes the processes required to make the most effective use of human resources involved with the project. It includes all project stakeholders: sponsors, customers, individual contributors and others. The main processes are described below and Exhibit 1 provides the mind map of the processes.

• Human Resources Planning – Identifying and documenting project roles,

responsibilities, and reporting relationships, as well as creating the staffing management plan.

• Acquire Project Team – Obtaining the human resources needed to complete the project.

• Develop Project Team – Improving the competencies and interaction of team members to enhance project performance.

• Manage Project Team – Tracking team member performance, providing feedback, resolving issues, and coordinating changes to enhance project performance.

9.4 Manage Project Team

9.1 Human Resources Planning

9. Project Human Resource Management

9.3 Develop Project Team

9.2 Acquire Project Team

Exhibit 1 – Human Resources Management Processes.

Myers-Briggs Type Indicator® (MBTI®) The purpose of the Myers-Briggs Type Indicator® (MBTI®) personality inventory is to make the theory of psychological types described by C. G. Jung understandable and useful in people’s lives. The essence of the theory is that much seeming-

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Avoiding Mistakes during the Team Acquisition: Assign the Right People to Their Right Functions using MBTI

ly random variation in the behavior is actually quite orderly and consistent, being due to basic difference in the way individual prefer to use their perception and judgment. The Myers-Briggs Type Indicator® was developed by Isabel Briggs Myers and her mother, Katharine Cook Briggs, to make C.G. Jung’s theory of psychological type practical and useful in people’s lives. With MBTI® people learn to identify their characteristics, their points of strength and development and the kinds of work more adequate to their type, special abilities and expectations. MBTI® also indicates how relationships and aptness can be developed for personal as well as for organizational success. MBTI® further indicates:

• How to improve interpersonal relationships • How to improve the ability to lead with efficiency • How to deepen self-knowledge • How to create more productive and participating work teams • How to endow your organization with higher patterns of efficiency and productivity.

The MBTI ® test is applied by CPP (Center for Applications of Psychological Type) through a questionnaire with 93 multiple choice questions, tabulated through a data bank with millions of people who have already used MBTI®. The result is presented with 4 letters that picture the exercise of the individual preferences regarding perception and judgment, as follows:

• E or I – Where the person prefers to focus his/her attention (Extroverts or Introverts)

• S or N – How the person obtains information about things (Sensors or Intuitives)

• T or F – How the person takes decisions (Thinking or Feeling) • J or P – How the person guides him/herself regarding the world (Judging or Perceiving)

The Internet has a series of tests available to MBTI®, such as the sites http://www. humanmetrics.com and http://bloginality.love-productions.com .

The Sixteen Types1 Excerpted from Introduction to Type by Isabel Briggs Myers published by CPP. Inc 1

By combining the individual preferences we have the sixteen psychological Types. Isabel Briggs Myers has prepared a basic set of characteristics for each of these types, as will be presented below.

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SENSORS

Judging Perceiving

INFJ

INTJ

ISTP

ISFP

INFP

INTP

ESTP

ESFP

ENFP

ENTP

ESTJ

ESFJ

ENFJ

ENTJ

INTROVERTS

ISFJ

EXTRAVERTS

ISTJ

Judging

With Thinking

Perceiving

EXTRAVERTS

INTUITIVES With Feeling

Judging

INTROVERTS

With Thinking

ISTJ - Quite, serious, earn success by thoroughness and dependability. Practical, matter-of-fact, realistic, and responsible. Decide logically what should be done and work toward it steadily, regardless of distractions. Take pleasure in making everything orderly and organized – their work, their home, their life. Value traditions and loyalty. ISFJ - Quiet, friendly, responsible, and conscientious. Committed and steady in meeting their obligations. Thorough, painstaking, and accurate. Loyal, considerate, notice and remember specifics about people who are important to them, concerned with how others feel. Strive to create an orderly and harmonious environment at work and at home. INFJ - Seek meaning and connection in ideas, relationships, and material possessions. Want to understand what motivates people and are insightful about others. Conscientious and committed to their firm values. Develop a clear vision about how best to serve the common good. Organized and decisive in implementing their vision. INTJ - Have original minds and great drive for implementing their ideas and achieving their goals. Quickly see patterns in external events and develop longrange explanatory perspectives. When committed, organize a job and carry it through. Skeptical and independent, have high standards of competence and performance – for themselves and others. ISTP - Tolerant and flexible, quiet observers until a problem appears, then act quickly to find workable solutions. Analyze what makes things work and readily get through large amounts of data to isolate the core of practical problems. Interested in cause and effect, organize facts using logical principles, value efficiency.

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ISFP - Quiet, friendly, sensitive, and kind. Enjoy the present moment, what’s going on around them. Like to have their own space and to work within their own time frame. Loyal and committed to their values and to people who are important to them. Dislike disagreements and conflicts; do not force their opinions or values on others. INFP - Idealistic, loyal to their values and to people who are important to them. Want an external life that is congruent with their values. Curious, quick to see possibilities, can be catalysts for implementing ideas. Seek to understand people and to help them fulfill their potential. Adaptable, flexible, and accepting unless a value is threatened. INTP - Seek to develop logical explanations for everything that interests them. Theoretical and abstract, interested more in ideas than in social interaction. Quiet, contained, flexible, and adaptable. Have unusual ability to focus in depth to solve problems in their area of interest. Skeptical, sometimes critical, always analytical. ESTP - Flexible and tolerant, they take a pragmatic approach focused immediate results. Theories and conceptual explanations bore them – they want to act energetically to solve the problem. Focus o n the here-and-now, spontaneous, enjoy each moment that they can be active with others. Enjoy material comforts and style. Learn best through doing. ESFP - Outgoing, friendly, and accepting. Exuberant lovers of life, people, and material comforts. Enjoy working with others to make things happen. Bring common sense and a realistic approach to their work, and make work fun. Flexible and spontaneous, adapt readily to new people and environments. Learn best by trying a new skill with other people. ENFP - Warmly enthusiastic and imaginative. See life as full of possibilities. Make connections between events and information very quickly, and confidently proceed based on the patterns they see. Want a lot of affirmation from others, and readily give appreciation and support. Spontaneous and flexible, often rely on their ability to improvise and their verbal fluency. ENTP - Quick, ingenious, stimulating, alert, and outspoken. Resourceful in solving new and challenging problems. Adept at generating conceptual possibilities and then analyzing them strategically. Good at reading other people. Bored by routine, will seldom do the same thing the same way, apt to turn to one new interest after another. ESTJ - Practical, realistic, matter-of-fact. Decisive, quickly move to implement decisions. Organize projects and people to get things done, focus on getting results

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in the most efficient way possible. Take care of routine details. Have a clear set of logical standards, systematically follow them and want others to also. Forceful in implementing their plans. ESFJ - Warmhearted, conscientious, and cooperative. Want harmony in their environment; work with determination to establish it. Like to work with others to complete tasks accurately and on time. Loyal, follow through even in small matters. Notice what others need in their day-by-day lives and try to provide it. Want to be appreciated for who they are and for what they contribute. ENFJ - Warm, empathetic, responsive, and responsible. Highly attuned to the emotions, needs, and motivations of others. Find potential in everyone, want to help others fulfill their potential. May act as catalysts for individual and group growth. Loyal, responsive to praise and criticism. Sociable, facilitate others in a group, and provide inspiring leadership. ENTJ - Frank, decisive, assume leadership readily. Quickly see illogical and inefficient procedures and policies, develop and implement comprehensive systems to solve organizational problems. Enjoy long-term planning and goal setting. Usually well informed, well read, enjoy expanding their knowledge and passing it on to others. Forceful in presenting their ideas.

Project Management Functions Related with Psychological Types Based in the psychological types presented by the MBTI® it can be proven that in an environment of projects, each one of the types has determined inherent easinesses and difficulties to the work in projects. Based in the work of KROEGER, THUESEN, J. M. & RUTLEGE, H. (2002), can be consolidated the following aspects of each type: Workplace Contribution, Pathway to the Professional Growth, Leadership Qualities, Team Spirit and suggested PM. Jobs.

ISTJ

• Workplace

Contribution – Establishes order dutifully and steadily and works within the system to manage and complete tasks on time and under budget.

• Pathway to Professional Growth – Must learn that both organizational

change and people issues – ideas that may violate tradition – can play powerful and positive roles in organizational life.

• Leadership Qualities – Brings tasks to completion efficiently and dutifully

while maintaining respect and order throughout the group or organization.

• Team Spirit – Teams, if well managed, are a good way to distribute tasks

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and complete projects, but the important work that is done is carried out by individuals when the team meetings are over.

• Suggested PM Job – financial work, controlling, technical problem solving, individual and isolated tasks.

ISFJ

• Workplace Contribution – Offers quiet support, a sense of order, and attention to detail from behind the scenes.

• Pathway to Professional Growth – Must learn to be open to new possi-

bilities and changing situations—-this flexibility can often be the support someone most needs.

• Leadership Qualities – Produces results through one-on-one relationships and detail control and tends to perform tasks oneself rather than delegate.

• Team Spirit – Teams are worthwhile work units, vital and important structures that are yet another arena in which to provide quiet, unassuming support to the organization and its people.

• Suggested PM Job – one to one relationship, tasks with no delegation, idea generation.

INFJ

• Workplace Contribution – Turns work into a cause and injects—with quiet, serious focus – inspiration and devotion throughout the organization.

• Pathway to Professional Growth – Must learn that his or her excitement about the future and the possibilities it holds for people is often overlooked, buried as it is beneath a serious exterior.

• Leadership Qualities – Provides inspirational and visionary direction with

a moralistic or values-related spin, working with focus toward change and development.

• Team Spirit – Teams are complex human systems that need understanding and care and that, if well managed, can produce inspirational, valuable work.

• Suggested PM Job – idea inspiration, visionary tasks and project management, work well with complex situations.

INTJ

• Workplace Contribution – Provides organizations and groups with objective clarity, vision, and strategic thinking white driving toward change and improvement.

• Pathway to Professional Growth – Must learn that each idea for visionary change brings with it untold details to which someone will need to tend – the stresses brought on by visionary change are real and painful.

• Leadership Qualities – Draws energy from the complexity of future possibilities and shepherds individuals and groups through uncertainty and change with decisiveness and fairness.

• Team Spirit – Teams are powerful and complicated systems that, if well designed and managed, can play a pivotal role in bringing a group or organi-

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zation’s vision to fruition.

• Suggested PM Job – strategic problem-solving, able to work with details and with uncertainty, group organization.

ISTP

• Workplace Contribution – Solves problems practically and immediately with a calm, clear-thinking resolve.

• Pathway to Professional Growth – Must learn that the complexity of people and relationships deserves consideration and offers no quick fix.

• Leadership Qualities – Sets an example to act independently and to attend

to the needs of the short term, unencumbered by tradition, procedure, or the demands of others.

• Team Spirit – Teams are often an irritation and a diversion from effective, practical work, work that is best done alone.

• Suggested PM Job – conflict resolution with calm and control, immediate action tasks, work well alone, practical and direct project tasks.

ISFP

• Workplace Contribution – Support people and their efforts with a gentle – almost anonymous – attention to details and action in the moment.

• Pathway to Professional Growth – Must learn to focus on the patterns and problems beyond immediate concern – to look for and confront the systemic or root issues and not to get lost in the foreground, solving only the problems of today.

• Leadership Qualities – Leads by example-by tending to task details and providing gentle; unassuming support.

• Team Spirit – Teams are fine and can be fun, through they are personal-

ly draining and intrusive; quiet support and diligent works are what make teams effective.

• Suggested PM Job – hidden project tasks, tasks that must be done without reward, quiet support.

INFP

• Workplace Contribution – Holds and protects the values within which are rooted individual, group, and organizational identities —often serving as moral ballast for organizations and teams.

• Pathway to Professional Growth – Must learn to face conflict and confront it in the moment.

• Leadership Qualities – Appeals to values through personal relationships

– controlling tasks and people in such a way that those concerned do not notice they are being controlled at all.

• Team Spirit – Though teamwork is difficult and draining, collaborating and working together to pool resources and ideas is valuable and motivating.

• Suggested PM Job – team support, intuitive jobs, team moral ballast.

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INTP

• Workplace Contribution – Uses cleverness and independent thinking to problem-solve and reinvent, and in an easygoing unassuming manner prods organizational change and improvement.

• Pathway to Professional Growth – Must learn that connecting and communicating with other people is important – great solutions and ideas are adopted and enacted through personal relationships.

• Leadership Qualities – Creates and works toward a vision and a better solution and allows others to follow at their own pace and ability.

• Team Spirit – A team is okay if it allows members to enter on their own

terms and to contribute in their own way – but the best visioning and problem solving is done in isolation.

• Suggested PM Job – valorize each contribution in a team, isolated problem-solving, technical project job, financial control.

ESTP

• Workplace Contribution – Goes with the flow, adapts to the unexpected, allows variables, and delivers what needs to be delivered.

• Pathway to Professional Growth – Must learn to be patient with routines and to be aware that others may find comfort in structure, rules, and contemplation of future possibilities.

• Leadership Qualities – Keeps oneself and others on their toes by being

open and responsive to the unexpected and abandoning rules of hierarchy and tradition in the name of expediency.

• Team Spirit – Teams can be fun; however, without constant action and variation, or in dull meetings, they can run aground.

• Suggested PM Job – work well with unpredictable job, without hierarchy and tradition.

ESFP

• Workplace Contribution – Provides high-spirited energy that keeps a variety of people and actions moving in positive ways.

• Pathway to Professional Growth – Must learn to stretch to face the nega-

tive, stressful, and even hostile moments of work life is not always a barrel of fun.

• Leadership Qualities – Has a personal and often playful go-with-the-moment style that can be highly motivating to others.

• Team Spirit – Teamwork is the best way to approach any endeavor; the entire world’s a team, and only good can come from such joined efforts.

• Suggested PM Job – diverse cultural team manager, group problem-solving, global project management.

ENFP

• Workplace Contribution – Motivates and invigorates thought inspiration, enthusiasm, and unyielding attention to personal relationships.

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• Pathway to Professional Growth – Must learn to follow projects and commitments through to completion and to be aware that one’s wide mood swings can frustrate and confuse those with whom he or she works.

• Leadership Qualities – Motivates, inspires, and cajoles people to accomplish tasks and to develop both personally and professionally.

• Team Spirit – Teams are fun and energizing – especially when conflict, hierarchy, and tight time lines can be avoided.

• Suggested PM Job – team motivator, group problem-solving, optimistic team leader that believe that work together is the best work.

ENTP

• Workplace Contribution – Regards the workplace as a system to be moved,

challenged, and reconfigured so that learning is constant and worthwhile tasks are accomplished.

• Pathway to Professional Growth – Must learn to focus energy on follow-through and completion – even when one deems them boring and to remember that wide mood swings can send mixed signals.

• Leadership Qualities – Empowers oneself and others by challenging, confronting, and even taking an opposing point of view to enhance each individual’s contribution to the end result.

• Team Spirit – Teams are one more important vehicle for earning – an arena for testing ideas, discussing differences, and collaborating on results.

• Suggested PM Job – change management, change leader, conflict resolution from opposing points of view.

ESTJ

• Workplace Contribution – Drives to take charge, to see the practical facilitation of a task, and to complete it with dispatch and skill.

• Pathway to Professional Growth – Must learn to be less hard-charging by listening to and allowing alternative viewpoints.

• Leadership Qualities – Takes charge, demands 1oyalty, pushes hard to accomplish a task, and tells it like it is.

• Team Spirit – Teams are an effective tool for accomplishing tasks as long as they are well managed and people’s roles and goals are defined.

• Suggested PM Job – practical team manager, quick answer problem-solving, role management, execution manager.

ESFJ

• Workplace Contribution – Builds a harmonious environment that supports personal achievement and task accomplishment.

• Pathway to Professional Growth – Must learn to accept differences, allow them to be expressed, and recognize that conflict is not always destructive.

• Leadership Qualities – Projects inspiration and graciousness with a constant yet gentle nudge toward task completion.

• Team Spirit – Teams are good and can be productive; however, arguments and disagreements should not be tolerated.

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Avoiding Mistakes during the Team Acquisition: Assign the Right People to Their Right Functions using MBTI

• Suggested PM Job – harmonious team manager, good supporting individual tasks, do not accept intolerances from the team members.

ENFJ

• Workplace Contribution – Personally inspires and motivates all to work harmoniously for the common good.

• Pathway to Professional Growth – Must learn that not all situations need rescue and that disagreements are not personal attacks.

• Leadership Qualities – Empowers others to accomplish what needs to be done by nurturing relationships and making personal appeals.

• Team Spirit – Teams are good, people are good, and work is good when the theme of togetherness drives the task.

• Suggested PM Job – positive team leader, optimistic point of view, nurture relationships, conflict resolution from team members.

ENTJ

• Workplace Contribution – Through hard-charging arguments and action, intellectually inspires and challenges everyone to experience a vision and to move toward its fulfillment with dispatch.

• Pathway to Professional Growth – Must learn to allow time for others to develop at their own pace and level of commitment.

• Leadership Qualities – Is task-driven and demanding, with a motivational spin for everyone to get on board and move toward achieving the goal.

• Team Spirit – Teams can be good and do provide opportunities for more

involvement – as long as the task is completed and the group’s process do not slow or water down the vision.

• Suggested PM Job – intuitive team leader, intellectual leader, task driven manager.

Conclusions The aim of this article is to associate the theory proposed by C. G. Jung and the need to accomplish a judicious and well-succeeded human resources selection for the project. With the allocation of the psychological types that are aligned to the functions they will represent, the probability of turn-over is reduced, thus increasing the motivation and facilitating the development of the team. Finally, it is important to stress that there is not a better or worse type. All types have strong and weak features, and, in a project’s human resources selection, several other factors, dynamics and processes will complement MBTI® in the adequate selection and allocation of the project’s resources.

References FLANES, S. W. & LEVIN, G. (2001). People Skills for Project Managers. Vienna: Management Concepts.

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GEYER, P. (2000). The MBTI© and its use in Organizations: An Overview. Website http://members.ozemail.com.au/~alchymia/library/mbtiorg.html. KROEGER, O, THUESEN, J. M. & RUTLEGE, H. (2002). Type Talk at Work: How to the 16 Personality Types Determine Your Success on the Job. New York: Dell Publishing. MYERS, I. B. & MYERS, P. B. (1997). Ser Humano é Ser Diferente. São Paulo: Editora Gente. MYERS, I. B. (1995). Introdução à Teoria dos Tipos Psicológicos. Palo Alto: Consulting Psychologists Press. PMI (2004). A guide to the Project Management Body of Knowledge. 3rd Edition. Newton Square: Project Management Institute. QUENK, N. L.(2000). In the Grip: Understanding Type, Stress and Inferior Function. Palo Alto: Consulting Psychologists Press. VARGAS, R. V. (2004). Using Earned Value Management Indexes as a Team Development Factor and a Compensation Tool. Prague: Project Management Institute Global Congress EMEA. VERMA, V. K. (1995). Human Resource Skills for the Project Manager vol. 2. Upper Darby: Project Management Institute.

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USING EARNED VALUE MANAGEMENT INDEXES AS A TEAM DEVELOPMENT FACTOR AND A COMPENSATION TOOL

Accepted for publication at IPMA – International Project Management Association Global Congress New Delhi – India – 2005 PMI Global Congress 2004 - EMEA Prague – Czech Republic – 2004

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Abstract Related Podcasts

♫

Demystifying the Portfolio Management – Part 1/3: The Strategy’s Side http://rvarg.as/1j

♫ Demystifying the Portfolio Management – Part 2/3: The Criteria http://rvarg.as/1o

♫ Demystifying the Portfolio

Management – Part 3/3: A comparison and Decision http://rvarg.as/1s

♫ Why is so complex to be

Simple and Effective during the Project Selection? http://rvarg.as/6x

♫ Why is it So Hard to Define Clear Criteria for Selecting Projects? http://rvarg.as/6c

The objective of this paper is to present the main components of the development of a project team, the motivational characteristics inherent to the team work and an interrelation proposal between the earned value analysis and team development through the SPI and CPI indexes obtained by the tool and team development models and the compensation and reward in the project, allowing to reduce the evaluation subjectiveness of the human resource in the project. The paper presents a brief report about the team development and compensation policies, as well as an introduction to the earned value concept aiming to align the approached concepts.

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Team Development in Projects The project human resources area is one of the PMBOK Guide 2000 (PMI 2000) knowledge areas that the manager and project team have requested more attention. As reported in the Guide, the Project Human Resources Management includes the processes required to make the most effective use of human resources involved with the project. It includes all project stakeholders: sponsors, customers, individual contributors and others. The main processes are described below and Exhibit 1 provides an overview of the processes according to each project phase. Organizational Planning – identify, document and assign project roles, responsibilities and the reporting relationships. Staff Acquisition – make the required human resources be designed and work in the project. Team Development – develop individual and group skills to increase project performance. PROJECT HUMAN RESOURCE MANAGEMENT Initiating

Planning

Executing

Organizational Planning

Team Development

Controlling

Closing

Staff Acquisition

Exhibit 1 – Human Resources Management Processes distributed throughout the project phases.

These processes interact with each other and with the process in the other knowledge areas. Each process may involve effort from one or more individuals or groups depending on the needs of the project. The team development approached in this paper involves the increase of the capability of the involved parties to contribute individually, as well as the increase of people capability to work as a team. The individual growth (managerial and technical) is the basis required to develop the team becoming it crucial to the success of the projects and, therefore, becoming the key for the organization to accomplish their goals. According to FITZ-ENZ (2000), each organization and each project are led by a combination of strengths and internal and external factors. These factors are the

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ones that make the organization unique, describing collectively how and why the organizational processes influence on the performance improvement. The internal factors are the ones determined by the own organization and project goals, while the external objectives are those determined by external business environment in which the company and projects are inserted (Exhibit 2). Internal Financial $

Vision

Structure

Talent

Resources

Culture

Growth

Performance

Industry

Technology

Client

Competition

Localization

Information

Government

External Exhibit 2 – Performance change factors

According to FLANES & LEVIN (2001) performance problems that impede the team members to perform their activities successfully can be divided in:

• Problems related to technical competency • Problems related to relationship and communication • Problems related to time management and work habits Due to the above problems, it is fundamental to have an impartial and objective performance evaluation process that besides addressing the mentioned problems, it allows improvements in the individual skills, improvements in the team behavior and improvements not only in the individual competencies but also in the team ones. This impartial model directly reduces the subjectiveness of the performance evaluation and increases the team motivation according to the Adam’s Equity Theory (VERMA, 1995), as people get motivated when they are treated in equity, impartial and fair way.

Professional compensation (Reward) Some of the main tools available to increase performance are the compensation and reward policies that according to PMI (2000) are the formal actions that promote or reinforce desired behavior. To be effective, such system should make the link between performance and reward clear, explicit and achievable.

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According to PARKER, MCADAMS & ZIELINSKI (2000) the reward models are designed to create a focus on specific goals or celebrate and reward individuals or teams with diversified performance. To them, the reward models should meet individual, team and organization needs, according to the model showed in Exhibit 3. Individual

Individual

Individual Team Organization

Base compensation and Benefits

Capability

Recognition

Project Incentives

Organization unit Incentives

Atract and Retain

Develop individual skills and competencies

Celebrate outstanding performance

Measure and reward project results

Align people and organization and improve upon those objectives

� Base pay � Adjustement to base pay � Benefits � Annual bonuses

� Technical and caree tracks � Competencybased performance management

� � � �

Celebrations Spot bonuses Nomination Promotion

Team

Organization

� Managementformed team performance � Group bonuses � Self formed teams

� Formula-based incentives with preannounced performanceaward schedule

Exhibit 3 – Reward policies and bonuses (PARKER, MCADAMS e ZIELINSKI, 2000)

To SHUSTER (2000) the bonuses always need to satisfy the individual and the team. If a team satisfaction is neglected to satisfy an isolated individual, this process naturally generates dissatisfaction and demotivation. Thus, higher reward can only be gotten when high team and individual performances are achieved, as per Exhibit 4. IN DIV IDU A L

Low Performance High Performance

T E AM

Low Performance

�

No individual compensation No team compensation

No individual compensation Small team compensation

High Performance

�

Small individual compensation No team compensation

Maxim individual compensation Maxim team compensation

Exhibit 4 – Team and individual performance extent (Based on Shuster, 2000)

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Using Earned Value Management Indexes as a Team Development Factor and a Compensation Tool

Earned Value Analysis Earned value is focused on the relation between incurred actual cots and the work performed in the project in a given time period. The focus is on performance obtained in comparison to what was spent to get it. (FLEMING & KOPPELMAN, 1999). Earned value is the evaluation between what was actually spent and what was budgeted, proposing that the value to be earned initially by an activity is the value budgeted for it. As each activity or task of a project is performed, the initial budgeted value for the activity starts to constitute now the Earned Value of the project. In order to formalize the mentioned concepts based on instruction DOD (1997) and on norm ANSI/EIA 748 of the American National Standards Institute, a specific terminology was created based on forecasted and actual costs, as well earned value. The basic three elements of the earned value analysis are: BCWS (Budget cost of work scheduled) is the value that indicates the budget portion that should be spent, taking into account the activity budget base line cost, allocation or resource. BCWS is calculated as budget base line cost divided into phases and cumulative up to the status date, or current date. It is the budgeted cost. BCWP (Budget cost of work performed) is the value that indicates the budget portion that should be spent, taking into account the work performed up to the moment and budget base line cost for the activity, allocation or resource. BCWP is also called Earned Value. ACWP (Actual cost of work performed) value that shows actual costs incurred from the work already performed by a resource or task up to the status date or project current date from financial inputs. Once these three parameters are determined, the outcome analysis is obtained based on the correlation between values found for each one in a given status date (Exhibit 5).

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WS BC

Cumulative cost

ACWP

AC W P

BCWS

W BC

BCWP

Status date

Time

Exhibit 5 – BCWS, BCWP e ACWP graphic example throughout the time for a given project

The correlation among BCWS, BCWP and ACWP values allows to rate project outcomes and to proceed evaluations and future final cost forecasts. To treat the ratio among BCWP and BCWS and ACWP parameters, there are the following indexes: A) SPI (Schedule Performance Index) – Division between the Earned Value (BCWP) and the budgeted base line cost (BCWS). SPI shows the conversion rate of the budgeted value in the Earned value.

SPI =

BCWP BCWS

SPI equals 1 indicate that the budgeted value was completely earned to the project. SPI lower than 1 indicates that the project is being performed at a conversion rate lower than scheduled, that is, the financial amount scheduled to be earned in the period was not achieved and the project is late. SPI higher than 1 indicates that the project is earning outcomes in a speed higher than scheduled, i.e., it is advanced. B) CPI (Cost Performance Index) – Division between the Earned Value (BCWP) and the actual cost and (ACWP). CPI shows the conversion between the actual values spent by the project and the earned values in the same period.

CPI =

BCWP ACWP

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Using Earned Value Management Indexes as a Team Development Factor and a Compensation Tool

CPI equals 1 indicate that the value spent by the project was completely earned to the project (project in the budget). CPI lower than 1 indicates that the project is spending more than scheduled up to the moment. If CPI is higher than 1 indicates that the project is costing less than scheduled up to that moment.

Human Performance Index and Professional Evaluation Models In order to allow the team evaluation ad project professional it was developed a new index called Human Performance Index (HPI). This index consists of the relationship between the CPI and SPI allowing the creation of an index that evaluates the accomplishment of the schedule and budget of the activities executed by the resource simultaneously. In developing this paper, it was studied several types of relationship between indexes (sum, average, product, etc.), however, as the nature of the two indexes differs from the complete percentage of the project, it was chosen the composition of the indexes with complete percentage, where the participation of schedule performance index in the beginning of the project is higher than the cost performance index, while at the end of project occurs an inversion in the participation of indexes, once the SPI tends to 1 (BCWP BCWS) with the termination of the project. The resulting formula is

HPI = %C x CPI + (1− %C ) x SPI

Where CPI = Cost performance index

SPI = Schedule performance index

%C = Project complete physical percentage Exhibit 6 observes it the participation of the indexes in the HPI composition along the project.

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100% SPI

100% CPI

% Index participation

100%

Growing participation of CPI

Decreasing participation of SPI

0% 0%

Project % Complete

100%

Exhibit 6 – Participation of the indexes according to project complete percentage

From the creation of this index, it is required to evaluate the HPI´s not only the individual work outcome but also the team work in which the resource is integral part of the project as a whole, creating a final HPI that is the weighed average of these three indexes as it is presented below: HPIIndividual = % C x CPIIndividual + (1− % C ) x SPIIndividual HPI Team = % C x CPI Team + (1− % C ) x SPI Team HPIProject = % C x CPIProject + (1− % C ) x SPIProject HPIFinal =

HPIIndividual x PesoIndividual + HPITeam x Peso Team + HPIProject x Peso Project Weight Individual + Weight Team + Weight Project

Where CPIIndividual = Cost performance index of the work packages where the evaluated resource was involved SPIIndividual = Scheduled performance index of the work packages where the evaluated resource was involved CPITeam = Cost performance index of the team work packages of which the evaluated resource is participant SPITeam = Schedule performance index of the team work packages of which the evaluated resource is participant CPIProject = Cost performance index of the project SPIProject = Schedule performance index of the project

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WeightIndividual = Contribution of resource HPI in the HPIFinal WeightTeam = Contribution of team HPI in the HPIFinal WeightProject = Contribution of project HPI in the HPIFinal %C = Project complete physical percentage HPIFinal can be obtained from different strategies starting from a strong focus on individual outcomes up to a balanced focus among the individual, team and project. Follows a model of weight composition for different focuses Individual Focus Team Focus Project Focus Balanced Focus

WeightIndividual 60 20 20 40

WeightTeam 20 60 20 30

WeightProject 20 20 60 30

Exhibit 7 – Example of a proposal of weight distribution for the HPI resource composition

It is important to emphasize that the resource, team and project HPI´s are not obtained from CPI and SPI´s work packages, but from the sum of the BCWS, BCWP and ACWP’s resource activities and later from the formula application, CPI=BCWP/ACWP and SPI = BCWP/BCWS.

Example In order to illustrate the index development it has a project composed of twenty different work packages to be performed by five resources in two teams. The resources 1, 2 and 3 belong to Team A and the resources 4 and 5 to Team B, respectively. In Exhibit 8 there is the distribution of the resources in the work packages. Package Package Package Package Package Package Package Package Package Package Package Package Package Package Package Package Package Package Package Package

01 02 03 04 05 06 07 08 09 10 11 12 13 14 15 16 17 18 19 20

Resource 1 Resource 2 Resource 3 Resource 4 Resource 1 Resource 3 Resource 2 Resource 3 Resource 4 Resource 1 Resource 2 Resource 3 Resource 4 Resource 3 Resource 3 Resource 1 Resource 3 Resource 1 Resource 2 Resource 3 Resource 2 Resource 4 Resource 2 Resource 4 Resource 2 Resource 3 Resource 1 Resource 3 Resource 4 Resource 1 Resource 3 Resource 4 Resource 2 Resource 3 Resource 4 Resource 1 Resource 3 Resource 4 Resource 4 Resource 1 Resource 2 Resource 3

Resource 5 Resource 5 Resource 5

Resource 5 Resource 5

Team A Team A Team A Team Team Team Team Team Team Team Team Team

A A A A A A A A A

Team A Team A Team A Team A Team A

Exhibit 8 – Resource distribution to be used in the work packages

Team B Team B Team B Team B Team B Team B Team B

Team B Team B Team B Team Team Team Team Team Team Team Team

B B B B B B B B

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In a given moment of the project the package performance inputs were evaluated, obtaining the figure 9 with BCWS, BCWP and ACWP inputs for each work package Package 01 Package 02 Package 03 Package 04 Package 05 Package 06 Package 07 Package 08 Package 09 Package 10 Package 11 Package 12 Package 13 Package 14 Package 15 Package 16 Package 17 Package 18 Package 19 Package 20 PROJECT

%complete 25% 50% 100% 75% 25% 50% 75% 25% 100% 75% 25% 50% 25% 50% 100% 75% 25% 100% 50% 25% 42%

Budget 4.000,00 2.400,00 2.240,00 1.400,00 2.400,00 1.160,00 1.600,00 6.400,00 3.450,00 2.800,00 5.000,00 5.000,00 17.000,00 2.400,00 450,00 400,00 6.200,00 1.450,00 2.600,00 3.000,00 71.350,00

BCWS 1.250,00 1.320,00 2.240,00 1.100,00 980,00 560,00 1.450,00 1.670,00 3.450,00 2.010,00 1.230,00 2.550,00 4.560,00 1.200,00 450,00 340,00 1.810,00 1.450,00 1.100,00 820,00 31.540,00

BCWP 1.000,00 1.200,00 2.240,00 1.050,00 600,00 580,00 1.200,00 1.600,00 3.450,00 2.100,00 1.250,00 2.500,00 4.250,00 1.200,00 450,00 300,00 1.550,00 1.450,00 1.300,00 750,00 30.020,00

ACWP 1.200,00 1.000,00 2.650,00 1.260,00 980,00 550,00 1.960,00 1.920,00 4.000,00 1.950,00 1.250,00 3.000,00 4.000,00 1.350,00 450,00 300,00 1.860,00 1.350,00 1.200,00 900,00 33.130,00

Exhibit 9 – Example of data collected for a project with determined BCWS, BCWP and ACWP

From the overcome crossing of each package with resources used in them, it was obtained the HPI of each one of the resources, as well as the HPI of each one of the teams and the total HPI of the project according to Exhibit 10. Resource Resource 1 Resource 2 Resource 3 Resource 4 Resource 5 Team Team A Team B

BCWS 12.130,00 12.050,00 16.390,00 18.160,00 10.060,00 BCWS 22.950,00 25.870,00 BCWS

Total

31.540,00

BCWP 11.400,00 11.850,00 14.930,00 17.590,00 9.530,00 BCWP 21.780,00 24.720,00 BCWP

30.020,00

ACWP 13.750,00 12.730,00 17.680,00 18.720,00 9.780,00 ACWP 24.830,00 26.250,00 ACWP

33.130,00

CPI 0,83 0,93 0,84 0,94 0,97 CPI 0,88 0,94 CPI

0,91

SPI 0,94 0,98 0,91 0,97 0,95 SPI 0,95 0,96 SPI

0,95

% Complete 38,51% 47,59% 36,05% 47,11% 39,36% % Complete 43,33% 42,37% % Complete

42,07%

Exhibit 10 – Project and resource HPI outcomes

Observing that each resource belongs to a given team, there are the following individual results, shown in exhibit 11. Resource Resource Resource Resource Resource Resource

1 2 3 4 5

HPI Individual

0,90 0,96 0,89 0,95 0,96

HPI Team

0,92 0,92 0,92 0,95 0,95

HPI Project

Exhibit 11 – HPI results of each resource

0,93 0,93 0,93 0,93 0,93

HPI 0,90 0,96 0,89 0,95 0,96 HPI 0,92 0,95 HPI

0,93

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Using Earned Value Management Indexes as a Team Development Factor and a Compensation Tool

From the combination of the results of exhibit 11 with the ones of exhibit 7, there are the final HPI of each resource from the individual, team and project focus and the balance focus among the three parameters, obtaining the exhibit 12. HPI Resource Resource Resource Resource Resource

1 2 3 4 5

Individual Focus Team Focus Project Focus Balanced Focus 0,91 0,92 0,92 0,91 0,95 0,93 0,93 0,94 0,90 0,91 0,92 0,91 0,95 0,95 0,94 0,95 0,95 0,95 0,94 0,95

Exhibit 12 – HPI results of each resource

From these values it can be analyzed the outcomes of each one of the resources and its contribution for the project and team outcome, as per example, it is shown in the exhibit 13.

Exhibit 13 – Comparative graphic of the focuses among the individual, team and project for the 5 evaluated resources

In this example, it is observed that the resources 1 and 3 showed a performance lower than their team and project; the resource 2 showed a higher individual performance, however, in analyzing its team, its performance was damaged by weak performance of resources 1 and 3. Resources 4 and 5, they had high performance, increasing this way the performance of Team B. The project performance was lower than the 4 and 5 resources performance and Team B because the members of Team A lowered the global performance by their weak individual performances.

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Conclusions The main objective of this paper was to present an evaluation proposal of the human resources and teams through more direct mathematical model than the subjective evaluation by the project manager. Besides, bonuses and reward policies may be directly connected to the indexes causing more transparent mechanism of the distribution of project outcomes However, some cautions have to be taken in using this kind of evaluation. First, when the executer is not the responsible for the budget accomplishment, he/she can have his/her performance compromised by weak performance of the procurement team, as an example. Secondly, this mathematical model may not be deterministic, i.e., the only one to represent the truth of the work outcome of the project resource, once they are completely mathematical, they may not evidence subjective human aspects inside the team work.

References DOD (1997). Earned value Management Implementation Guide. Washington: United States of America Department of Defense FITZ-ENZ, J. (2000). The ROI of Human Capital: Measuring the Economic Value of Employee Performance. New York; AMACOM. FLANES, S. W. & LEVIN, G. (2001). People Skills for Project Managers. Vienna: Management Concepts. FLEMING, Q. W. & KOPPELMAN, J. M. (1999). Earned value Project Management, 2nd Ed. Newton Square: Project Management Institute. PARKER, G., MCADAMS, J. & ZIELINSKI, D. (2000). Rewarding Teams: Lesson from the Trenches. San Francisco: Jossey-Bass. PMI (2000). A guide to the Project Management Body of Knowledge. Newton Square: Project Management Institute. SHUSTER, H. D. (2000). Teaming for Quality: The Right Way for the Right Reason. Newton Square: Project Management Institute. VERMA, V. K. (1995). Human Resource Skills for the Project Manager vol. 2. Upper Darby: Project Management Institute.

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EARNED VALUE PROBABILISTIC FORECASTING USING MONTE CARLO SIMULATION

Accepted for publication at AACE - Association for Advancement of Cost Engineering 48th Annual Meeting Washington – DC – USA – 2004 Revista Brasileira de Gerenciamento de Projetos Curitiba – PR – Brazil – 2004

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Earned Value Probabilistic Forecasting Using Monte Carlo Simulation

Abstract Related Podcasts

♫

Earned Value Project Management http://rvarg.as/bk

♫ Monte Carlo Simulation http://rvarg.as/bi

♫ 4 Mechanisms for Forecast the Project Schedule and Cost http://rvarg.as/15

♫ 4 Different Indexes to Forecast the Project Final Cost Using the Earned Value Analysis http://rvarg.as/21

♫ Understanding the Difference Between Cost and Price http://rvarg.as/4x

♫ The WBS Dictionary http://rvarg.as/51

The aim of this article is to present a proposal of interconnection between models and probabilistic simulations of project as possible ways to determine EAC (Final cost) through Earned Value Analysis. The article proves that the use of the 3 main models of projection (constant index, CPI and SCI) as the basis of a triangular probabilistic distribution that, through Monte Carlo simulation will permit associate and determine the probability according to the accomplishment of budgets and costs of the project.

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Earned Value Analysis Earned Value focuses on the relation between actual costs and the work done in the project within a certain time limit. The focus is on the performance obtained in comparison to what was spent to obtain it (FLEMING & KOPPELMAN, 1999a). Earned Value can be defined as the evaluation between what was obtained according to what was truly spent and to what was planned to be spent, in which it is suggested that the value to be earned initially by an activity is the value budgeted for this activity. As each activity or task of a project is accomplished, that value initially budgeted for the activity, now builds the Earned Value of the project. In order to formalize the concepts mentioned before, based on the norm ANSI/ EIA 748 of the American National Standards Institute, a specific terminology was made up, based on data of the forecasted cost, real cost and earned value.

The 3 Elements of the Earned Value Analysis A project that will be controlled through the Earned Value Analysis needs to be planned through the management basic principles, applicable to any kind of project. Exhibit 1 evidences these management processes. Firstly, the work to be done is defined. In a second moment, the schedules and budgets are developed. The measurement and evaluation of the results of Earned Value are then, determined and compared to the planned values.

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Earned Value Probabilistic Forecasting Using Monte Carlo Simulation

SCOPE DEFINITION

SCHEDULE DEVELOPMENT AND COST BUDGETING

PERFORMANCE MEASUREMENT Data de Status

20 Linha de Base (BCWS)

10 40 20

Custo Real (ACWP)

4 20

4 6

Valor Agregado (BCWP)

6 8 12 10 10 10

Exhibit 1 – Planning and monitoring system using Earned Value Analysis (ABBA, 1998).

Likewise, the PMI (2000) shows, in its process of planning (Exhibit 2), a detailing of processes of planning according to the same steps mentioned by ABBA (1998), in which the scope definition of the project (Scope Definition – 5.3) is prerequisite for schedule development (Schedule Development – 6.4), for resource allocation (Resource Planning – 7.1) and for cost budgeting (Cost Budgeting – 7.3). Based on the conclusion of these processes, the project plan is developed (Project Plan Development – 4.1). SCOPE 5.2. Scope Planning

TIME 6.1 – Activity Definition

TIME 6.2 - Activity Sequencing

SCOPE 5.3 – Scope Definition

COST 7.1 – Resource Planning

TIME 6.3 - Activity Duration Estimating COST 7.2 - Cost Estimating

TIME 6.4 - Schedule Development COST 7.3 – Cost Budgeting

RISK 11.1 - Risk Management Planning

INTEGRATION 4.1 – Project Plan Development

Exhibit 2 – Planning processes (PMI,2000).

BCWS (Budget cost of work scheduled) is the value that points out the part of the budget that should be spent, considering the cost of baseline of the activity,

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attribution or resource. The BCWS is calculated as the costs of baseline divided in phases and accumulated until the date of the status, or current date. It is the cost originated in the budget. During the execution, the monitoring of the progress of the project is made through the comparison between the real results obtained and the ones forecasted by the project in the BCWS. In this moment, the Earned Value of the work (BCWP) is evaluated, as well as the appropriation of real costs (ACWP). BCWP (Budget cost work performed) is the value that points out the part of the budget that should be spent, considering the work done up to the moment and the cost of baseline for the activity, attribution or resource. The BCWP is also called Earned Value. The way of measurement of Earned Value, or BCWP, is directly linked to the way the project was planned. Without an adequate planning, the measurement of performance has little or no applicability. HARROFF (2000) and FLEMING & KOPPELMAN (1999) subdivide the measurement of Earned Value (BCWP) in different methods: 1. Milestone with weighted value: The control cell is converted in 2 or more marks where each one of them is defined by a partial delivery of the work, generating, consequently, a specific cost. The sum of the costs of accomplishment of each one of these marks is the cost of the item. 2. Fixed formula by CAP: It is the method that divides CAP in 2 parts that, if summed up, complete 100% of the work. In general, the most used formulas are 25/75, 50/50 and 75/25. The formula 25/75 separates the work in 2 points: the first point is accomplished immediately at the beginning of CAP (25% of costs are already accounted); the other 74% are accounted when the work is finished. The formula 50/50 points out that 50% of costs will be accounted at the beginning of the work and 50% at the end. 3. Percent complete: This method attributes to each element of a certain percent complete (between 0 and 100%) to each control cycle. This percentage is multiplied by the forecasted cost, aiming to determine the part of the budget already done. 4. Equivalent units: It is a method that calculates the Earned Value based on the units produced or made by individual elements of costs, applied in repetitive works or where the elements are defined in terms of direct consumption of resources. It is common sense in all Earned Value reports that there is not only one method able to fulfill all kinds of work. Most of the times, companies should allow the use of more than a mechanism of Earned Value calculation. In this article we decided to choose the use of percent complete as a way to determine Earned Value (BCWP), due to its popularity and user-friendliness. The

281

Earned Value Probabilistic Forecasting Using Monte Carlo Simulation

percent complete is being more used in projects because it is easy to use and it is a standard entry mechanism for earned values in many project management software. On the other hand, it brings a huge obstacle in its use, which is the strong subjectivity in its evaluation. It is influenced directly by the evaluator’s perception. Since the data entry relies on the individual perception, the percent complete method can be threatened by clients’ pressure or by management staff, and as a consequence it could harm the results obtained. In order to minimize those problems, some companies have been using internal evaluation procedures of percent complete. The use of Earned Value in projects leads to more precise estimates. The actual costs (ACWP) are measured and evaluated by the project team that is in charge of accounts payable and receivable or by the finance department of the same company. The team is supposed to report the real cost of the project until the dead line (status) in a specified accounts plan, defined by the controller’s department of the enterprise. ACWP (Actual cost of work performed) presents the actual costs resulting from the work already done by a resource or activity, until the status date, or actual date of the project, due to financial data. When those 3 parameters are defined, the analysis of results is obtained based on the correlation among the values found for each one of them in a certain status date.

WS BC

ACWP

Cumulative Cost

BCWS

AC W P

282

W BC

Time

BCWP

Status Date

Exhibit 3 – Graphic example of BCWS, BCWP and ACWP within a time length.

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Project Evaluation and Development of Projections with the Earned Value Analysis The correlation among the values of BCWS, BCWP and ACWP allows the verification of the results of the project and continue the evaluations and future projections of final costs In order to relate between BCWP and the parameters BCWS and ACWP there are the following indexes: A) SPI (Schedule Performance Index) – Division between the Earned Value (BCWP) and the planned value in the baseline (BCWS). The SPI shows the conversion rate of the forecasted value in Earned Value.

SPI =

BCWP BCWS (equation 01)

When the SPI equals 1 it means that the planned value was integrally earned to the project. When the SPI is less than 1 it means that the project is being done in a lower conversion rate than the forecasted one. In other words, the forecasted financial amount to be earned in the period defined couldn’t be obtained, and the project is late. When the SPI is superior to 1, it means that the project is earning results faster than expected, in other words, it is advanced. B) CPI (Cost Performance Index) – Division between the Earned Value (BCWP) and the actual cost (ACWP). The CPI indicates which the conversion is between the actual values used by the project and the earned values in the same period.

CPI =

BCWP ACWP (equation 02)

When the CPI equals 1, it means that the value spent by the project was integrally earned to the project (project within the budget). When the CPI is less than 1, it means that the project is spending more than forecasted until that moment. If the CPI is superior to 1, it means that the project costs less than forecasted until that moment. When CPI equals 1, it means that the project is according to the forecasted budget until the reference date. According to project forecasting, the following terminology is used: A) EAC (Estimated at Completion) – finance value that represents the final cost of the project when concluded. It includes the actual costs (ACWP) and the rest of estimate values (ETC)

EAC = ACWP + ETC (equation 03)

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Earned Value Probabilistic Forecasting Using Monte Carlo Simulation

B) ETC (Estimated to Complete) – financial value necessary to complete the project. It is calculated according to mathematical models to be presented. C) VAC (Variation at Completion) – difference between the budgeted cost (BAC) and the projected final cost (EAC).

VAC = BAC − EAC (equation 04) EAC EAC Projection Using CPI

VAC

Cumulative Cost

BAC

WS BC

ACWP

BAC

BCWS

AC W P

284

W BC

BCWP

Status Date

Time

Exhibit 4 – Earned Value forecasting of final deadlines and final costs (GEROSA & CAPODIFERRO, 1999).

Indexes Used for Projection of Project Final Costs The generic formula for the remaining estimated cost is function of a performance factor

ETC =

BAC − BCWP Índice (equation 05)

where BAC is the final budget of the final project and index is the performance index of the project.

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The performance index is determined by the combination of the Cost Performance Index (CPI) with the Scheduled Performance Index (SPI), according to what is described next, in its usual cases.

ETC through the constant deviation index (optimistic) It assumes that the rest of the work to be done by the project will be done according to the original plan and that an occurred deviation will not represent a tendency of degeneration or recovery of the forecasted budget. This estimate is commonly called the Optimistic Estimation, because, the indexes CPI and SPI are usually less than 1, therefore permanence in the plan turns out to be a good result.

Índice = 1 BAC − BCWP = BAC − BCWP Índice EAC = ACWP + ETC = ACWP + BAC − BCWP

ETC =

(equation 06)

ETC through costs performance index (realistic or more probable) It assumes that the rest of the work to be done by the project will follow the same finance performance obtained until this moment, through the costs performance index (CPI). A negative or positive tendency obtained up to the moment in terms of CPI, will project the same tendency for the final costs of the project. Since, there is a natural tendency to work with CPI indexes inferior to 1, this estimate is commonly called Realistic Estimation or more probable.

Índice = CPI BAC − BCWP BAC − BCWP = CPI Índice BAC − BCWP EAC = ACWP + ETC = ACWP + CPI ETC =

(equation 07)

ETC through future scheduled cost index SCI (pessimistic) It assumes that the rest of the work (future) to be done by the project will follow the finance projection determined by the cost performance index (CPI), as well as the scheduled projection determined by the scheduled performance index, generating the scheduled cost index SCI. This procedure aims to catch a natural human tendency of recovering the time wasted, and this try means to spend more resources to do the same work planned before. The SCI index is strongly applicable in EAC projection in case of late proj-

285

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Earned Value Probabilistic Forecasting Using Monte Carlo Simulation

ects, and with forecasted costs overspent. The product SPIxCPI makes up the strictest index in order to determine the EAC. Since there is a natural tendency to work with CPI and SPI indexes inferior to 1, this estimate is usually called Pessimistic Estimation.

Índice = SCI = SPIxCPI BAC − BCWP BAC − BCWP = SPIxCPI Índice BAC − BCWP EAC = ACWP + ETC = ACWP + SPIxCPI ETC =

(equation 08)

When the 3 ways of Estimated at completion is determined, a probabilistic model is applied in the data, in order to allow verification, in a desired reliability degree, which is the projected final cost for the project.

Monte Carlo Simulation “Monte Carlo” was a nickname of a top-secret project related to the drawing and to the project of atomic weapons developed by the mathematician John von Neumann. He discovered that a simple model of random samples could solve certain mathematical problems, that couldn’t be solved up to the moment. The simulation refers, however, to a method in which the distribution of possible results is produced from successive recalculations of the data of the project, allowing the construction of multiple scenarios. In each one of the calculations, new random data is used to represent a repetitive and interactive process. The combination of all these results creates a probabilistic distribution of the results. The feasibility of produced distribution relies on the fact that, for a high number of repetitions, the model produced reflects the characteristics of the original distribution, transforming the distribution in a plausible result for analysis. The simulation can be applied in schedules, costs and other project indexes. Mathematically the result of the simulation becomes a reasonable approximation for the original data. In an infinite number of repetitions, we could define that +∞

Re sults = . ∫ xF ( x)dx

−∞

(equation 09)

where X is the variable analyzed and F is its density of probabilities function. Since the exact determination of the integration xF(x) is rather complex, the simulations permits an approximate form of results with less complexity.

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Start

Finish

Forecast of Final Cost

Exhibit 5 – Construction of model of distribution of costs and activities or work packages making up a final distribution from random data of the project (PRITCHARD, 2001).

Execution of the Simulation The execution of the simulation assumes that all data of SPI, CPI and EAC’s have 1 In this paper all the case study data are displayed in already been determined for each activity or work package, according to what Brazilian Portuguese, as the original article. was evidenced in the project example shown as follows.

Exhibit 6 – Project example using the simulation.1

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288

Earned Value Probabilistic Forecasting Using Monte Carlo Simulation

NAME

Simulação

% COMPLETE

BUDGET

BCWS

BCWP

ACWP

41%

R$ 50.000,00

R$ 41.200,00

R$ 35.100,00

R$ 37.400,00

(R$ 2.300,00)

100%

R$ 4.000,00

R$ 5.000,00

(R$ 1.000,00)

Preparar dados

50%

R$ 4.000,00

R$ 3.000,00

R$ 2.000,00

R$ 5.000,00

(R$ 3.000,00)

Obter ferramentas

25%

R$ 6.000,00

R$ 1.500,00

R$ 3.000,00

(R$ 1.500,00)

Desenhar solução

100%

R$ 12.000,00

R$ 10.000,00

R$ 2.000,00

Comprar equipamentos teste

100%

R$ 15.000,00

R$ 13.500,00

R$ 1.500,00

R$ 0,00

Construir ambiente de testes

10%

R$ 6.000,00

R$ 1.200,00

R$ 600,00

R$ 900,00

(R$ 300,00)

Aguardar chegada equipamento

30%

R$ 0,00

R$ 3.000,00

R$ 0,00

Desenhar requerimentos

Dados de teste completos

Testar

NAME

CPI

SPI

EAC CONSTANT

EAC CPI

EAC SCI

(R$ 6.100,00)

0,94

0,85

R$ 52.300,00

R$ 62.500,00

R$ 100.100,00

R$ 0,00

0,80

1,00

R$ 5.000,00

Preparar dados

(R$ 1.000,00)

0,40

0,67

R$ 7.000,00

R$ 10.000,00

R$ 12.500,00

Obter ferramentas

(R$ 4.500,00)

0,50

0,25

R$ 7.500,00

R$ 12.000,00

R$ 39.000,00

Desenhar solução

R$ 0,00

1,20

1,00

R$ 10.000,00

Comprar equipamentos teste

R$ 0,00

1,11

1,00

R$ 13.500,00

Dados de teste completos

R$ 0,00

Simulação Desenhar requerimentos

Construir ambiente de testes

(R$ 600,00)

0,67

0,50

R$ 6.300,00

R$ 9.000,00

R$ 17.100,00

Aguardar chegada equipamento

R$ 0,00

Testar

R$ 0,00

R$ 3.000,00

Exhibit 7 – Initial basic data of simulation and determination of the 3 models of EAC (optimistic, pessimistic and realistic).

From a complete database, the function of distribution of probability is determined for the 3 EAC data, building the medium EAC as a result of distribution, as shown at table below. The function of density of probability used in the simulation will be the triangular distribution. This distribution is determined based on its minimum value, its more probable value and its maximum value. This function is probably the most direct and simplest among distributions (GREY, 1995), requiring only 3 points in its built.

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Probability

EACCPI

EAC

Cost Forecast

SCI

Exhibit 8 – Function of density of triangular probability for EAC.

Using the simulation software @Risk, we could determine the final EAC from the function RiskTriang (EAC1, EAC cpi, EAC sci), making up the results evidenced at table below. EAC CONSTANT

EAC CPI

EAC SCI

R$ 52.300,00

R$ 62.500,00

R$ 100.100,00

Desenhar requerimentos

R$ 5.000,00

=RiskTriang(E16; F16; G16)

R$ 5.000,00

Preparar dados

R$ 7.000,00

R$ 10.000,00

R$ 12.500,00

=RiskTriang(E17; F17; G17)

R$ 9.833,33

Obter ferramentas

R$ 7.500,00

R$ 12.000,00

R$ 39.000,00

=RiskTriang(E18; F18; G18)

R$ 19.500,00

Desenhar solução

R$ 10.000,00

=RiskTriang(E19; F19; G19)

R$ 10.000,00

Comprar equipamentos teste

R$ 13.500,00

=RiskTriang(E20; F20; G20)

R$ 13.500,00

R$ 0,00

=RiskTriang(E21; F21; G21)

R$ 0,00

R$ 6.300,00

R$ 9.000,00

R$ 17.100,00

=RiskTriang(E22; F22; G22)

R$ 10.800,00

R$ 0,00

=RiskTriang(E23; F23; G23)

R$ 0,00

R$ 3.000,00

=RiskTriang(E24; F24; G24)

R$ 3.000,00

NAME

Simulação

Dados de teste completos Construir ambiente de testes Aguardar chegada equipamento Testar

EAC

=RiskOutput()

Exhibit 9 – Function of density of triangular probability determined for the final EAC.

When we build the function of density of probability, the parameters of simulation are determined, as well as the number of iterations and repetitions of simulation and other information. In this article 50,000 iterations were made. The number of iterations is important to determine the quality of the results, therefore, the more iterations are made, the more the function of final density gets closer to the original functions. However, this kind of process requires a long execution time, even for fast computers that are able to make the simulation in high speed. SUMMARY INFORMATION

Workbook Name

eva.xls

Number of Simulations

Number of Iterations

50.000

Number of Inputs

Number of Outputs

Exhibit 10 – Simulation data.

EAC

R$ 71.633.33

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Earned Value Probabilistic Forecasting Using Monte Carlo Simulation

SUMMARY INFORMATION

Sampling Type

Latin Hypercube

Simulation Start Time

20/1/2004 13:26

Simulation Stop Time

20/1/2004 13:27

Simulation Duration

00:00:55

Random Seed

1102974243

Exhibit 10 – Simulation data.

Analysis of the Results After doing the simulation, the product generated is a distribution of probability of final EAC of the project, called “Simulation”, evidenced in the following exhibits.

Values in 10^ -5

290

Distribution for Simulação / EAC/N2 Mean=71633,34

5 4 3 2 1 0 50

62,5 5%

87,5

Values in Thousands 61,1023

90%

85,079

100 5%

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1,000

Distribution for Simulação / EAC/N2 Mean=71633,34

0,800 0,600 0,400 0,200 0,000

62,5 5%

87,5

Values in Thousands 90%

61,1023

100 5%

85,079

Exhibit 11 – Distribution for the final EAC of the Project “Simulation” with an interval of confidence of 90% and cumulative distribution for the final EAC of the Project “Simulation”. %TILE

VALUE

%TILE

VALUE

R$61.102,32

55%

R$71.753,30

10%

R$62.673,17

60%

R$72.871,88

15%

R$63.868,52

65%

R$74.055,95

20%

R$64.892,36

70%

R$75.366,22

25%

R$65.850,78

75%

R$76.773,88

30%

R$66.776,27

80%

R$78.350,77

35%

R$67.728,09

85%

R$80.126,48

40%

R$68.686,84

90%

R$82.236,36

45%

R$69.675,42

95%

R$85.078,99

50%

R$70.702,85

Exhibit 12 – Percentage distribution of final EAC of Project “Simulation”.

According to prior data, we can assume for sure (about 90% of certainty), for example that the projected final cost will be between $61,102 and $85,078. These intervals could be altered in order to determine more or less precision. For example, assuming a certainty of 99% regarding the values, we obtain the interval between $57,858 and $90,792, according to what is shown in the following exhibit.

291

Earned Value Probabilistic Forecasting Using Monte Carlo Simulation

Values in 10^ -5

292

Distribution for Simulação / EAC/N2 Mean=71633,34

5 4 3 2 1 0

62,5 ,5%

57,8581

Values in Thousands 99%

87,5

100 ,5%

90,7921

Exhibit 13 – Distribution for the final EAC of the Project “Simulation” with an interval of confidence of 99%.

Conclusions The use of Simulation Monte Carlo with the data of final EAC of the project, can, in an associated way, contribute to a probabilistic vision and not a deterministic vision of the final costs elaborated for the project, without the additional effort in its construction. As mentioned in the study of CHRISTENSEN (1993), there isn’t an agreement in order to define which forecast model presents the best precision and applicability. However, many studies have been made in order to compare many models for the costs estimated in a certain project or group of projects, after its conclusion, aiming to identify which models are more precise and in which phases of the project they are applicable, as well as to associate a certain type of project to a certain index. The need of estimates and costs projections is mentioned and characterized by DOD (1997) in Instruction 5000.2R in 1997 in 2 criterion. Start with an estimate area for the final cost, reflecting the best and worst scenarios.(DOD, 1997). Determine the estimate for the final cost that reflects the best professional judgment concerning costs. If the contract is at least 15% complete and the estimate is less than the calculated using the accumulated performance index, give an explanation (DOD, 1997). However, none of these studies provides a probabilistic treatment for the projects, since the most adequate final EAC for the project is no longer an isolated

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value and turns out to be a values area with certain probabilities, as suggested in this article. As a suggestion for new works, the next step will be to evaluate the results produced in the simulation with the real results of concluded projects in order to determine the precision of data obtained, aiming to produce cases associated to the simulation model applied to EMVS.

Abbreviations ACWP – Actual cost of work performed BAC – Budget at completion BCWP – Budget cost of work performed BCWS – Budget cost of work scheduled C/SCSC – Cost/Schedule Systems Control Criteria CAPs – Cost Account Plans CPI – Cost Performance Index CV – Cost variation DOD – United States of America Department of Defense EAC – Estimated at completion EMVS – Earned Value Management Systems ETC – Estimated to complete EVMS – Earned value Analysis PMBOK® - A guide to the Project Management Body of Knowledge PMI – Project Management Institute SCI – Scheduled Cost Index (SPIxCPI) SPI – Scheduled Performance Index SV – Scheduled variation VAC – Variation at completion

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Earned Value Probabilistic Forecasting Using Monte Carlo Simulation

References ABBA, W. F. (1998). Defense Acquisition Reform and Project Management. Long Beach: 29th Annual Project Management Institute Seminars & Symposium. CHRISTENSEN, D. S. (1993). Determining an Accurate Estimate at Completion. Vienna: National Contract Management Journal. DOD (1997). Earned Value Management Implementation Guide. Washington: United States of America Department of Defense FLEMING, Q. W. & KOPPELMAN, J. M. (1999). Earned value Project Management, 2nd Ed. Newton Square: Project Management Institute. GEROSA S. & CAPODIFERRO C. (1999). Earned value Management (EMV) Techniques form Engineering and Prototype Production Activities. Philadelphia: 30th Annual Project Management Institute Seminars & Symposium. GREY, S. (1995). Practical Risk Assessment for Project Management. West Sussex: John Wiley & Sons. HARROFF, N. N. (2000). Discrete Versus Level of Effort. Milford: NNH Enterprise. PMI (2000). A guide to the Project Management Body of Knowledge. Newton Square: Project Management Institute. PRITCHARD, C. L. (2001). Risk Management: Concepts and Guidance. 2ª Ed. Arlington: ESI International. SCHUYLER, J. R. (1994). Decision Analysis in Projects: Monte Carlo Simulation. Upper Darby: Project Management Institute.

EARNED VALUE ANALYSIS IN THE CONTROL OF PROJECTS: SUCCESS OR FAILURE?

Accepted for publication at AACE - Association for Advancement of Cost Engineering 47th Annual Meeting Orlando - Florida – USA – 2003 PMI College of Performance Management Measurable News Magazine Arlington - Virginia – USA - 2003

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Earned Value Analysis in the Control of Projects: Success or Failure?

Abstract Related Podcasts

♫

Earned Value Project Management http://rvarg.as/bk

♫ Earned Value Management

System – Critical Success Factors http://rvarg.as/bh

♫ Success Factors in Project Control http://rvarg.as/3y

♫ 4 Mechanisms for Forecast the Project Schedule and Cost http://rvarg.as/15

♫ 4 Different Indexes to Forecast the Project Final Cost Using the Earned Value Analysis http://rvarg.as/21

The objective of this paper is to present and discuss the main obstacles and benefits of the use of the Earned Value Analysis in projects, including factors to be improved and implemented during the project plan and actions to be taken while the project is accomplished and controlled. Also, through a real case study in civil construction field, the applicability of the technique is faced with a theoretical reference, in order to identify aspects of the applicability of the tool tested in case study. The results to be presented and discussed are subdivided in two parts: The first one is about the different features of each business and its contribution for the success or failure in the implementation of the Earned Value Analysis, and the second is about the characteristics of similarity found in all businesses that together favor or not the use of the tool.

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Earned Value Analysis as a Control Technique Many studies about the applicability of the Earned Value Analysis have been made. THAMHAIN (1998) tried to evaluate the popularity of different practices of project management. Surveys were made with 400 professionals who work with projects (managers, directors, people in charge) in 180 projects in Fortune-1000 companies. They were asked about the popularity and value of different techniques of performance evaluation. As a result, he could see that the Earned Value Analysis is used by 41% of people who work with projects. It is more used than critical path method, QFD (quality function deployment) and Crashing, among others. The Earned Value Analysis is almost as popular as the net PERT/CPM. Concerning the value of the technique, the results found for Earned Value Analysis are fitted in a layer of little value, staying below practically all techniques analyzed, what infers that the popularity of the technique doesn’t seem to show its applicability or value. Trying to justify the low value proved by researchers, THAMHAIN (1998) states that little applicability found as a result in the studies made, can be attributed to different barriers, either being internal or from the environment. They are

• lack of comprehension of how the technique works; • anxiety concerning the adequate use of the tool; • use of the tool requiring a lot of work and time consumption; • tools trimming creativity in the use of other strategies; • inconsistency of the tool in managerial procedures / businesses processes; • method of control as threat, concerning the freedom of the team; • vague and inaccurate purpose and its benefit; • high cost of its implementation; • unsuccessful prior experience in the use of other techniques; • low familiarity with the technique. WIDEMAN (1999) states that a project of great importance requires a unit of planning and control that has professionals capable of collecting the information and making the Analysis of Added Value, turning its applicability justifiable. CHRISTENSEN (1998) states, in his studies about the applicability of Added Value in govern organizations in the United States, that the implementation of Earned Value requires a cultural change, which demands time and effort. This means to make sure that policies and knowledge are taught by the organization and by the project in order to quicken the work of the ones involved. To Sparrow (2000), the Earned Value Analysis enables a supplementary value to the project because it offers a premature visibility of its results, in other words,

297

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Earned Value Analysis in the Control of Projects: Success or Failure?

it is possible to determine a tendency of costs and deadlines of the project in a certain phase of it, when there is still a possibility of implementation of corrective actions. On the contrary, WEST & MCELROY (2001) agree that the Earned Value Analysis is an adequate tool for the generation of reports of work done, and not a managerial tool, since the control in real time of the project, using all parameters of analysis becomes unviable: “the Earned Value Analysis shows to the project team the performance obtained until then, and not the future forecast of the project.” WIDEMAN (1999) supports that the technique is conceptually attractive, however it requires great efforts in its maintenance, therefore it needs a qualified team to understand and provide reliable information. He also states that many project managers don’t consider the analysis an appropriate cost-benefit ratio. From those opposite points of view, we may imply that the Earned Value Analysis is a group of powerful intrinsic characteristics, wide and varied, like payment projection and forecasting. However, it is bound to find great difficulty in either data collection or in the low speed of information generation. These considerations may mean that, if the data collection is made in adequate speed and accuracy, and the information is correctly compiled accomplishing the deadlines, the analysis has its applicability widely enlarged. Otherwise, it will not add much to the process of project control. TERREL et al (1998) states that, in order to make the Earned Value Analysis effectively implemented, it is necessary to have the information about the resources clearly defined. A failure in obtaining these data, motivate the creation of inaccurate performance measurement baseline (PMB), distant from the real scenario. FLEMING & KOPPELMAN (1999) state, also that another difficulty factor is about an adequate work breakdown structure (WBS). If the work is subdivided in small packages of work, it will represent a high cost of control and a lot of paperwork. On the other hand, a badly stratified subdivision may represent an inaccuracy of data, concerning real costs and deadlines. This confirmation may be proved in the low application of the Earned Value Analysis in technology and marketing areas, where the creative work is the variant in a scope previously defined, making its application limited and directly related to the stability of a defined scope, according to PETERSON & OLIVER (2001). They state that, the more short-term projects grow, with reduced team and a generically defined scope, the more the Earned Value Analysis, according to Instruction 5000. 2R (DOD, 1997) and by ANSI/EIA 748, is not viable, due to inaccurate

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projections, consequence of a badly defined scope and to high costs noticed by the entrepreneurs.

Case Study The company researched belongs to a sector of civil construction, which applies project management in a steady way. It is a segment that most invests in researches and new tools in this field. Furthermore, it is the only one on the market that admits publicly the use of Earned Value Analysis in its process of construction control. This company is the 11th in the national segment of heavy construction. It is also part of the three biggest groups in the segment in the country and a leader in other segments of the economy, such as government work and telecommunications. It has been on the market for more than 50 years. Its turnover in 2001 was about US$200,000,000 with 80 engineers and about 4000 workers in its personnel. Concerning the case study, everything started through a process of interview in which three professionals from the southeast business planning unit of the company, gave their opinion about the process in a wide and clear way. These professionals were the first ones to be interviewed, because they have developed the construction control system using the Earned Value Analysis. After analyzing the issues discussed by the first interviewees, a new series of interviews was made, based on a new open line of interviews, with construction managers and the ones in charge of planning departments of each construction site. At this moment, the objective was to test the planning practices, analysis and gathering of data in different levels, aiming to find possible distortions, conceptual flaws, resistance and work style in the use of Earned Value Analysis in the construction sites. In a third and final moment, the final result of the projects (deadlines and costs) was faced with the values computed for the Earned Value Analysis that were under the responsibility of the planning unit of the company. From that combination between interview results and the data available in the company, we tried to get evidence that could link the success or failure of the business to maturity in the use of Earned Value. Concerning the evaluated construction sites, they were categorized by the company as medium-sized and average technique complexity constructions, which will be finished by the end of this work.

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Results The results to be presented and discussed in this section are about different characteristics of each construction site and its contribution to the success or failure in the implementation of the Earned Value Analysis. The second part is about the traces of similarity found in the construction sites and the general conclusions about the case study. The results obtained in the process of interview and the combination of real data provided by the planning department, as seen before, were significantly different in each business. From these different characteristics (Table 1), the main factors are presented and evaluated in each construction site, just like the final result concerning the fidelity of the results and the preliminary conclusions. The factors evaluated have been presented in the discussion and analysis of each construction site, and they are: scope, deadlines and schedules, budget process, type of contract, type of client, partners and/or consortiums, organizational support, support by the client, geographic distribution of work, the presence of outsourced staff, use and knowledge of the indexes and models of projection provided by the tool. As we can see, after analyzing table 1, we may conclude that the implementation of construction site one was successful and of construction site three was a failure. Both results are not necessarily product of an isolated factor, but from lots of linked factors. CHARACTERISTIC

CONSTRUCTION SITE ONE

CONSTRUCTION SITE TWO

CONSTRUCTION SITE THREE

Scope

Clearly detailed and specified

Defined preliminary, detailed as the project is done

Detailed and specified satisfactorily (not in the same level of construction site one )

Deadlines and schedules set up

Closed and previously defined according to clear schedules to be followed

Closed, but with significant Open with schedules to be changes throughout the defined while duties are project, result of problems accomplished linked to lack of financial funds.

Budget process

Structured through CAP’s allowing the adequate use of the tool

Traditional, not based on the Analysis of Added Value

Type of contract

Fixed price. Not adjustable One price

One price

Type of client

Private

Public

Traditional, not based on the Analysis of Added Value

Table 1 – Comparative analysis of the main characteristics of three construction sites evaluated.

ricardo-vargas.com CHARACTERISTIC

CONSTRUCTION SITE ONE

CONSTRUCTION SITE TWO

CONSTRUCTION SITE THREE

Partnerships and/or consortiums

Nonexistent

Consortium with another contractor that has the construction being controlled by the company evaluated

Organizational Support

High

Moderate to low

Moderate

Support by the client

High

Moderate

Moderate to low

Geographic distribution of work

Concentrated geographically

Distributed in 150Km of groups of work

Presence of outsourced staff

None

High

Use of knowledge of indexes and models of projections provided by the tool

Moderate to low

Low

Loyalty to official results presented with real results

High

Moderate to low

Low

Preliminary conclusion about the success of the implementation

Partial success

Partial failure

Failure

Table 1 – Comparative analysis of the main characteristics of three construction sites evaluated.

It is not possible to conclude that the unsatisfactory results found in construction site three are linked to the existence of a consortium or to an inadequate geographic distribution of work. This failure is a consequence of a group of many unfavorable characteristics, which contributed to unexpected results. On the other hand, evaluating construction sites one and two, we find characteristics in construction site two that are significantly closer to those in construction site one. However, the main difference found was the lack of detailing of scope, which consequently made the budget process unsatisfactory. We may conclude that it was one of the main factors that made the results different (see Table 1). Similarities have been found in all three projects evaluated. They are related to the process of interview and obtained results. Initially, we may conclude that there are factors linked to the organizational structure and to the management model of the company that could affect directly the fidelity of results. Therefore, it is necessary to investigate deeply about the influence in organizational structure in the use of the tool. Also, concerning organizational aspects, the ones in charge of construction sites planning, questioned the need of a high number of indexes, and mentioned that the determination of performance indexes was redundant.

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A preliminary evaluation of these considerations, allows us to conclude, at first, that the Earned Value Analysis as we can see in all of the three projects, may show a high use of excessive indexes, superior of the management potential of a construction site. That could make the construction site impossible, result of low priority in the process and unnecessary use of indexes. The opinion of the interviewees about the real value of the tool was unanimous. They all agree that the Earned Value Analysis is a great step for the improvement and introduction to a more modern mechanism of construction site control. This would confirm the first result of THAMHAIN’S (1998) study about how popular the technique is, however, in the same study (THAMHAIN, 1998), they all agree that the conditions of market and the necessity of a quicker generation of results, made the dedication to the use of the tool, difficult, proving the low value of the technique presented in the study of THAMHAIN (1998). According to prior quotations, we can also see that in all three cases, there was an active participation of the planning departments of the business unit. However, this participation was different in each construction site. We can imply that the success of construction site 1, concerning the implantation of the tool, is directly linked to a strong presence of the professionals of planning department. Therefore, we may conclude that, very often, the efforts of the construction site team are divided in many different groups, and the team dedicates to the tool, directly used by top executives to evaluate the businesses. As a result, the dedication to other competitive tools is ignored or left behind, as seen in Earned Value Analysis in projects two and three. In construction site one, this process inverted. In this project, the Earned Value Analysis was the main mechanism of construction site control. It gathered all the necessary support to be successful. Finally, we may conclude that the case study proved the characteristics of the projects that favor the use of the tool and the use of others which show to be difficult in the process. It was also proved that the implementation of the Earned Value Analysis is a complex process that involves several aspects, since the kind of business until its organizational structure, its scope, its geographic distribution and the relationship with the client, among others, that deserve a more detailed study.

Conclusions From this paper, we may conclude that the Earned Value Analysis is a powerful tool in the control of performance evaluation. However, most of the projects have insufficiently detailed scope, staff with little experience in the use of the tool

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and a natural dissociation in the control of costs and deadlines. These elements make the results questionable to a necessary effort. We may also conclude that the results are not very obvious in short-term basis. They will only be evidenced in future phases of the project, especially in cost reduction of operations and rework. As a third conclusion, we may see that, in projects of clearly defined scope, or in contracts with price and work established, the Earned Value Analysis shows a favorable cost-benefit ratio. Other element that might favor the application of the tool is the qualification of project teams in the use of the tool and the organizational support, which allows the tool to be simplified to meet the specific needs of the project and the organization. To sum up, the combinations of the conclusions obtained from the analysis of the theoretical reference, with the case study are shown as follows. Nature of the project – the application of the Earned Value Analysis can be considered more successful in projects of clear and tangible objectives, with a detailed scope, simple and direct. This type of project presents better results in the use of the analysis, as evidenced in the case study (construction site one). Projects with incomplete final products or services, or projects that involve aspects of creativity that make a precise plan impossible, show high inviability in the use of the technique. Since the planning has not been established, the date of performance can be determined (construction sites two and three). Scope definition – from empirical evidence obtained from the evaluated construction sites and based on theoretical discussions presented in the theoretical reference, we may imply that the facility or difficulty concerning detailing and specification of scope, permits the tool to be favored or disfavored, since a tangible scope, controllable and detailed provides better specification of the work to be made. Consequently, it facilitates the process of measurement of real and added values. The establishment of tangible, controllable and detailed scope is a process that comes from the nature of the project and from the model of business established (contract, type of client, etc). Therefore, a strong dedication to the process of development of the scope improves results in the use in the Earned Value Analysis. Informality in management and resistance to changes – we can see in the case study that the informality in the control of project is high, and resistance found in the implementation of a new model of control exists and cannot be ignored. This resistance is associated to a perception that the planning work and control rise in an unjustifiable way, when using the tool. Trying to associate both factors, ANT-

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VIK (1998) states that the resistance comes from a cultural process of informality in the control of projects. In this way, it is implied that it is necessary to create a different work of management of changes, for example the training of management of projects, workshops, an efficient support of the professionals of this area, with prizes and bonuses. Everything aims to minimize the resistance found in the implementation and to favor the project environment. Attractively and value of the technique – Based on the evidence of the case study and on researches presented on theoretical reference, we can say that the Earned Value Analysis is considered by the ones who have already used it or known it, as attractive and complete. However, this fact is not true, concerning the use of the technique. The interviewees and several authors that were cited and in this theoretical reference agree that the technique demands a strong effort that, if not analyzed widely, should not have good results. This consideration justifies the results presented on the research of THAMHAIN (1998). Training – The Earned Value Analysis suggests a cultural change in the process of projects control, therefore people who have experience in dealing with the tool are really necessary in this process. Moreover, it is also necessary a process of intense training, in order to reduce the resistance to its implementation that comes originally from a low technical knowledge of the tool. Organizational support – The way an organization implements the tool, influences directly the results. As seen empirically in the case study, the construction site one, that had an organizational support, provided by specialized resources, had better results in terms of application. However, the organizational support, has a cost that has to be determined and accounted, otherwise the obtained results might be distorted.

References ANTVIK, L. C. S. (1998). Earned value Management – a 200 Year Perspective. Long Beach: 29th Annual Project Management Institute Seminars & Symposium. CHRISTENSEN, D. S. (1998). The Cost and Benefits of the Earned value Management Process. Acquisition Review Quarterly. DOD (1997). Earned value Management Implementation Guide. Washington: United States of America Department of Defense FLEMING, Q. W. & KOPPELMAN, J. M. (1999). Earned value Project Management, 2nd Ed. Newton Square: Project Management Institute.

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PETERSON, C. D. & OLIVER, M. E. (2001). EV-Lite – Earned value Control for Fast Paced Projects. Nashville: 32th Annual Project Management Institute Seminars & Symposium. SPARROW, H. (2000). EVM = Earned value Management Results in Early Visibility and Management Opportunities. Houston: 31st Annual Project Management Institute Seminars & Symposium. TERREL, M. S., BROCK, A. W., WISE, J. R. (1998). Evaluating Project Performance Tools – A Case Study. Long Beach: 29th Annual Project Management Institute Seminars & Symposium. THAMHAIN, H. J. (1998). Integrating Project Management Tools with the Project Team. Long Beach: 29th Annual Project Management Institute Seminars & Symposium. WEST, S. M & MCELROY, S. (2001). EVMS: A Managerial Tool vs. a Reporting Tool. Nashville: 32th Annual Project Management Institute Seminars & Symposium. WIDEMAN, R. M. (1999). Cost Control of Capital Projects and the Project Cost Management Systems Requirements. 2ª ed. Vancouver: AEW Services e BiTech Publishers.

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A New Approach to PMBok® Guide 2000

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