INDUSTRIAL ENGINEERING SERIES – Supply Chain Management by Sciencenetwork

INDUSTRIAL ENGINEERING SERIES – Supply Chain Management Volume 1

Publisher: Science Network ISBN: 978-0-9869554-9-5 First published in January, 2014 Printed in Canada

A free online edition of this book is available at www.sciencenetwork.ca Additional hard copies can be obtained from reprint@sciencenetwork.ca

INDUSTRIAL ENGINEERING SERIES

Supply Chain Management Volume 1

ď&#x192;Ł Science Network Online Open Access Publisher

Table of contents Supply chain descriptive modeling Abstract Introduction 1- Defining Modeling 2- Supply Chain Modeling 3- Supply Chain Modeling Framework 3-1 Classifying Models 3-2 Descriptive Models 3-3 Simulation Models 3-3-1 Forecasting Models 3-3-2 Cost-related Models 3-3-3 Referential Models Conclusion References

1 1 2 3 4 4 6 7 10 11 12 14 17

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Supply chain descriptive modeling Martha Helena Carrillo Ramirez School of Engineering, Pontificia Universidad Javeriana E-mail :

mcarrill@javeriana.edu.co

his book aims at analyzing current supply chains modeling in order to have an overview of its principal elements, as well as to identify its basic concepts. There will be a special emphasis on descriptive modeling due to fact that it is considered as the basis for other types of modeling.

Introduction Unsteadiness and uncertainty are crucial variables to consider in business. Continuous changes at political, economic, social and environmental levels force organizations to adapt really fast in order to be able to stay in the market. In addition, during the last few years the world have witnessed how complex, multidimensional and globally distributed networks have grown as a result of supply chain expansion. All this presents challenges at the management level because organizations require even more demanding levels of coordination, collaboration and integration than before.

As a way to help entrepreneurs, researchers have done important contributions to the field of modeling by analyzing all its phenomena at strategic, tactic and operative levels; as well as by finding the most adequate forms to analyze the processes of a complex organization (Kanda and Deshmukh 2008). According to Meixel and Gargeya (2005) and Ding et al (2000), there has been a lot of research on this field and a significant growth of published articles over the last decade.

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The process of modeling is crucial to understand any system since it offers conceptual approaches to its elements, its characteristics and to the dynamics of its behavior. More specifically, descriptive modeling aims at being the most accurate representation of reality. By using any of the different types of approaches to the subject, it allows to study reality in an easy and flexible way in order to propose different scenarios regarding its dynamic or static behavior. Some of the tools that can be used for descriptive modeling are conceptual theories, simple mathematic analysis and single or combined computer data processing.

To sum up, descriptive modeling plays an important role to represent reality, and users agree on a common language to use for it (Loucopoulos and Kavakli, 1999). Furthermore, it allows to analyze complex uncertain systems which are not easily reflected by other types of modeling (Chan and Chan, 2005) (Stefanovic and Stefanovic, 2008).

1. Defining Modeling There are different ways to model like mathematic equations, graphs, drafts, descriptive schemas, and in general, any type of used methodology to represent reality. These mechanisms, in their most basic forms, have been used since old times as ways to represent human problems faced on a daily basis.

Industrialization gave birth to models that help in the analysis of challenges related to mass production, and since then, new applications for modeling have

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arisen every day to support the development of production and distribution processes.

A model can be defined as an abstract representation of a system that allows us to make decisions on it. Due to the complexity of the system, it is hard to make out it completely and therefore the proposed model will show some differences. However, this should not be interpreted as an disability to make predictions on the behavior of the system itself (Camarinha-Matos and Afsarmanesh, 2007).

According to the majority of the researchers in the modeling movement, a model is a logical system itself with several characteristics (Loucopoulos and Kavakli, 1999): -

It is explicit and precise so it can be used several times.

It defines the problem and its restrictions.

It presents a solution to the problem.

It limits the context and the frequent situations in which the model can be applied.

It has to be identifiable which means that all possible users should be able to interpret it.

2- Supply Chain Modeling As mentioned above, business modeling was born with industrialization as a useful tool to register processes that were more and more complex. This was developed in the 50s when scholars were focused on mathematic modeling, simulation and optimization applied to computer systems that helped deeper analysis in larger non-lineal models, and avoided time consumption in manual procedures (Klatt and Marquardt, 2008)

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In the 90s thanks to Michael Porter and his contribution to value chains, value and supply chain modeling were born as a logical consequence. A supply chain holistic model includes all constituents and their typology; interrelations that define collaboration levels; stakeholders in all processes involved; technology support, and used business strategies (Angerhofer and Angelides, 2006).

3- Supply Chain Modeling Framework According to Magee and Weck (2002), a complex system counts on several components, interconnections, interactions or interdependencies which are difficult to describe, understand, predict, manage, design and change. Supply chains can be considered complex systems due to the fact that they comply with all these conditions since they have multiples entities, processes, products, installations, people, costumes and languages, interrelations, flows and variables.

Models do not have the entire necessary scope to achieve total representation. Therefore, it is convenient to appeal to several models and to several modeling methodologies in order to approach the basic qualities that could define a specific chain.

3-1-

Classifying Models

There have been different proposals for classification in order to make analysis and application of supply chain models easier. The first proposal considers as a distinction criterion the decision period in which the model is applied so three groups can be obtained. The Strategic one is a far-reaching group because the period of time considered in the analysis takes more than one year. This group generally works with incomplete and inaccurate information, and sometimes with forward-looking statements.

The Tactic group takes a year or less and the

Operative one is daily or hour-based. The latter works with accurate information since it is the result of executed or about-to-be executed transactions (Ballou, 4

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2003). Each group has different problems to deal with and therefore the models involved vary.

The following chart summarizes the most common questions that each type of modeling proposes according to Min and Zhou (2002). Chart 1: Types of modeling according to their time horizon Planning level

Time horizon

Types of problems

Strategic

1- 5 years

Installation placing, assessment of market demand, distribution channels, strategic alliances, development of new products, outsourcing, suppliersâ&#x20AC;&#x2122; selection, TIC, network structure.

Tactic

12 months

Inventory control, production and distribution coordination, plant layout.

Operative

weeks - days

Routes programming, workforce programming.

Economic Quantity Order (EOQ) for inventories is an example of Operative model. It is a quantitative model that aims at determining the ideal quantity to ask a supplier so orders have the lowest cost. Another example is the search for the most appropriate network structure of outlets, in which case it would be more appropriate to use simulation. This example corresponds to the Strategic type.

A second classification of models emphasizes on the kind of variable that is analyzed. There are 4 types of models to consider: probabilistic, deterministic, hybrid and Information Technology â&#x20AC;&#x201C; IT models (Min and Zhou, 2002). For instance, problems related to covering a supermarket demand are probabilistic while a monthly order on stationary items for a specific number of employees at an office could be deterministic, although not always. Hybrid models are the most sensible to reality since they understand current complexity and mix both types of variables. In regards to the IT models, they are aimed at integrating and coordinating several supply chain planning stages using software to process information in real time. 5

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A third classification proposal considers the specific characteristics of the model as a basic criterion, as stated by Nienhaus et al (2003). This classification divides the whole group of models into two subgroups: Pure Modeling Techniques (Flow chart) and Integrated Modeling Approach. The latter in turn consists of several groups: Visualization, Reference, Simulation, and Optimization. Finally, the Reference group is divided into Process, Performance, Task and Procedure.

The fourth and last considered proposal is easy to understand and is based on Shapiroâ&#x20AC;&#x2122;s principles (2004). It nearly covers the whole range of possible approaches to the subject of value chain: -

Descriptive Models: These types of models are used to understand the general supply chain operating mechanism. Qualitative, forecasting and cost-related, referential or conceptual, and simulation models are also included in this category.

Prescriptive Models: They are also called normative or analytical by some authors.

They

mainly

consist

optimization

and

mathematic

programming models. They are constructed from descriptive models.

It is important to consider that normative approaches in general have to be simplified in order to reflect a complex business reality by means of mathematical expressions. Thus, if more detailed models are desired, descriptive models should be used. These will be described as follows.

3-2-

Descriptive Models

Descriptive models aim at being the most accurate representation of reality. They allow to study reality in an easy and flexible way in order to propose different scenarios. This means that some of the tools that can be used for descriptive modeling are conceptual theories, simple mathematic analysis and computer data processing, single or combined.

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Identifying what happens in a company from the descriptive point of view is very useful to get a better understanding of a system, and consequently, to detect abnormalities and suggest enhancement.

Different kinds of descriptive models such as simulation, forecasting, cost-related and referential will be analyzed as follows.

3-3-

Simulation Models

It is just over the last decade that a great number of simulation tools to analyze supply chains have been developed. Some of them have been constructed and used in a single organization, and some others have been offered to the business sector (Van der Zee and Van der Vorst, 2005).

Simulation can be defined as the process in which an abstract model is designed from a real system and then experiments are made with that design in order to understand the behavior of the system in different circumstances. This helps reducing operational risks when implementing it (Stefanovic and Stefanovic, 2008). In other words, once the conceptual model of a process is developed, it is possible to achieve its systematic reproduction by means of programming. The process of implementing this program under certain preliminary specifications, given to represent the real system, is called simulation (Lee et al, 2002). A simulation should be flexible, parametric1, and should have a short time of processing in order to achieve an optimal performance (Longo and Mirabelli, 2008). There could be two types of simulations: stochastic which implies random behaviors; and deterministic which assumes that there are no random effects involved (Shapiro, 2001).

It should allow its evaluation in different scenarios.

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Another classification considers simulation as static when it involves conditions that do not suffer variations over time but that are given in a specific moment or dynamic when there are permanent changes. An example of static simulation is the design of a distribution center plant since it corresponds to a specific instant and permanent changes are not expected.

The last classification, which is very common, indicates that simulation can imply continuum or discrete behavior variables. The former supposes that variables change over time, constantly, the latter assumes that changes occur every specific time-cycle. Recent research has proved that most of the problems in supply chains are solved using discrete-type of models (Lee et al, 2002).

Using simulation as an analysis tool in supply chains is not a difficult decision to make. Discrete simulation models can analyze the stochastic behavior along the chain and thus make the study of queuing and other operation and transport phenomena easier (Persson and Olhager, 2002). Given that quantitative models do not enable to manage the whole dynamic of a supply chain, simulation is needed when complex chains are being analyzed (Stefanovic and Stefanovic, 2008). Below are detailed the necessary steps to achieve a supply chain simulation. As mentioned before, once you have the conceptual model, it is possible to proceed with simulation, if previous optimization has been considered. The last step is making decisions on the final design (Lee et al, 2002).

a. Network Configuration b. Optimization Module c. Simulation d. Analysis and Decision-Making e. Robust Design

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Nowadays there is a tendency to construct simulation software designs that emphasize on tactic and operational aspects. These designs define the supply chain as a group of intelligent agents that are responsible for one or more activities. They interact with other agents in planning and implementing the activities. These designs are characterized by their emphasis on representing interactions through basic physical flows, especially through product flow (Van der Zee and Van der Vorst, 2005).

There are multiple languages used for this programming and for the obtained programs, which seems obvious due to the complexity of the supply chain. Recent development on the subject consists of using distributed simulations which are implemented in different computer platforms and which interact on a unique network. This permits more complex simulations but requires standards that guarantee compatibility among programs. The latter are called High Level Architecture or HLA.

Some of the most recognized programs in our current context are (Crosbie, 2000): The IBM supply chain analyzer 2 The SDI 3 Industry Product Suite Compaq CSCAT Supply Chain Analysis Tool System Modelling Corporation. ARENA discrete simulation software Tailored Clothing Technology Corporation with its corresponding Sourcing Simulator Ziegler and others who developed The Discrete Event System Specification4

Even though several simulation tools for chains have already been developed, still there is a broad opportunity for proposing new improvements or applications 2

SCA Simulation Dynamics Inc. 4 DEVS 3

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(Chatfield et al, 2006). For instance, during the last few years, some researchers have focused on mix discrete-continuum simulation that approach to the complex reality that characterizes these types of systems (lee et al, 2002). On the other hand, given the variety of our current contexts, there are new challenges and requirements every day. This is the case for integral information flow structures that provide better visibility along the chain.

3-3-1- Forecasting Models Forecasting models are a valuable planning tool since they enable agents to foresee the future and operational actions adequately in order to develop capacity plans, cash flows, purchase processes, production, service levels and count on a flexible but consistent basis that lets them adapt to the different context realities (Rocha dos Santos et al, 2006). Based on a combination of historical facts, on management judgment and on modeling expertise, forecasting models allow to do a tentative advance on how the behavior of a given type of data will be seen along time and in a variety of scenarios. These models are obviously needed when constructing supply chain models.

Time series are a type of forecasting models which look for patterns to foresee the future based on historical data. Regressions and econometric methods are also types of models which search for causal relations or at least explanations underlying independent and dependent variables.

In this sense, there is a

modeling field not yet implemented in which it is possible to create forecasting models by means of integral data relation. These models consider internal decision variables besides the commonly used for sales forecast at the external level. An example of internal variable is the defined price for the product (Shapiro, 2001).

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3-3-2- Cost-related Models Cost-related models provide structures to integrate data and make decisions generally by means of using databases (Shapiro, 2001). They are a tool that permits chief executive officer (CEOs define the financial impact of different items in the budget related to costs and expenses of the company, and to keep legal and internal control records (Shank and Govindarajan, 1998).

According to Gupta and Gunasekaran (2005), cost-analysis has evolved over time. The average cost focus was used in the pre-industrial era while the fixed-cost and variable-cost were used in the 80s, as well as opportunity cost and transfer pricing. In the 90s, ABC5 costing arises and costing studies begin related to the product lifecycle and JIT6. Finally, over the last decade the focus has been on value creation.

Modeling opportunities have arisen from every period of organizational development due to the fact that they imply different company needs. Today, the focus is on value creation along the whole chain. This implies moving from a partial vision of internal production costs to a new paradigm in which the price calculation considers all the direct and indirect involved elements. As an example of this, in a CEOs salary, the relation to the value creation of every analyzed product should be identified.

There are some clear opportunities for cost-related modeling. For example, even though there is generally believed that less time in processes means lower costs, this has not yet really been explored and therefore still leaves a path for development. There are also some attempts for using methodologies like ABC for chain flow analysis (Whicker et al, 2006). 5

Special costing model that identifies activities in an organization and assigns the cost of each activity with resources 6 System of production, so actual orders provide a signal for when a product should be manufactured

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3-3-3- Referential Models When analyzing a supply chain with these types of models it is possible to identify elements that comprise it and links among themselves7, stakeholders, inter-relationships among them8, processes, technology involved and used business strategies (Angerhofer and Angelides, 2006). These models are the basis for administration methodologies, simulation models, information systems and quantitative models since they are fundamental as a conceptual structure to new and more detailed modeling proposals (Stefanovic and Stefanovic, 2008).

What makes difficult to create a good conceptual model lies in finding a balance between what is simple, with few elements and easy to analyze, versus what is complex representing the system but making the analysis difficult. Below there is a chart with some of the proposals that characterize referential modeling during certain years, according to the proposal made by Rocha dos Santos et al (2006) and Aguilar Saven (2004).

7 8

Topology Levels of collaboration

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Chart 2: Modeling Proposals Development Years

Type of Model

Since 1970

DFD: Data Flow Diagrams are diagrams that show information flows from one spot to another and describe the processes showing how they are related among themselves by means of databases. Petri Networks: is a kind of language used with graphs for system design,

specification,

simulation

and

verification.

They

are

particularly used when dealing with a high number of processes that should be synchronized and communicated. Since 1980

IMC: The open system architecture for Integrated Manufacturing Control, IMC, is a supporting methodology for companiesâ&#x20AC;&#x2122; integration, based on computer databases.

Since 1990

BPR: Business Processes Re-engineering Workflow: Workflow techniques are usually defined as computerbased business processes automation, during which documents, information or tasks are transmitted from one participant to another according to a set of procedural rules. UML Object-oriented methods: used for modeling and programming processes described as objects which are transformed by activities along the process.

Nowadays, Camarinha â&#x20AC;&#x201C; Matos and Afsarmanesh (2007) propose a very complete taxonomy of different current theoretical and conceptual approaches on referential modeling applied to collaborative systems. Depending on the way in which the system is analyzed, on its elements and on its relations, there is a general subdivision based on 4 dimensions: componential, behaviorist, structural and operational. Each one of these involves different types of models. Some examples of each group are as follows: a. Structural Dimension: theory of graphs, federated systems, workflow b. Operational Dimension: petri networks, game theory, portfolio theory c. Behaviorist Dimension: synergistic, memetics, semiotics d. Componential Dimension: ontology, knowledge mapping 13

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Conclusions 1. Currently, there are a great amount of conceptual approaches to the field of modeling. Among the different classifications there is one that proposes two great groups of methods: descriptive and normative. This one offers a better and a more cohesive understanding of all of the models.

The first group of models is appropriate for complex uncertain scenarios which correspond to the current supply chains. Additionally, these models are flexible and adaptable to different conditions and relatively easy to understand for all users.

2. The current state of supply chains descriptive modeling allows us to conclude that there is a huge variety of theoretical approaches to the field. A wide range of possibilities is open for future research and also for new proposals in integrative methodologies that get to more realistic explanations of the systems by gathering the most appropriate models for each one of their parts or organizations.

3. Referential models are the basis for future analysis. In other words, they constitute the conceptual framework on which to construct new more detailed approaches, or on which to make simulations. Therefore, they will always be needed as a main source for any other kind of analysis. One of the most recognized classifications for these types of models is the one that considers structural, componential, behavioral and operational models. As seen, this grouping includes four different ways to understand what a supply chain is as a complex system.

4. Simulation models are built based on conceptual models. They are flexible and allow us to make analysis of multiple scenarios. Thus, it is not difficult to

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make the decision of using them for studying supply chains. Nowadays, they have great possibilities to contribute to the study of supply chains.

5. Another development field in simulation is distributed simulations that are implemented in different platforms but interact in a unique network. This requires the use of standards that are called HLA to enhance that all programs are compatible. As an advantage, they have a distributed processing, according to the multiple analyses that are needed, which in turn can be integrated in a unique network. This mechanism enables a better visibility in the networks.

6. Supply chains and networks are complex entities that to be understood require the use of different combined tools to offer an overview in accordance with reality with multiple variables and interrelations. Each period of time has presented particular challenges that have demanded an adaptation of supply chains. Nowadays, the context requires making computer systems that can allow partial and integrated processing in a unique network.

7. Forecasting and cost-related models are the result of a longer historical progress, and therefore they offer fewer possibilities for further developments. However, there are always new research challenges given the changing business paradigms.

8. In regard to forecasting models, there is a possibility to expand their use and understanding as long as different conceptual tendencies on the subject are integrated in a single model. For instance, historical databases offer important clues to project new operations and, if this is combined with complementary elements such as current administration concepts and CEOsâ&#x20AC;&#x2122; management expertise, a very effective model is obtained. Nowadays, this rarely occurs since analyses are usually limited to a single conceptual tendency.

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Cost-based models have had a long history together with industrial development. Of course, these models have always been related to earnings which have led to a special interest on them. However, business paradigms have been changing, which consequently new approaches to the subject of costs have taken place. An example of this is the current tendency to value generation analysis which implies new calculation forms and new cost analyses. ABC system is a clear illustration of how paradigms have changed over the last years.

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