Risk Assessment Using AHP in South Indian Construction Companies: A Case Study

International Journal of Engineering, Management & Sciences (IJEMS) ISSN-2348 –3733, Volume-2, Issue-5, May 2015

Risk Assessment Using AHP in South Indian Construction Companies: A Case Study Parvathy.P, ShivaprasadH.C, Gopalkrishna.B, Giridhar. B. Kamath  Abstract— The term risk is synonymous with uncertainty and it is present in every business. The construction industry in India also bares no exception to this fact. There are numerous problems which arise on a daily basis in the construction sector.

These problems are attributed to risks. The focus of this paper is to identifyand prioritize the major risks and risk factors that influence the three classes of the Indian construction companies which undertake the majority of the projects in the South Indian cities of Cochin and Udupi using Analytic Hierarchy Process , a multi-attribute decision making method which acts as a tool for risk analysis. The priority value of each risk factor and sub- risk factor were found out and compared and a rank was allotted to each risk factor based on this output. The output helps the management of construction companies in identifying which type of risk is most likely to occur in a particular class of company, so that it can be mitigated in the future.

Index Terms— Analytic Hierarchy Process, priority values, risk analysis, risk assessment model.

I. INTRODUCTION Risks are associated with every business. The construction industry in India is no exception to this fact. The Indian construction industry is worth over USD 120billion and this continues to grow considerably every year (Subramanyan, Sawant& Bhatt 2012) [1]. Hence there is a need to identify and prioritize among the risk factors that may otherwise adversely affect the project.

A. Background The Construction Industry Development Council, India has classified contractors based on the number of workers they employ. According to this the construction companies can be broadly classified as small, medium and large companies.

Small companies also known as Class III companies are those that employ about 1-200 persons. They account for about 96.4 percentage of the total construction companies in India.

Medium companies also known as Class II companies employ about 200-500 persons and they account for about 2.86 percentage of the total construction companies in India, whereas large sized companies also known as Class I companies are those that employ more than 500 workers and Manuscript received May 20, 2015.

Parvathy.PPG Scholar, Manipal Institute of Technology, Manipal University, Manipal.

Shivaprasad, H.C Faculty, Department of Humanities and Management, Manipal Institute of Technology, Manipal.

Gopalkrishna, B. Faculty, Department of Humanities and Management, Manipal Institute of Technology, Manipal.

Giridhar.B.Kamath H.C Faculty, Department of Humanities and Management, Manipal Institute of Technology, Manipal.

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they account for about 0.74 percentage of the total number of construction companies in India (Construction Industry Development Council, 2006). [2] The focus of this paper is to identify and prioritize the major risks and sub- risk factors that influence these three construction company classes of the South Indian cities of Cochin and Udupi using the Analytic Hierarchy Process (AHP). From earlier researches and literature reviews, the major risk factors have been identified as administrative, financial, resource, manpower and technical. In this particular study pair-wise comparison of risk factors is done and their priority values are calculated in order to rank them.

II. LITERATURE REVIEW According to Akintoye and Macleod (1997) ‘risk analysis and management’ in construction industry is dependent on three factors; experience, judgment and intuition of team members.

They concluded that formal activities to analyze and manage risk are rarely used in the construction industry. [3] A number of important risk factors in construction projects have been identified by researchers in the past. Iyer and Jha (2005) [4]identified 55 major attributes which were responsible for impacting the overall performance of a construction project. They observed that two factors, namely, commitment of project participants and internal conflict among project participants significantly affected the overall performance of the project.Abbasi, Abdel-Jaber and Abu-Khadejeh(2005) [5] in their study identified five major risk factors which affect the overall success of a project in a developing nation. They are Administrative, Financial, Resources, Manpower and Technical aspects. Tang and Young (2007) [6] identified certain risk factors such as contractor-specific, subcontractor-specific, client-specific, estimator-specific, design and project-specific, unknown geology conditions and economic-specific as critical for successful completion of a project. Vidivelli, Surjith and Jayasudha (2014) identified the risk factors that affected the performance of bridge projects as a whole. They concluded that ‘time management’ and ‘financial management’ significantly contributed towards predicting risk analysis of time and cost in bridge construction. [7] A number of risk assessment models have also been developed by different researchers. Kang and Feng (2008)[8] identified and assessed the potential risks faced by private sectors in holding BOT (Build, Operate and Transfer)

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Risk Assessment Using Ahpin South Indian Construction Companies: A Case Study

projects by developing a risk assessment model, which concluded that the primary risk factor for a BOT project is concession period for the project and the secondary risk factor is foreign exchange ratio.

Subramanyan, Sawant and Bhatt (2012) [1] in their study identified risk factors that influence the smooth completion of a project by developing a risk assessment model. Fuzzy analytical hierarchy process was used as a tool to analyze the risk factors and their significance in smooth completion of a project.Their findings indicated that risks specific to architects, consultant, environment and contract clause are more unpredictable in nature whereas, risks specific to project- manager, owner, contractor, finance and resource is more predictable in nature and can be effectively managed by appropriate contract provisions. They also suggested a risk response strategy in order to mitigate future risks in the Indian construction industry.

III. METHODOLOGY The methodology adopted for this project is given below: 1.

2.

3.

4.

5.

6.

Study of literature related to risk analysis and AHP. Identification of risk factors and sub risks.

Preparation of questionnaire.

Questionnaire survey and personal interviews.

Calculation of risk index score for each risk factor.

Formulation of decision problem into hierarchical structure.

7. Pair-wise comparison of the risk factors and sub-risk factors.

8. Formation of decision matrices and calculation of priority value of each risk factor and its sub- risks.

9. Ranking the risks according to the overall priority values of risk factors for each class of company separately.

A. Method of surveying Relevant information pertaining to the study was collected from primary and secondary data sources. Primary data sources refer to the information obtained from the questionnaires distributed to the construction project teams of the three classes of Indian Construction companies.

Secondary data sources consisted of the information taken from various books, journals, articles, the internet and websites. The targeted respondents were drawn using random sampling from a list of all construction companies operating in these cities. Random sampling was used so as to eliminate any form of bias and stratified sampling technique was adopted, since the population was split into non-overlapping portions. Sample size of 200 respondents was planned to collect the response.

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B. Questionnaire structure and design Prior to the administration of the questionnaire, it was validated through a panel of experts to ensure its content validity. Its reliability and content validity were also checked using the Smart PLS software (2.0). The questionnaire is divided into two parts. The first part consists of general information about the company such as the name of the company and the number of employees employed by the company. The second part focused on the risk factors in the construction industry.

Some of the risks commonly confronted by the construction companies were then classified under five major risk factor groups, namely – administrative (AD), financial (FD), resource (RE), manpower (MA) and technical (TE).On the basis of assessment of respondent answers from the questionnaire survey, a risk score was allocated based on the importance and probability of occurrence of risk on a scale of 1 - 4. Once the risk scores are obtained, the level of risk was assigned as - very important risk, fairly important, somewhat important and less important. In order to ascertain the risk scores for each risk, the significance score was first calculated using equation (1).

S ij  PijOij (1) Where Sij signifies the risk score given by respondent j for risk i; and Pij signifies the probability of occurrence of risk i assessed by respondent j; and Oij signifies the importance of risk i as assessed by respondent j [7].

The risk index score for each risk factor can be obtained by averaging the significance scores of all responses from each of the three classes of construction companies as shown in equation (2).

T

RS

i





S

i T

T

i j

(2)

Where RSi= index score for risk i; and Sij= significance score assessed by respondent j for risk i and T= total number of respondents.

After the risk index scores are determined, this is used to evaluate the priority values using analytic hierarchy process.

C. The Analytic Hierarchy Process The AHP was developed by Saaty (1994) [9] and it is a flexible as well as robust multi-criteria decision analysis method. The method of AHP makes it easy to understand and analyze project risks. It allows consideration of both objective as well as subjective factors in decision making. The important step of AHP is formulation of the decision problem into a hierarchical structure,with the top of the hierarchy representing the overall objective that is risk assessment. The next level of hierarchy represents the various risk factors as they are all of the same magnitude. The sub risks of the risk factors are represented in the subsequent level of the hierarchy. The lowest level comprises of the decision options which are the ranks given to the risk factors based on their

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International Journal of Engineering, Management & Sciences (IJEMS) ISSN-2348 –3733, Volume-2, Issue-5, May 2015 priority values in the present study. The hierarchical order gives a very clear picture of all the risk factors and sub-risks that affects the decision and the relationship between them.

The proposed risk classification scheme is shown (Fig. 1).

This ensures that the weightage factors remains similar for user input even if the scales differ.

Once the pair wise comparison is done, square matrices are created and relative weights are derived for elements of each level with respect to an element on the adjacent upper level of the hierarchy. The relative weights are computed as components of normalized eigenvector associated with the largest eigenvalue of their comparison matrix. A path is followed from the top of the hierarchy to the lowest level and the weights are multiplied along each segment of this path.

The outcome is a normalized vector of overall weights of the option.

IV. RESULTS AND DISCUSSIONS Fig. 1 Proposed AHP risk Assessment Model

A.Demographic analysis

After completion of the hierarchy, prioritization procedure to determine the relative risk index of each element of level two is initiated. The risk index scores of the elements of level two are pairwise compared. The decision maker can judge between two elements and determine if they are equally important or if one element is extremely important when compared to the other. This is then repeated with the subsequent levels of the hierarchy. Saaty[9] developed a scale of 1-9 of relative importance for pair wise comparisons [Table I].

The present study used a rating scale of 1-4 for depicting importance of risk and the scale is converted to 0.25-4 for depicting the probability of occurrence of the risk. When the risk index score was calculated for each risk category, it was observed to be in the range of 0.9 to 4. Thus this was chosen as the rating scale for risk index scores .Span of the AHP rating scale of relative importance is to be made equal to the span of the chosen scale of risk index. Hence the ratio of rating scale was determined and found to be 0.45 and this was multiplied to each value of the fundamental 1-9 scale of absolute numbers (Hossain, Adnan and Hasin 2014) [10].

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Out of the total number of 86 companies visited for the collection of data and it was observed that 34.9 % constituted the Class I companies whereas both Class II and Class III companies constituted 32.6 % each. Therefore the majority of respondents were from Class I companies.

B.Pair wise Comparison Using the information obtained from the respondent answers and that from Table I, pair wise comparisons were done between the different risk factors on level two of the hierarchy separately for all three company classes. The output was used to develop a decision matrix separately for each class of company. (Table II). From this a normalized matrix was developed.

To get the normalized matrix, reciprocal of decision matrix was obtained and each element of the reciprocal matrix was divided with them sum of its column to get the normalized relative weight. The normalized principal Eigen vector was obtained by averaging across the rows (Table III). The normalized principal Eigen vector is also called priority

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Risk Assessment Using Ahpin South Indian Construction Companies: A Case Study

vector. From the priority values, the rank for each risk factor can be determined. The consistency of the decision was checked by determining the consistency ratio. The consistency ratio must be less than or equal to 10% for a decision to be called consistent (Shahroudi, 2011) [11].

Once the pair-wise comparison of all elements on level two of the hierarchy is completed, then similarly pair wise comparison of all elements on level three of the hierarchy is performed. This level includes the sub risks of all the risk factors. Total of one matrix for the risk factors and 41 matrices for the sub factors are formed in this manner for each class of company. The relative importance of the risk factors and sub risks are computed as components of the normalized eigenvectors of the matrices. The priorities of the risk factors and the local and global percentage of each sub- risk for Class I companies isobtained (Table IV).

The local percentages (LP) are obtained by pair wise comparison of all the sub risks corresponding to each risk factor. The Global percentage (GP) is determined by multiplying the LP of each sub risk with that of the relative importance of the respective risk- factor. The overall priority or importance of a risk can be determined by summing up the figures in each GP column. Those sub risks whose overall weight is less than 10% were omitted because their weights

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are negligible to the overall outcome [11]. Similarly the priorities of the risk factors and the local and global percentage of each sub- risk for Class II and Class III companies are shown in Tables V and VI.

AD = administrative risks, FI= financial risks, RE= resource risks, MA= manpower risks, TE= technical risks Among Class I companies, financial risks have the highest relative importance of 0.476 followed by administrative risks with a relative importance of 0.228 (Table VI). Among the different financial risks, sub risk F1 “Absence of financial wing which has knowledge about company’s financial position and its activities� had the highest relative importance

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International Journal of Engineering, Management & Sciences (IJEMS) ISSN-2348 –3733, Volume-2, Issue-5, May 2015 or LP of 0.346. The results show that for class I companies, financial risks are given the highest priority in terms of their level of importance and probability of occurrence and hence they are allotted rank one.

In the case of Class II companies, resource risks had the highest relative importance of 0.476 followed by technical risks with a relative importance of 0.228. Among the different resource risks, sub risk R1 “Delay in mobilization

In the case of Class III companies, the highest relative importance was of 0.476 was for technical Risks followed by manpower risks with a relative importance of 0.234. Among the different technical risks, sub risk T1 “Insufficient data collection & survey before designs” had the highest relative importance of 0.259 (Table VI). These results show that in the case of Class III companies, the technicalrisks had highest priority among all the other risk factors with respect to their level of importance and probability of occurrence and hence were allotted rank one.

of work in spite of getting possession of site” had the largest relative importance of 0.359 (Table V). The results indicate that for class II companies, resource related risks are given the highest priority in terms of level of importance and probability of occurrence of the risk and hence allotted rank one.

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Class I companies normally handled large scale projects which involves large capital. Pair wise comparison among all the risk factors by the respondents of this company class showed thatfinancial risk had the highest priority value of 47.6% and among the various financial risks, one major sub risk secured the highest priority value of 13.2%. It was ‘the absence of a financial wing which has knowledge about the company’s financial position and its activities’. This indicated the importance of having an independent body within the organization having qualified people who are solely responsible for maintaining the financial discipline within the organization, and all projects taken up by the company has to be cleared for feasibility studies with this department. Thus in Class I company category, financial risks were given Rank 1 in terms of its priority value.

Class II companies worked on both bigger projects as well as small scale projects. When pair-wise comparisons were done

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Risk Assessment Using Ahpin South Indian Construction Companies: A Case Study

between the risk factors and priority value was calculated, it was seen that resource risks had the highest priority value of 47.6%. This indicated that though many companies belonging to the Class II category took on large scale projects, not all of them were able to successfully complete the project on time due to the lack of sufficient resources required for a project of that scale. Among the resource risks the sub risk R1 ‘delay in mobilization of work in spite of getting possession of site’ had the greatest priority value of 35.9%. Contractors have to commence work as soon as possible after possession of the site so as to avoid further delays to the final handover period.

If there is a delay in mobilization of work it can lead to further expenses for the contractor as well as his client and in extreme situations even lead to termination of the contract by the client. Thus in Class II company category, resource risk was given Rank 1 in terms of its priority.

Class III companies hired fewer employees and took on small scale projects. When priority values of the various risk factors were computed for the Class III companies, it was seen that Technical risk had the highest priority value of 47.6%. This is because most companies coming under this category did not have sufficient numbers of qualified engineers and supervisors. Among the technical risks the sub risk T1‘insufficient data collection and survey of site before submission of designs’, had the highest priority value of 25.9%. Certain soil types and conditions like higher ground water table can result in difficulties and more expenses during construction. Therefore preliminary site investigations and surveys have to be carried out by a qualified and licensed engineer or contractor before submission of final designs and quotation amount. Thus in Class III category technical risks were given Rank 1with respect to its priority value.

[4] Iyer KC & Jha KN (2005). “Factors affecting cost performance: evidence from Indian construction projects”. International Journal of Project Management, 23 (4), 283-295.

Parvathy. PPG Scholar,Engineering Management, Manipal Institute of Technology, Manipal University.

Prof.Dr.Shiva Prasad H C,Graduated with B.E. (Ind. Prod. Engg), & M.E. (Production Management) with Ph. D (IIT KGP). Fellow of WBI, Australia. His research interest: EIC, KM, & Entrepreneurship, General Management. Editor of ManipalJournal of Management

Prof. Dr. Gopalkrishna B. is Professor in Department of Humanities and Management, MIT, Manipal University. He has BE in Mechanical Engineering, M.Tech in Engineering Management and PhD in Services Marketing. His research areas include service quality, human resource management, Real estate Management, System Dynamics.

Giridhar B Kamathis an Assistant Professor in the Department Of Humanities and Social Sciences at Manipal Institute of Technology.

CONCLUSIONS The findings of this study can help the management of construction companies in identifying, prioritizing and preparing for all the key risk factors which are likely to affect a project. Risk is subjective by nature. However AHP provides a basis for the management to take objective decisions in order to reduce the impact of risk to a more manageable level. The main limitation of this study is that it cannot be generalized for all Indian construction companies because the study is constrained to only the South Indian cities of Cochin and Udupi where respondents were drawn using random sampling. Also very little data is available for the construction industry. Therefore more research is required with the cooperation of the government and local builders associations.

REFERENCES [1] Subramanyan H, Sawant PH & Bhatt V (2012). “Construction project risk assessment: development of model based on investigation of opinion of construction project experts from India”. Journal of Construction Engineering and Management, 138 (3), 409-421.

[2] Construction Industry Development Council (2006 - 2007). “Indian Construction Industry”. Available: http://asiaconst.com.

[3]Akintoye, A. S., & MacLeod, M. J. (1997).” Risk analysis and management in construction”. International journal of project management, 15(1), 31-38.

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