USING THE FUZZY-AHP TECHNIQUE FOR DETERMINING THE KEY PERFORMANCE INDICATORS OF PUBLIC CONSTRUCTION COMPANIES IN IRAQ

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1 International Journal of Civil Engineering and Technology (IJCIET) Volume 9, Issue 13, December 2018, pp , Article ID: IJCIET_09_13_145 Available online at ISSN Print: and ISSN Online: IAEME Publication Scopus Indexed USING THE FUZZY-AHP TECHNIQUE FOR DETERMINING THE KEY PERFORMANCE INDICATORS OF PUBLIC CONSTRUCTION COMPANIES IN IRAQ Ali S. Tofan and Hatem K. Breesam Civil Engineering Department, University of Baghdad; Baghdad, Iraq ABSTRACT The fierce competition, the economic weakness and the openness that Iraq is going through on international construction companies have put great pressure on the local construction companies to develop their performance continuously. Most of the researchers focused on measuring the performance and success of construction projects, but a limited number of studies were done on performance measurement at the level of the construction company, but these studies began to increase due to the need for performance measures at the companies' level. Key Performance Indicators are considered one of means for measuring performance and progress towards objectives of the organization and help organizations and agencies to identify and measure their progress toward specific objectives. The indicators are weights and measures derived from strategic objectives through the translation of those objectives into programs, procedures, and activities. Performance measurement includes four operations, which are building standards, then assessment then evaluation and then optimization. Key performance indicators KPIs typically have financial and nonfinancial metrics, which is one of the success measurement techniques of the performance of organizations. Organizations look to the performance indicators as a strategy to achieve the objectives and results, and a strong indicator of the organizational success. List of 58 KPIs in five perspectives (financial, customer, social and environmental, internal business, and learning and growth) obtained from the previous studies. Then, the questionnaire was developed for data collection to find the most important KPIs for construction companies in Iraq. The results of the questionnaire extracted 26 KPIs. The KPIs from the first questionnaire were included in a second pairwise comparison questionnaire using the fuzzy analytic hierarchy process (FAHP) technique to obtain the weights related to each KPIs. Finally, the study obtained 15 KPIs for construction companies in Iraq divided into four perspectives, financial, customer, internal business, and learning and growth. Key words: Construction Companies, Fuzzy-AHP (FAHP), Key performance indicators (KPI), Iraq editor@iaeme.com

2 Ali S. Tofan and Hatem K. Breesam Cite this Article: Ali S. Tofan and Hatem K. Breesam, Using the Fuzzy-Ahp Technique for Determining the Key Performance Indicators of Public Construction Companies in Iraq, International Journal of Civil Engineering and Technology, 9(13), 2018, pp INTRODUCTION High competitiveness and profound changes in the construction industry force executives to continually improve the performance of their companies [1]. According to Luu 2008 performance measurement is the core of continuous improvement, application of the measurement process within companies can identify strengths and weaknesses in performance and thus use lessons learned to identify best practices that can lead to an outstanding performance in the case of its adoption and implementation[2]. In order to measure performance, it is essential to select the appropriate key performance indicators that are most important in determining the success of the construction process. Its play an essential role in providing information on the performance of the construction tasks, projects, and companies. Many studies were conducted to identify the key performance indicators, most of which focused on performance measurement at the project level, while studies at the company level were limited[1]. In Iraq, few efforts to identify indicators that can be used to measure the performance of construction projects carried out and did not focused on the performance of companies wholly where interest only financial indicators. Thus the set of balanced performance indicators that can be used to measure the organization's performance is non-existent. Therefore to fill this gap, this research aims to identify the most critical performance indicators that can be used by managers to measure the performance of construction companies in Iraq. 2. LITERATURE REVIEW The literature review considered two specific subjects, construction company performance and the importance of key performance indicators in the construction companies. Neely 1995 defined performance measurement as: the process of quantifying effectiveness and efficiency of actions. [3]. The previous studies addressed several aspects of construction companies' performance, such as financial, process, customer, learning, and environmental, etc., by adopting different approaches [4]. National Institute for Standards and Technology (NIST) 2018 defined indicators as: "numerical information used to quantify the input, output and performance dimensions of processes, products, programs, projects, services and the overall outcomes of an organization"[5]. The concept of (KPIs) generated an ever growing number of indicators because each organization will choose the indicators that support the activities which are critical and of best value for the organization[6]. To measure the performance of a company, the first thing to do is to look for the Key Performance Indicators (KPIs). Measurement is the heart of the performance management process as the information system[7]. Adhiprasangga 2016 confirmed that the first step to use the balanced scorecards is identifying the KPIs of the company[8]. The creators of the balanced scorecards (BSC), Kaplan and Norton, began their studies on the subject in the 80s, and published it in their article: "The BSC" of the Harvard Business Review (1992), where they define the BSC as: "A set of indicators that provide top management with a comprehensive view of the business." Over time, and to the extent that the BSC is imposed on more organizations, it has become a comprehensive management system editor@iaeme.com

3 Using the Fuzzy-Ahp Technique for Determining the Key Performance Indicators of Public Construction Companies in Iraq articulated with strategic planning[9]. Ali et.al developed 10 key performance indicators for construction companies in Saudi Arabia[1]. The authors found that the company performance did not depend on the financial indicators only but also the non-financial indicators. Yu 2007 developed a framework to evaluate the company performance[10]. The author stressed on the need to develop an integrated method to measure the performance of the project and the performance of the company simultaneously, because the construction industry oriented towards the project. 3. METHODOLOGY To identify the key performance indicators (KPIs) for public construction companies in Iraq, we determines the KPIs from the literature studies to extract the initial KPIs list that related to the construction companies. Also, personal interviews with the head or deputy of planning and monitoring department or quality section manager because they are the most related to performance measurement of the company. The interviews were made to identify the method used to measure the performance of the company. Then, from the previous studies and interviews, the first questionnaire was defined and finalized after the judgment of external experts. The questionnaire has been distributed to experienced engineers working for the public construction companies which related to the ministry of Construction, Housing, and Municipalities. After that, the questionnaire was analyzed to find the KPIs with high importance according to the relative mean of data collected. The important KPIs from the last step has been included in the second questionnaire form for pair-wise comparison using the fuzzy analytic hierarchy process (FAHP) approach to find the relative weight for each perspective and each KPI. Taking in consideration checking the consistency ratio for the medium and, lower and upper values of integrated comparison matrices which must be less than 10%. Finally, identifying the metric for each KPI to measure it. These metrics will help to Measure the performance of the company toward the objectives on periodical intervals. The methodology steps is shown in (Figure 1). 3.1 Criteria Selection The primary data collected from the previous studies in order to find the initial set of KPIs. These KPIs were included in the first questionnaire to identify the relative importance of each of them. An interview is an essential tool in academic and scientific research. Therefore, we did personal interviews with the head or deputy of planning and monitoring department or the manager of a quality section of the public construction companies. Interviews showed that there is a performance evaluation in the companies, but it is not applied conscientiously, and the evaluation used routinely for employee promotion only editor@iaeme.com

4 Ali S. Tofan and Hatem K. Breesam Figure 1 Research methodology 3.2 Questionnaire To determine the relative importance of performance indicators for construction companies, we designed questionnaire form to collect the data. 88 questionnaires were distributed to experienced engineers and number of heads of departments in 8 public construction contracting companies (Table 1). From the 88 forms, returned 84 forms. Also we canceled three forms due to incomplete information and mistakes. Therefore, the number of questionnaires analyzed were 81 which represent 92% of questionnaires distributed. The questionnaires categorized into two parts: the first part is the personal information of the participant where the second part contains the questions to identify the importance of KPIs proposed editor@iaeme.com

5 Using the Fuzzy-Ahp Technique for Determining the Key Performance Indicators of Public Construction Companies in Iraq Table 1 Questionnaire participants distribution according to the company Company No. of Participants Al-Mansour General Company for Constructional Contracts 11 Hammurabi General Company for Constructional Contracts 11 Al-Rasheed General Company for Constructional Contracts 11 Al-Farouq General Company for Constructional Contracts 11 Ashur General Company for Constructional Contracts 10 Saad General Company for Constructional Contracts 9 Al-FAO General Engineering Company 10 AL-Mutasim General Company for Constructional Contracts 8 Reliability of the scale is the extent to which the scale to give results close to each time it is used, and the reliability of the questionnaire is measured in many different ways. We used Guttman Split-Half Reliability coefficient and Cronbach's alpha coefficient. The Guttman coefficient between the two split halves is 0.97, and this means excellent stability of the questionnaire. Also, by using the alpha equation for the internal consistency of the questionnaire, had obtained a scale (0.967) for the KPIs group and this mean high internal consistency of the questionnaire. The result will take a value from the range (1 to 5) due to the use of a five-point Likert scale. The calculation has been done using Statistical Package for the Social Sciences (SPSS) and Microsoft Excel spreadsheets. After completion of the analysis, the KPIs with relative importance equal or greater than 4.00 were selected from each perspective to evaluate it with the fuzzy AHP pair-wise comparisons to find the relative weight for each perspective and KPI. Therefore, the selected KPIs with relative mean are shown in (Table 2) arranged by perspective in descending order. Table 2 The selected KPIs for FAHP comparison Perspective KPI Mean Profitability 4.65 Financial stability 4.26 Financial Cash flow 4.26 Completion within Budget 4.01 Capital 4.00 Quality of service and work 4.47 Competitive price 4.20 Customer External customer satisfaction 4.02 Market share 4.01 Internal customer satisfaction 4.00 Policy or law of government 4.02 Social and Risk control 4.01 Environmental Competitors 4.00 Business efficiency 4.41 Meet technical specification 4.40 Internal business Managers competency 4.23 On-time delivery 4.19 Meet predetermined goals editor@iaeme.com

6 Ali S. Tofan and Hatem K. Breesam Learning and Growth Effectiveness of planning 4.12 Safety 4.07 Quality control and rework 4.02 Company's reputation 4.22 Organization competency in management human resources 4.05 Continuous improvement 4.04 Motivation 4.04 Human resource training and development Fuzzy analytic hierarchy process technique Analytical hierarchy process (AHP) is a powerful decision-making methodology developed by Saaty in the 1980s to simplify the decision-making process [11]. It includes qualitative and quantitative techniques and makes it possible to decompose complex problems into simpler sub-problems where each level shows a set of objectives or criteria relative to each sub-group [11][12][13]. The nine-point scale simplifies the choice of criteria and provides information regarding the dominance of each element over others concerning the importance of each criterion of the higher levels of the hierarchy. Individual opinions of view are made in groups, taking into account the pertinent decision maker and are handled as a foundation for the analysis of the reasons for specific judgments; there is the one week spot that occurs during the setup of comparisons matrixes [14][15]. When the number of characteristics is increasing in the hierarchy, more matchings' between attributes need to be applied. Furthermore, by increasing of criteria and sub/criteria, the experts are dealing with physical and mental fatigue. As a result, the judgments are becoming unreliable, subjective and imprecise. Therefore, the fuzzy triangular numbers is a valuable solution for handling the subjective and inaccurate judgments. Zadeh developed the fuzzy set theory and had become a critical methodology in pair-wise comparisons. The fuzzy number can be defined as triple M= (l, m, u) where its membership function was defined as[16]: =,,,,,,,, Where l m u, l and u are lower and upper values, m is middle value of M. When all three numbers are equal (l = m = u), then we are dealing with non-fuzzy numbers. The pairwise comparisons linguistic scale used is shown in (Table 3): Table 3 The linguistic scale, which used in pairwise comparisons (Salman) (1) The preference degree (Intensity of the importance) of one activity over another(linguistically scale) Equal importance The preference degree Digital value 1 Explanations Two activities contribute equally to the objective Fuzzy digital value Invert of the fuzzy value (1,1,1) (1,1,1) editor@iaeme.com

7 Using the Fuzzy-Ahp Technique for Determining the Key Performance Indicators of Public Construction Companies in Iraq Intermediate importance between (Equal and moderate) Moderate importance 3 Intermediate importance between (Moderate to strong) Strong importance 5 Intermediate importance between (Strong and very strong) Very strong importance 7 Intermediate importance between (Very strong and absolute) Absolute importance One activity has (equal to moderate importance) over another Experience and judgment slightly prefer one activity over another One activity has (moderate to strong) over another Experience and judgment strongly prefer one activity over another One activity has (strong to very strong) over another An activity is preferred very strongly over another One activity has (very strong to absolute) over another The evidence preferring one activity over another is of the highest Possible order of affirmation (1,2,3) (1/3,1/2,1) (2,3,4) (1/4,1/3,1/2) (3,4,5) (1/5,1/4,1/3) (4,5,6) (1/6,1/5,1/4) (5,6,7) (1/7,1/6,1/5) (6,7,8) (1/8,1/7,1/6) (7,8,9) (1/9,1/8,1/7) (8,9,10) (1/10,1/9,1/8) The procedure proposed for acquiring priority weights for KPIs can be illustrated in the (Figure 2). Development of the hierarchy tree, based on the objective (Best KPIs for construction company performance), criteria (Perspective), and sub-criteria (KPIs) Preparation of the matrices for data collection (pair-wise comparison) Data collection from the expert group Consistency check of the matrices (fuzzification of data) Evaluation of the weights of criteria and sub-criteria Prioritization of sub-criteria (KPIs) Figure 2 The proposed procedure for acquiring priority weights for KPIs (Figure 3) illustrates the hierarchy tree for the pair-wise comparison editor@iaeme.com

8 Ali S. Tofan and Hatem K. Breesam Figure 3 The hierarchy tree for pair-wise comparison During the first step, the comparison on the first level between the main criteria (Perspectives) was established. On the second level, the pair-wise comparison between subcriteria (KPIs) was performed. The pair-wise comparison was made by the expert group of 34 members who have over 15-years' experience in the field of planning and monitoring in the construction companies The FAHP calculation of consistency ratio We must be sure of the harmonic of the comparisons of every participant, to identify if the comparisons are harmonic or assonant to be confident of the consistency and validity of sample answers, Gogus and Boucher (1998) method is used to calculate the consistency ratio[17]. The calculation completed using the procedure below: Step 1: divide the integrated fuzzy triangular matrix into two matrices, the first matrix for middle numbers and the second matrix resulted from the geometric mean of upper values and lower values of triangular numbers. Step 2: the vector weight is computed using Saaty method through the equations (2) and (3) below: = = " =# " $ " =! (2) %! (3) % Step 3: the largest eigenvalue for each matrix is calculated using the equations (4) and (5) as follows: = '!! (4) editor@iaeme.com

9 Using the Fuzzy-Ahp Technique for Determining the Key Performance Indicators of Public Construction Companies in Iraq " ' = " " % ) * +!! (5) Step 4: Following Saaty's procedure, consistency indices (CI), which represent the deviation from perfect consistency, are calculated using the equations (6) and (7) below:,- = ',- " =.' " / Step 5: The ratio of consistency index to the average random index shown in (Table 4) is called the consistency ratio as presented in equation (8), Following Saaty's rule[11][18], a consistency ratio of 0.1 or less is considered acceptable for each matrix type. If the consistency ratio of a pair-wise comparison matrix is greater than 0.1, then the participants should be encouraged to re-evaluate their preferences.,0=,- 0- Table 4 Random indices [17][19][20] Matrix Size RIm RIg Matrix Size RIm RIg The FAHP calculation of consistency ratio was done using the programs proposed by Aldhaheri 2018 depending on Gogus and Boucher 1998 method[17][20]. After computing the weights for each perspective and each KPI, the consistency ratios calculated for the new fuzzy integration matrices. The results for the main matrix (perspectives) and sub-matrices (KPIs) are shown in (Table 5). All the matrices achieved a permissible consistency ratio under 0.1. Table 5 The consistency ratio for main and sub-matrices The KPIs applied to measure the performance Number of of construction companies criterions CRm CRg The main matrix (perspectives) Financial KPIs Customer KPIs Social and environment KPIs Internal business KPIs Learning and growth KPIs (6) (7) (8) editor@iaeme.com

10 Ali S. Tofan and Hatem K. Breesam The FAHP Priority Weights and Ranking of perspectives and key performance indicators As well, to obtain the priority weights of the perspectives and KPIs, the program proposed by Al-dhaheri 2018 depending on Chang 1996 is used[16][20]. The relative weights and rank of each perspective are shown in (Table 6): Table 6 The relative weights and rank of each perspective No. Perspective Weight Rank 1 Financial Perspective Customer Perspective Social and environment Perspective Internal Business Perspective Learning and Growth Perspective As seen, the social and environment perspective obtain weight which means that the majority of experts opinion that this perspective does not affect the overall performance of the construction company. The financial perspective obtains the highest weight with due to its high effect on the success of the company. The results of relative weights and rank of financial KPIs are shown in (Table 7): Table 7 The relative weights and rank of the financial KPIs No. Financial KPI Weight Rank 1 Profitability Financial stability Cash flow Completion within Budget Capital It is noted that the profitability has the highest effect on the financial perspective with Also, the experts decided that the financial stability and capital have weight and this due to that these companies are public supported by the government. The relative weights and rank of customer KPIs are shown in (Table 8): Table 8 The relative weights and rank of the customer KPIs No. Customer KPI Weight Rank 1 Quality of service and work Competitive price External customer satisfaction Internal customer satisfaction Market share It is evident that the Quality of service and work obtained the highest importance with weight followed by the external customer satisfaction with weight, which proves that there is great importance to satisfy the customer to increase the performance of the company. Although social and environmental indicators will not be adopted because the perspective weights zero, the results of the pair-wise comparison between them will be explained in (Table 9) to identify the preferences of experts if the social and environment perspective is studied alone editor@iaeme.com

11 Using the Fuzzy-Ahp Technique for Determining the Key Performance Indicators of Public Construction Companies in Iraq Table 9 The relative weights and rank of the social and environment KPIs No. Social and environment KPI Weight Rank 1 Policy or law of government Risk control Competitors The experts consider that the competitors have no importance in comparison with policy or law of government, and risk control to the social and environment perspective. The relative weights and rank of internal business KPIs are illustrated in (Table 10), which shows that the business efficiency is the highest in weight (0.227) followed by the effectiveness of planning with weight: Table 10 The relative weights and rank of the internal business KPIs No. Internal business KPI Weight Rank 1 Business efficiency Meet technical specifications Managers competency On-time delivery Meet pre-set targets Effectiveness of planning Safety Quality control The relative weights and rank of learning and growth KPIs is shown in (Table 11), which shows that the motivation is the highest importance with weight followed by reputation with weight: Table 11 The relative weights and rank of the learning and growth KPIs No. Learning and growth KPI Weight Rank 1 Reputation Motivation Organization competency in management human resources Continuous improvement Human resource training and development RESULTS ANALYSIS Kaplan and Norton 1998 mentioned that the number of KPIs is for the company[21], while Ali 2013 stated that the suitable amount of KPIs is 8-12 [1]; also Yu 2007 identified 16 KPIs from 26 KPIs to apply it on construction companies[10]. Alarcon 2001 said that the implementation of a performance measurement system (PMS) should begin with relatively few KPIs[22], while Costa 2004 mentioned that high number of KPIs prevents the smooth application of PMS[23]. Therefore, the KPIs with relative weight equal to or less than is not considered, and the relative weights for the remaining KPIs in the same perspective renormalized by sum the remaining KPIs and divide each KPI weight on the sum to obtain the normalized weight in 100 percent. The final weights for the obtained 15 KPIs are presented in (Table 12) editor@iaeme.com

12 Ali S. Tofan and Hatem K. Breesam In conclusion, these 15 KPIs can be used to measure the performance of the construction companies in Iraq KPI metrics After determining the set of key performance indicators (KPIs) and the weights of each KPI and perspective, these KPIs divided as explained on the perspectives to conclude the mathematical equation that measures the performance of the construction company in a specific period. The weight for each KPI is obtained from the experts by the fuzzy analytic hierarchy process (FAHP). (Table 13) illustrate the weights and measures of each KPI. The measures obtained from the previous researches on this field. Table 12 The final KPIs obtained with relative weights Perspective Weight KPI Weight Profitability Financial Cash flow Completion within Budget Business efficiency Effectiveness of planning Internal Business Safety Quality control Managers competency Quality of service and work Customer External customer satisfaction Internal customer satisfaction Motivation Reputation Learning and growth Organization competency in management human resources Human resource training and development Table 13 Weight, code and measure of each KPI No. KPI Weight Measure or metric 1 Financial perspective Profitability[1] : 9;< 5;6828=6 >369? 28@8;A8= 1.2 Cash flow[1] B9=h 4?3D 423E 3F829653;= BA228;6?5975?5658= Completion GA<H868< B3= within Budget IJ6A9? B3=6 Internal 2 business perspective Business K:F8;=8= efficiency [1] L8@8;A8= editor@iaeme.com

13 Using the Fuzzy-Ahp Technique for Determining the Key Performance Indicators of Public Construction Companies in Iraq 2.2 Effectiveness of planning[24] Safety[1] Quality control[25] Managers competency Customer perspective Quality of service and work [26] External customer satisfaction [24] Internal customer satisfaction[24] Learning and Growth perspective >369? J3=6 34 Jh9;H8= 5; D32M= IJ6A9? 6369? J3=6 34 D32M= NAE F32697?89JJ5<8;6= I@829H8 ;AE7828EF?3O8< NAE <848J6= 5; F23P8J6= >369? ;AE78234 F23P8J6= NAE =M= J3EF?868< NAE =M= 9==5H;8< >369? Q528J6 J3=6 34 R58?< L8D32M IJ6A9? J3;=62AJ653; Fh9=8 J3= External Customer satisfaction Survey internal Customer satisfaction Survey Motivation[6] B3EF8;=9653; L8@8;A8= 4.2 Reputation [24] Value of Company reputation Survey Organization competency in management human resources [27][25] Human resource training and development[6] SAE9; L8=3A2J8 4A?? 65E8 8TA5@9?8;6= >369? ;AE A?? 65E8 8TA5@9?8;6= NAE F9265J5F9;66= 5; 96?89=6 3;8 J3EF9;O F95< 6295;5;H 9J65@56O NAE EF?3O88= 8?5H57? ;5;H After measuring each KPI, the progress toward targets then calculated using the linear equations below: 1. When the trend of KPI is upward: X" YU- UVW = X" Z 2. When the trend of KPI is downward: YU- X" UVW = Z X" Furthermore, the Absolute performance is calculated by multiplying the performance by the weight as shown below: [\ ]VW=]VW " editor@iaeme.com

14 Ali S. Tofan and Hatem K. Breesam Moreover, to find the performance of each perspective, the related KPIs absolute performance are summed and multiplied by weight of perspective to find the perspective absolute performance. Finally, the total performance resulted from the summation of the perspectives absolute performances. 5. CONCLUSION After the study of hypotheses and the results analysis of the questionnaire and interviews conducted, where the goal of this study is to develop the key performance indicators to measure the performance of public construction companies in Iraq, where these indicators were divided into four perspectives based on the Balanced Scorecard framework. We found that most companies focus on financial indicators to measure its performance while other indicators were neglected. We concluded that the financial indicators have the highest impact on the performance of the construction companies. However, the financial indicators not reflect the complete picture of the company's performance. It is worth mentioning that the four perspectives are not separate from each other, where each affects the other, as by increasing learning and growth indicators, this will lead to an improvement in the internal business indicators, which in turn will improve the service provided to the customer and thus increase his satisfaction, which will eventually increase Company profits, The paper result can be considered as the first step to create an integrated framework to measure the performance of construction industry in Iraq. ACKNOWLEDGEMENTS The authors thank the Ministry of construction, housing, and municipalities and the construction companies related to it for support and participation in the questionnaire. Also, we thank University of Baghdad for permission and encouragement to conduct such studies for the benefit of science and society. REFERENCES [1] H. A. E. M. Ali, I. A. Al-Sulaihi, and K. S. Al-Gahtani, Indicators for measuring performance of building construction companies in Kingdom of Saudi Arabia, Journal of King Saud University-Engineering Sciences, vol. 25, no. 2, pp , [2] V. T. Luu, S. Y. Kim, and T. A. Huynh, Improving project management performance of large contractors using benchmarking approach, International Journal of Project Management, vol. 26, no. 7, pp , [3] A. Neely, M. Gregory, K. Platts, A. Neely, M. Gregory, and K. Platts, A literature review and research agenda [4] M. Eshtaiwi, I. Badi, A. Abdulshahed, and T. E. Erkan, Determination of key performance indicators for measuring airport success: A case study in Libya, Journal of Air Transport Management, vol. 68, pp , [5] NIST, Baldridge Award Criteria, National Institute for Standards and Technology, [Online]. Available: [Accessed: 12-Nov-2018]. [6] R. A. Maya, Performance Management for Syrian Construction Projects, International Journal of Construction Engineering and Management, vol. 5, no. 3, pp , [7] M. Kagioglou, R. Cooper, and G. Aouad, Performance management in construction: a conceptual framework, Construction Management and Economics, vol. 19, no. 1, pp , [8] A. Adhiprasangga, A. P. Sari, T. W. Putra, and W. Java, Develop Balanced Scorecard From Kpi in Construction, editor@iaeme.com

15 Using the Fuzzy-Ahp Technique for Determining the Key Performance Indicators of Public Construction Companies in Iraq [9] R. S. Kaplan and D. P. Norton, The balanced scorecard - measures that drive performance. (includes related articles), Harvard Business Review, vol. 70, no. 1, p. 71, [10] I. Yu, K. Kim, Y. Jung, and S. Chin, Comparable performance measurement system for construction companies, Journal of Management in Engineering, vol. 23, no. 3, pp , [11] T. L. Saaty, The analytic hierarchy process. New York: McGraw-Hill., [12] A. Shahin and M. A. Mahbod, Prioritization of key performance indicators: An integration of analytical hierarchy process and goal setting, International Journal of Productivity and Performance Management, vol. 56, no. 3, pp , [13] F. Kong and H. Liu, An improvement on Saaty s AHP, Artificial Intelligence Applications and Innovations - IFIP TC12 WG12.5-2nd IFIP Conference on Artificial Intelligence Applications and Innovations, AIAI 2005, pp , [14] C. C. Sun, A performance evaluation model by integrating fuzzy AHP and fuzzy TOPSIS methods, Expert Systems with Applications, vol. 37, no. 12, pp , [15] R. W. Saaty, The analytic hierarchy process what it is and how it is used, Mathematical Modelling, vol. 9, no. 3 5, pp , Jan [16] D. Y. Chang, Applications of the extent analysis method on fuzzy AHP, European Journal of Operational Research, vol. 95, no. 3, pp , [17] O. Gogus and T. O. Boucher, Strong transitivity, rationality and weak monotonicity in fuzzy pairwise comparisons, Fuzzy Sets and Systems, vol. 94, no. 1, pp , [18] T. L. Saaty, How to make a decision: The analytic hierarchy process, European Journal of Operational Research, vol. 48, no. 1, pp. 9 26, [19] A. Goodarzi and H. N. Dokht, Measuring the Relative Importance of Financial Ratios: An Integrated Approach of Fuzzy DEMATEL and Fuzzy ANP, International Business and Management, vol. 11, no. 3, pp , [20] S. A. M. Al-dhaheri, Evaluation of overhead cost in iraqi construction industry, University of Baghdad, [21] R. S. Kaplan and D. P. Norton, Putting the Balanced Scorecard to Work, The Economic Impact of Knowledge, pp , [22] L. F. Alarcón, A. Grillo, J. Freire, and S. Diethelm, Learning from collaborative benchmarking in the construction industry., Proceedings of 9th Annual Conference of the International Group of Lean Construction., pp. 1 10, [23] D. B. Costa, H. Lima, and C. T. Formoso, Performance measurement systems for benchmarking in the Brazilian construction industry, in Performance measuring system for benchmarking Conference. Rotterdam, Netherlands, 2004, pp [24] J. Rankin, A. R. Fayek, G. Meade, C. Haas, and A. Manseau, Initial metrics and pilot program results for measuring the performance of the Canadian construction industry, Canadian Journal of Civil Engineering, vol. 35, no. 9, pp , [25] Smart KPIs, KPI list, [Online]. Available: [26] M. S. El-Mashaleh, R. Edward Minchin Jr, and W. J. O Brien, Management of construction firm performance using benchmarking, Journal of Management in Engineering, vol. 23, no. 1, pp , [27] B. K. L. Kaminski and T. Lopes, Return on Investment : Training and Development TRAINING AND Return on Investment : Training and Development, Evaluation, pp. 1 18, editor@iaeme.com