Selecting the Best Integrated Dynamic Performance System On the Base of BSC and F.MADM

Subject Areas : Industrial Management

Hamed Karimi Shirazi ¹ (M.S in Industrial Management)
Mahmoud Modiri ² (Department of Management, Tehran-South Branch, Islamic Azad University, Tehran, Iran)

Received: 2018-01-08 Accepted : 2018-01-08 Published : 2013-08-23

Keywords:

Abstract :

In a production collection, nowadays the Performance Measurement of an organization has an important role in achieving to their efficiency and effectiveness on order to competing in current dynamic environment. In this view, the key indicators of the integrated dynamic performance measurement in a competitive environment are considered as a necessary and unavoidable case. In this paper, considering the affective indicators of dynamic Performance to approach BSC in industry, we focus on the problem that how we can provide a mathematical and hierarchical affective model so that it has sufficient answers in the conditions of uncertainty in the fuzzy technics of AHP and TOPSIS; its aim is to recognizing and prioritize the effective indicators related to dynamic performance evaluation on the base of BSC with approach F.MADM in order to prioritize a set of dynamic indicators to increasing competitive power in industry. In order to determine the indicators, hierarchical tree of deceiving compiled during the sessions with the organization's experts. The statistical sample of the research includes experts and directors of the strategic area. Since these indicators used to prioritizing the decision tree indicators are considered as vocal variables, collecting the data is performed by questionnaire in a fuzzy way; so the multiple attribute decision making is fuzzy. The paper is an applied one and in the form of descriptive-measurable researches and it has a field importance; the solving approach of problems is in the type of mathematical modeling and the hierarchy of decision making of some group indicators is fuzzy. The results of analyzing the prioritizing of the factors and their grading by the two technics show that the indicator of expense, delivery time and quality can help to programmers a director of the organization in order to make long-term decisions for competition. There are also positive and strong coordination between the results of the validity of the answers of the two technics for dynamic performance evaluation of six alternatives. The fuzzy logic is used in order to design a way increasing the measurement of the occurrence of each performance indicator and the validity of the evaluation and programming process is performed in the content of EXCEL.  

References:

1- Asghar pour, M.J. (2009). Group decision and game theory in operations research approach. Tehran university, Tehran.

2-  Asghar pour, M.J. (2008). Multiple Attribute Decision Making. Tehran university, Tehran.

3- Aydogan, E.K. (2011). Performance measurement model for Turkish aviation firms using the rough-AHP and TOPSIS methods under fuzzy environment. Expert Systems with Applications, 38, 3992–3998.

4- Aryanezhad, M. B., & Tarokh, M.J.(2011). A Fuzzy TOPSIS Method Based on Left and Right Scores. International Journal off Industrial Engineering & Production Research, 22, 51—62.

5- Azar, A., & Darvishi, Z. (2007). Improvement Balance scord card(BSC) Based on Fuzzy Logic. The Third National Conference on Performance Management. Tehran univ. Tehran, 210.

6- Balli,S.,& Korukoğlu, SH.(2009). Operating System Selection Using Fuzzy AHP AND TOPSIS Methids. Mathematical and Computational Applications, 14(2), 119-130.

7- Bernard, W.W.,& Guo, L.,& Li, W., &Yang, D.(2007). Reducing conflict in balanced scorecard evaluations. Accounting, Organizations and Society, 32, 363–377.

8- Bititci، U.S. (2000).Dynamics of performance measurement systems. International Journal of Operations &Production Management.20 (6), 692-704.

9- Bititci, U.S., & Carrie, A.S.(1997). Integrated performance measurement systems: a development guide. International Journal of Operations & Production Management. 17(5), 522-535.

10- Chen, Chen-Tung. (2000) Extensions of the TOPSIS for group decision-making under fuzzy environment. Fuzzy Sets and Systems, 114, 1-9.

11- Chen, S.J., & Hwang, C.L. (1992).Fuzzy multiple Attributed decision-making.  Springer-verlag.

12- Cheng, S.,& Chan, C.W.,& Huang, GH.(2002). using multiple criteria decision analysis for supporting decision of solid waste management, journal of environmental science and health. 37(6), 975-990.

13- Chen Z. and Yang W. (2011). An MAGDM based on constrained FAHP and FTOPSIS and its application to supplier selection. Mathematical and Computer Modeling, 54, 2802–2815.

13- Ding, Ji-Feng. (2011). AN Integrated fuzzy TOPSIS method for ranking alternatives and its  application. Journal of Marine Science and Technology, 19(4), 341-352.

14- Ertuğrul İ., & Karakaşoğlu, N. (2007). Performance evaluation of Turkish cement firms with fuzzy analytic hierarchy process and TOPSIS methods. Expert Systems with Applications, 56, 487-501.

15- Ertuğrul İ., & Karakaşoğlu, N. (2008). Comparison of fuzzy AHP and fuzzy TOPSIS methods for facility location selection. Int J Adv Manuf Technol, 39,783–795.

16- Ghalayini, A. M. (1997). Integrated dynamic performance measurement system for improving manufacturing competitiveness. Production Economics, 48, 207-225.

17- Ghalayini, A. M, & Noble, J. S. (1996). The changing basis of performance measurement. International Journal of Operations & Production Management,16, 63-80.

18- Ghodsi pour, H.(2007). Analytical Hierarchy Process. Tehran univ.Tehran.260p.

19- Ghosh, D. N.(2011). Analytic Hierarchy Process & TOPSIS Method to Evaluate Faculty Performance in Engineering Education. UNIASCIT, 1 (2), 63-70.

20- Hwang, C.L., & Yoon, K. (1981). Multiple attribute decision making. Springer- verlag, Berlin.

21- Kahraman, C., & Secme, N.Y. (2009). Fuzzy performance evaluation in Turkish Banking Sector using Analytic Hierarchy Process and TOPSIS. Expert Systems with Applications, 36, 11699–11709.

22- Kaplan, R.S., & Norton, D.P. (1996). Linking the Balanced Scorecard to Strategy. In: California Management Review.39.53-79.

23- Karimi, A.R., & Mehrdadi, N. (2001). Using of the Fuzzy topsis and Fuzzy AHP Methods for wastewater treatment process selection. International journal of academic research, 3(1), 84-96.

24- Kennerley, M., & Neely, A. (2002). A framework of the factors affecting the evolution of performance measurement systems. International Journal of  Operations & Production Management 22 (11). 1222-1245.

25- Korukoğlu, S., & Balli, S.(2009). OPERATING SYSTEM SELECTION USING FUZZY AHP AND TOPSIS METHODS. Mathematical and Computational Applications, 14(2), 119-130.

26- Laitinen, E.K. (2002).  A dynamic performance measurement system: evidence from small Finnish technology Companies .Scandinavian journal of management, 18, 65-69.

27- Lee, A. H. I., Chen, W. C., & Chang, C. J. (2008). A fuzzy AHP and BSC approach for evaluating performance of IT department in the manufacturing industry in Taiwan. Expert Systems with Applications. 34, 96–107.

28- Laitinen, A. K. (2002). A dynamic performance measurement system: evidence from small Finnish technology Companies. Scandinavian journal of management, 18, 65-69.

29- Leung, L. C., & Cao, D. (2006). On consistency and ranking of alternatives in fuzzy AHP. European Journal of Operational Research. 124, 102–113.

30- Madi, E.N.(2001). Fuzzy TOPSIS Method in the Selection of Investment Boards by Incorporating Operational Risks. Proceedings of the World Congress on Engineering, 86, 782-801.

31- Matin, H.R., & Fathi, M.R., & Karimi Zarchi, M. (2011).The Application of Fuzzy TOPSIS Approach to Personnel Selection for Padir Company, Iran. Journal of Management Research, 3, 875-903.

32- Momeni . m,& Fathi, M. (2011). A fuzzy topsis-based approach to maintenance strategy selection: a case study. middle-east journal of scientific research, 8(3), 669-706.

33- Nilsson, F.,& Kald, M.(2002). Recent advances in performance management: the Nordic case.European management journal, 20(3), 45-68.

34. Saaty,T.L.(1999). Fundamentals of The Analytic Network Process, kobe japan: ISAHP, 12- 14.

35- Sezhian, M.V. (2011). Performance measurement in a public sector passenger bus transport company using fuzzy topsis, fuzzy AHP AND ANOVA – ACASE study, International Journal of Engineering Science and Technology.3 (2), 556-581.

36- Sharma, M. K., (2007). An integrated BSC-AHP approach for supply chain management evaluation . Emerald Group Publishing Limited, 11 (3), 57-68.

37- Shih,H.,& jyhshyur,H.,&, lee,E.S.(2007). Extension of topsis forgroup decision making.  mathematical and computer modelling.45, 801-813.

38-  Sun, Chia-Chi.(2010). A performance evaluation model by integrating fuzzy AHP and fuzzy TOPSIS methods. Expert Systems with Applications, 37, 7745–7754.

39-  Sun, CH.(2010). A performance evaluation model by integrating fuzzy AHP and fuzzy

      TOPSIS methods. Institute of Technology Management.1001, 7745-7754.

40- Tabarsa, Gh. R.(2004). Review and explain the strategic requirements of government agencies in the performance evaluation model. Jahad Daneshghahi,Tehran.

41. Wanga,Y. G.,& Leeb,H. SH.(2007).Generalizing TOPSIS for fuzzy multiple-criteria group decision-making. Computers and Mathematics with Applications, 53, 1762–1772.    

42-Wang,T. CH.,& Chang,T. H.(2007). Application of TOPSIS in evaluating initial training aircraft under a fuzzy environment. Expert Systems with Applications, 33, 870–880.

43- Yayınları. Sohn, M. H., You, T., Lee, S-L., & Lee, H. (2003). Corporate strategies, environmental forces, and performance measures: A weighting decision support system using the k-nearest neighbor technique. Expert Systems with Applications. 25: 279– 292.

44- Zanakis, S.H.,& Solomon,& Dublis, W.(1998). Multi-attribute decision making: A simulation mparision methods. European journal of operational Research, 107, 507-529.