A unified performance evaluation model in competitive environmen tby combining of data envelopment analysis, balanced scorecard and game theory-case study: Cement companies

Document Type : Research Paper

Authors

1 Department of Industrial Engineering, Urmia University of Technology, I.R. Iran

2 Assisstant professor of industrial engineering-,قئهش عدهرثقسهفغ خب فثزادخمخلغDepartment of Industrial Engineering, Urmia University of Technology, I.R. Iran

3 Department of Industrial Engineering, Faculty of Engineering, Kharazmi University, I.R. Iran

Abstract

In this paper, a comprehensive and simplified model for industries’ performance evaluation and performance measurement is offered. We use the balanced scorecard as a framework for the continuous DEA models. This means that we used four output-oriented DEA models with variable returns to scale, for each of the four aspects of BSC and used the indicators tailored to each BSC aspects as inputs and outputs of DEA models. In this model we use the bargaining game theory to show the impact of bargaining power of units in the competitive environment. Thus,we offer a holistic approach to evaluating and improving the performance of the industries in a competitive environment. Finally, by providing a case study of 17 cement companies of the holding of Shasta (Social Security Investment Company) to run the model and offer solutions to improve the poor performance of units. Results imply the power of the proposed model in evaluating the overall performance of the units and a key advantage over previous models.

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Main Subjects

References

1. Neely, A. R. (2000). “Performance Measurement system Design: Developing and Testing a process-based Approach.” International Journal of Operations & Production Management, (45), 1119-20.
2. Amado, C. A., Santos, S. R., and Marques, P. M. (2012). “Integrating the Data Envelopment Analysis and the Balanced Scorecard approaches for enhanced performance assessment.” Omega, (40), 390-403.
3. Mousavi-Avval, S. H., Mohammadi, A., Rafiee, S., and Tabatabaeefar, A. (2012). “Assessing the technical efficiency of energy use in different barberry production systems.” Journal of Cleaner Production, (27), 126-132.
4. Olanrewaju, O., Jimoh, A., and Kholopane, P. (2012). “Integrated IDA- ANN- DEA for assessment and optimization of energy consUinption in industrial sectors.” Energy, (46), 629- 635.
5. Abri, A. G. (2013). “An investigation on the sensitivity and stability radius of returns to scale and efficiency in data envelopn1ent analysis.” Applied Mathematical Modelling, (37), 1872- 1883.
6. Yang, F., Ang, S., Xia, Q., and Yang, C. (2012). “Ranking DMUs by using interval DEA cross efficiency matrix with acceptability analysis.” European journal of Operational Research, (223), 483-488.
7. Mohammad Arabzad, S., Bahrami, M., and Ghorbani, M. (2012). “Integrating Kano-DEA Models for Distribution Evaluation Problem.” Procedia Social and Behavioral Sciences, (41), 506-512.
8. Riccardi, R., Oggioni, G., and Toninelli, R. (2012). “Efficiency analysis of world cement industry in presence of undesirable output: Application of data envelopment analysis and directional distance function.” Energy Policy, (44), 140-152.
9. Lee, S. K., Mogi, G., and Hui, K. (2013). “A fuzzy analytic hierarchy process (AHP)/data envelopment analysis (DEA) hybrid model for efficiently allocating energy R&D resources: In the case of energy technologies against high oil prices.” Renewable and Sustainable Energy Reviews, (21), 347-355.
10. Tse Kuah, C., Yew Wong, K., and Peng Wongb, W. (2012). “Monte Carlo Data Envelopment Analysis with Genetic Algorithm for Knowledge Management performance measurement.” Expert Systems with Applications, (39), 9348- 9358.
11. Halkos, G. E. and Tzeremes, N. G. (2011). “A conditional nonparametric analysis for measuring the efficiency of regional public healthcare delivery: An application to Greek prefectures.” Health Policy, (103), 73-82.
12. Jahangoshai Rezaee, M., Moini, A., and Makui, A. (2012). “Operational and non-operational performance evaluation of thermal power plants in Iran: A game theory approach.” Energy, (38), 96-103.
13. Xin-zhong, B., Xiao-jun, L., and Ning, W. (2011). “Cost Allocation of Joint-Managed Inventory Based on Cooperative Game and Data Envelopment Analysis.” Industrial Engineering Journal .
14. Liang, L., Cook, W. D., and Zhu, J. (2008). “DEA models for two-stage processes: Game approach and efficiency decomposition.” Naval Research Logistics, 55(7), 643- 653.
15. Azar, A., Zarei, M., and Rostami, A. (2012). “Balanced performance evaluation based on BSC measures (case study on Yazd ceramic tile companies).” Operations research and its applications, 9, 63-79.
16. Zhou, Z., Sun, L., Yang, W., Liu, W., and Ma, C. (2013). “A bargaining game model for efficiency decomposition in the centralized model of two-stage systems.” Computers & Industrial Engineering, (64), 103-108.
Advances in Industrial Engineering
Volume 49, Issue 1 - Serial Number 1
بهار و تابستان
April 2015
Pages 45-54

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