Comparison of supply chain performance evaluation with BSC-FDEA and BSC-RDEA methods in Tabriz automotive industries

Subject Areas : Industrial Management

SINA CHARTAB JABBARI ¹ ( Ph.D Student, Department of Management, Management, Economic and Accounting Faculty , Tabriz Branch , Islamic Azad University , Tabriz, Iran. )
kamaleddin rahmani youshanloui ² ( Assistant prof., Department of Management, Management, Economic and Accounting Faculty, Tabriz Branch, Islamic Azad University, Tabriz, Iran. )
mohammad paseban ³ ( Assistant Professor, Department of Management, Tabriz Branch, Islamic Azad University, Tabriz, Iran )
yagoub Alavi matin ⁴ ( Assistant Prof., Management Faculty, Islamic Azad University Tabriz Branch, Tabriz, Iran )
Mojtaba Ramazani ⁵ ( Department of Management, Bonab Branch, Islamic Azad University, Bonab, Iran )

Received: 2022-09-21 Accepted : 2023-01-23 Published : 2023-02-20

Keywords: Data envelopment analysis, fuzzy number set, rough number set, Supply Chain Efficiency, Automobile manufacturing,

Abstract :

Considering the importance of the issue of efficiency in the advancement of societies and the place it occupies in today's organizations, the use of performance evaluation has become an unavoidable necessity. In the present study, the performance evaluation of five active supply chains with the same structure with the aim of comparing the results of the combined RDEA-BSC and FDEA-BSC models in Tabriz automotive industry and the input and output data in the form of symmetrical triangular fuzzy numbers and the set of uneven numbers to the input and output model The models show the performance of the supply chain. From the Balanced Scorecard (BSC) method as a tool for designing performance evaluation indicators in four aspects; financial, processes, customer and learning and human force growth have been used and also the type of applied-descriptive research and measurement tool is questionnaire, financial documents and information analysis method, FDEA, RDEA, BSC mathematical model and sensitivity analysis. The results of the research show that the efficiency of Amico in each model is higher than other studied companies

References:

• Beamon, B. M. (1999). Measuring supply chain performance. International journal of operations & production management, 19(3), 275-292.


• Charnes, A., Cooper, W. W., & Rhodes, E. (1978). Measuring the efficiency of decision making units. European journal of operational research, 2(6), 429-444.


• Chen, Y., Liang, L., & Yang, F. (2006). A DEA game model approach to supply chain efficiency. Annals of Operations Research, 145(1), 5-13.


• Easton, L., Murphy, D. J., & Pearson, J. N. (2002). Purchasing performance evaluation: with data envelopment analysis. European Journal of Purchasing & Supply Management, 8(3), 123-134.


• Ebrahimnejad, A., Tavana, M., Lotfi, F. H., Shahverdi, R., & Yousefpour, M. (2014). A three-stage data envelopment analysis model with application to banking industry. Measurement, 49, 308-319.


• Eftekhari, H. (2021). A Linear model for performance evaluation of descision making units with DEA in pharmaceutical companies in. Managment Joournal of Sharif univerunivercity, 185, 430-431. [In Persian].


• Estampe, D., Lamouri, S., Paris, J. L., & Brahim-Djelloul, S. (2013). A framework for analysing supply chain performance evaluation models. International Journal of Production Economics, 142(2), 247-258.


• Gunasekaran, A., Patel, C., & McGaughey, R. E. (2004). A framework for supply chain performance measurement. International journal of production economics, 87(3), 333-347.


• Guo, P., & Tanaka, H. (2001). Fuzzy DEA: a perceptual evaluation method. Fuzzy sets and systems, 119(1), 149-160.


• Gh., Hassanzadeh Lotfi, F., Nikomaram, H. (2010). Data Envelopment Analysis and Its Applications. University of Science and Research Publications, [In Persian].


• Xu, J., Li, B., & Wu, D. (2009). Rough data envelopment analysis and its application to supply chain performance evaluation. International Journal of Production Economics, 122(2), 628-638.


• Mirhedayatian, S. M., Azadi, M., & Saen, R. F. (2014). A novel network data envelopment analysis model for evaluating green supply chain management. International Journal of Production Economics, 147, 544-554.


• Nahavandi, N., & Sharifinia, M. (2018). D‌e‌s‌i‌g‌n‌i‌n‌g A M‌o‌d‌e‌l F‌o‌r P‌e‌r‌f‌o‌r‌m‌a‌n‌c‌e E‌v‌a‌l‌u‌a‌t‌i‌o‌n A‌n‌d R‌a‌n‌k‌i‌n‌g O‌f I‌n‌s‌u‌r‌a‌n‌c‌e C‌o‌m‌p‌a‌n‌i‌e‌s B‌y I‌n‌t‌e‌g‌r‌a‌t‌e‌d B‌s‌c-D‌e‌a M‌e‌t‌h‌o‌d. Sharif Journal of Industrial Engineering & Management, 1(2.2), 27-37. doi: 10.24200/j65.2018.5516.


• Shafiee, M., Saleh, H., & Ghaderi, M. (2021). Benchmarking in the Supply Chain Using Data Envelopment Analysis and System Dynamics Simulations. Iranian Journal of Supply Chain Management, 23(70), 55-70.


• Taheri, S. (2020). Productivity analysis in organizations (inclusive productivity management). 28^th Tehran: Hastan Fresh Air Publications. [In Persian].


• Talluri, Srinivas & Narasimhan, Ram & Nair, Anand. (2016). Vendor performance with supply risk: a chance-constrained DEA approach. International Journal of Production Economics, 100 (2), 212–222.


• Wong, W. P., & Wong, K. Y. (2007). Supply chain performance measurement system using DEA modeling. Industrial management & data systems, 212–222.


• Xu, J., Li, B., & Wu, D. (2009). Rough data envelopment analysis and its application to supply chain performance evaluation. International Journal of Production Economics, 122(2), 628-638.