Benchmarking in the Supply Chain Using Data Envelopment Analysis and System Dynamics Simulations

Document Type : Research/ Original/ Regular Article

Authors

1 Associate Professor of Industrial Management, Economic and Management Faculty, Shiraz Branch, Islamic Azad University, Shiraz, Iran

2 Department of Applied Mathematics, Tehran Central Branch, Islamic Azad University, Tehran, IRAN

3 Department of Industrial Management, Economic and Management Faculty;ty, Shiraz Branch, Islamic Azad University, Shiraz, Iran

Abstract

Nowadays performance evaluation is necessary for selecting a proper combination of all the supply chain resources in the best possible way, to provide products and services in the market. One approach for measuring the supply chain efficiency is the data envelopment analysis (DEA), which involves the use of past and present inputs and outputs to evaluate the supply chain performance. Therefore, the outcome of the DEA evaluation is not suitable for providing a benchmark for the future. Hence, managers are not able to improve the activities of their subset by using the results of DEA model directly. For this purpose, we forecasted the data of the units under evaluation using system dynamics simulation and then we presented a proper model to formulate strategies for improving performance using the proposed DEA model. Finally, we implemented the designed algorithm in the milk industry of Fars province (Iran), and proper strategies for improving the efficiency of this industry were developed.

Keywords

Main Subjects

References

[1] Golony B. An interactive MOLP procedure for the extension of DEA to effectiveness analysis. Journal of Operational Research Society, 39, 725-34, (1988).##
[2] Stewart Theaodor J. Goal directed benchmarking for organizational efficiency. Omega, 38, 534-539, (2010).##
[3] Teimoory, A., Ahmady, M., Supply Chain Management, Iran University of Science and Technology. (1388).##
[4] Lambert, D. M., Cooper, M. C., Issues in supply chain management. Ind. Market Manag. 29, 65-83, (2000).##
[5] Cooper, M. C., Ellram, L. M., Gardner, J. T., Hanks, A. M., Meshing multiple alliances. J. Bus. Logist. 18, 67-89, (1997).##
[6] Zhu, J., Quantitative Models for Performance Evaluation and Benchmarking: Data Envelopment Analysis with Spreadsheets, Third Edition. Springer Cham Heidelberg New York Dordrecht London. ISBN 978-3-319-06647-9, (2014).##
[7] Seiford, L. M., Zhu, J., Profitability and marketability of the top 55 US commercial banks. Management Science, 45(9), 1270-1288, (1999).##
[8] Chen, Y., Zhu, J., Measuring information technology's indirect impact on firm performance. Information Technology & Management Journal, 5 (1-2), 9-22, (2004).##
[9] Fare, R., Grosskopf, S., Network DEA. Socio- Economic Planning Sciences, 34, 35-49, (2000).##
[10] Golany, B., Hackman, S. T., Passy, U., An efficiency measurement framework for multistage production systems. Annals of Operations Research, 145(1), 51-68, (2006).##
[11] Liang, L., Yang, F., Cook, W. D., Zhu, J., DEA models for supply chain efficiency evaluation. Annals of Operations Research, 145(1), 35-49, (2006).##
[12] Kao, C., Hwang, S. N., Efficiency decomposition in two- stage data envelopment analysis: An application to non- life insurance companies in Taiwan. European Journal of Operational Research, 185(1), 418-429, (2008).##
[13] Liang, L., Cook, W. D., Zhu, J., DEA models for two- stage processes: Game approach and efficiency decomposition. Naval Research Logistics, 55, 643-653, (2008).##
[14] Chen, Y., Cook, W. K., Li, N., Zhu, J., Additive efficiency decomposition in two- stage DEA. European Journal of Operational Research, 196, 1170-1176, (2009).##
[15] Seiford, L. M., Zhu, J., Sensitivity and stability of the classification of returns to scale in data envelopment analysis. Journal of Productivity Analysis, 12(1), 55-75, (1999).##
[16] Liang, L., Yang, F., Cook, W. D., Zhu, J., DEA models for supply chain efficiency evaluation. Annals of Operations Research, 145(1), 35-49, (2006).##
[17] Chen, Y., Liang, L., Yang, F., A DEA game model approach to supply chain efficiency, Ann Oper Res. 145, 5–13, (2006).##
[18] Liang, L., Cook, W. D., Zhu, J., DEA models for two- stage processes: Game approach and efficiency decomposition. Naval Research Logistics, 55, 643-653, (2008).##
[19] Xu, J., Li, B., Wu, D., Rough data envelopment analysis and its application to supply chain performance evaluation. Int. J. Production Economics. 122, 628–638, (2009).##
[20] Yang, F., Wu, D., Liang, L., Bi, G., Wu, D.D., Supply chain DEA: production possibility set and performance evaluation model. Ann Oper Res. 185, 195–211, (2011).##
[21] Chen, C., Yan, H., Network DEA model for supply chain performance evaluation. European Journal of Operational Research. 213, 147–155, (2011).##
[22] Stefanovic, D., Stefanovic, N., Radenkovic, B., Supply network modeling and simulation methodology. Simulation Modelling Practice and Theory 17, 743-766, (2009).##
[23] Cheung, K. L., Hansman, W. H. An exact performance evaluation for the supplier in a two- echelon inventory system. Operations research, 48, 646-653, (2000).##
[24] John, S., Naim, M. M., Towill, D. R., Dynamic analysis of a WIP compensated decision support system. Int. J. Manuf. Syst. Des. 1, 283-297, (1994).##
[25] Campuzano, F., Model of the Demand Variability Management in the Supply Chain. Analysis of the Bullwhip Effect (in Spanish). PhD Thesis. University Politecnica de Valencia, Department of Business Management, CEL Price in 2006 to the Best Doctoral Dissertation, (2006).##

Files

History

  • Receive Date: 15 September 2020
  • Revise Date: 07 July 2021
  • Accept Date: 31 May 2021
  • Publish Date: 22 May 2021

Share

How to cite

Statistics