Main Article Content

Abstract

Among many applications, several studies using Data Envelopment Analysis (DEA) have examined and studied the efficiency of supply chains. However, the majority of existing approaches dealing with this research area have ignored the important factor of decision makers’ preferences. The main objective of this article is to provide consistent DEA models that allow for efficiency analysis in order to determine the optimal allocation of resources according to these preferences. We propose three cases that are inspired from the geometric decomposition of preference attributions: (1) horizontal attribution, which is when decision makers treat each supply chain as a single non-detachable entity; (2) vertical attribution, which is when decision makers consider supply chains detachable and (3) combined attribution, which is when decision makers concurrently assign weights to the supply chain and to its members. Based on this suggested decomposition, new DEA models are developed, and an illustrative example is applied. The obtained results are relevant and show that DEA is capable of easily incorporating the preferences of decision-makers without resorting to weight restrictions on inputs or outputs.

Keywords

data envelopment analysis efficiency supply chains decision-makers preferences geometric attribution

Article Details

Author Biography

Walid Abdelfattah, Northern Border University/ College of Arts and Sciences of Rafha

Department of Mathematics
How to Cite
Abdelfattah, W., & Cherif, M. S. (2020). Incorporation of Preferences into Supply Chains DEA Efficiency: A Geometric Attribution Approach. Pakistan Journal of Statistics and Operation Research, 16(4), 761-774. https://doi.org/10.18187/pjsor.v16i4.2578

References

  1. Abdelfattah, W., Rebai, A. (2016). Measurement of dyadic supply chains efficiency under new assumptions using DEA models. Journal of Applied Science 16:445-453.
  2. Alcaide-López-de-Pablo, D., Dios-Palomares, R., Prieto, Á. M. (2014). A new multicriteria approach for the analysis of efficiency in the Spanish olive oil sector by modelling decision maker preferences. European Journal of Operational Research 234:241-252.
  3. Allen R., Athanassopoulos, A., Dyson, R. G., Thanassoulis, E. (1997). Weights restrictions and value judgements in data envelopment analysis: evolution, development and future directions. Annals of Operations Research 73:13-34.
  4. Arabshahi, H., Fazlollahtabar, H., Maboudi, L. (2020). Efficiency Evaluation of Supply Chain Network Using a Framework Based on DEA and Seller-Buyer Structure. Asia-Pacific Journal of Operational Research DOI: 10.1142/S0217595920500293.
  5. Balfaqih H., Nopiah, Z. M., Saibani, N., Al-Nory, M. T. (2016). Review of supply chain performance measurement systems: 1998–2015. Computers in Industry 82:135-150.
  6. Chen, Y., Cook, W. D., Li, N., Zhu, J. (2009). Additive efficiency decomposition in two-stage DEA. European Journal of Operational Research 196:1170-1176.
  7. Chen, Y., Liang, L., Yang, F. (2006). A DEA game model approach to supply chain efficiency. Annals of Operations Research 145:5-13.
  8. Contreras, I. (2011). A DEA-inspired procedure for the aggregation of preferences. Expert Systems with Applications 38:564-570.
  9. Cook, W., Liang, L., Zhu, J. (2010). Measuring performance of two-stage network structures by DEA: a review and future perspective. Omega 38:423-430.
  10. Cook, W. D., Seiford, L. M. (2009). Data envelopment analysis (DEA) – thirty years on. European Journal of Operational Research 192:1-17.
  11. Emrouznejad, A., Parker, B.R. , Tavares, G. (2008). Evaluation of research in efficiency and productivity: a survey and analysis of the first 30 years of scholarly literature in DEA. Socio-Economic Planning Sciences 42:151-157.
  12. Emrouznejad, A., Yang, G. L. (2018). A survey and analysis of the first 40 years of scholarly literature in DEA: 1978-2016. Socio-Economic Planning Sciences 61 (1): 4-8.
  13. Kao, C., Hwang, S.N. (2008). Efficiency decomposition in two-stage data envelopment analysis: an application to non-life insurance companies in Taiwan. Eurpean Journal of Operational Research 185:418-429.
  14. Kaviani, M. A., Abbasi, M. (2014). Analyzing the operations strategies of manufacturing firms using a hybrid grey DEA approach – a case of fars cement companies in Iran. International Journal of Supply Operations Management 1:371-391.
  15. Liang, L., Yang, F., Cook, W. D., Zhu J. (2006). DEA models for supply chain efficiency evaluation. Annals of Operations Research 145:35-49.
  16. Maghbouli, M., Amirteimoori, A., Kordrostami, S. (2014). Two-stage network structures with undesirable outputs: a DEA based approach. Measurement 48:109-118.
  17. Matin, R. K., Azizi, R. (2015). A unified network-DEA model for performance measurement of production systems. Measurement 60:186-193.
  18. Parkan, C., Wang J. (2007). Gauging the performance of a supply chain. International Journal of Productivity and Quality Management (IJPQM) 2:141-176.
  19. Saen, R. (2010). Restricting weights in supplier selection decisions in the presence of dual-role factors. Applied Mathematical Modelling 34:2820-2830.
  20. Seiford, L. M., Zhu, J. (1999). Profitability and marketability of the top 55 U.S. Commercial banks. Management Science 45:1270-1288.
  21. Tavana, M., Kaviani, M. A., Di Caprio, D., Rahpeyma, B. (2016). A two-stage data envelopment analysis model for measuring performance in three-level supply chains. Measurement 78:322-333.
  22. Wang, Y. M., Chin, K.S. (2010). Some alternative DEA models for two-stage process. Expert Systems with Applications 37:8799-8808.
  23. Wang, C. N., Nguyen, T.V., Duong, H.D., Do, T.H. (2018). A Hybrid Fuzzy Analytic Network Process (FANP) and Data Envelopment Analysis (DEA) Approach for Supplier Evaluation and Selection in the Rice Supply Chain. Symmetry 10:221-236.
  24. Wong, W. P. (2009). Performance evaluation of supply chain in stochastic environment: using a simulation based DEA framework. International Journal of Business Performance and Supply Chain Modelling (IJBPSCM) 1:203-228.
  25. Xu, J., Li, B., Wu, D. (2009). Rough data envelopment analysis and its application to supply chain performance evaluation. International Journal of Production Economics 122:628-638.
  26. Yang, F., Wu, D., Liang, L., Bi, G., Wu, D. D. (2009). Supply chain DEA: production possibility set and performance evaluation model. Annals of Operations Research 185:195-211.
  27. Zhao, X., Sun, L. (2008). A preference restraint DEA approach for supplier selection. In: 2008 4th International conference on wireless communications, networking and mobile computing. IEEE, Dalian, China, pp 1-4.
  28. Zhai, D., Shang, J., Yang, F., Ang, S. (2019). Measuring energy supply chains’ efficiency with emission trading: A two-stage frontier-shift data envelopment analysis. J. Clean. Prod. 210:1462-1474.
  29. Zhu, J. (2003). Quantitative models for performance evaluation and benchmarking: data envelopment analysis with spreadsheets. Kluwer Academic Publishers, Boston