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Supply chain DEA: production possibility set and performance evaluation model

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Listed:
  • Feng Yang
  • Dexiang Wu
  • Liang Liang
  • Gongbing Bi
  • Desheng Wu

Abstract

Performance evaluation is of great importance for effective supply chain management. The foundation of efficiency evaluation is to faithfully identify the corresponding production possibility set. Although a lot of researches have been done on supply chain DEA models, the exact definition for supply chain production possibility set is still in absence. This paper defines two types of supply chain production possibility sets, which are proved to be equivalent to each other. Based upon the production possibility set, a supply chain CRS DEA model is advanced to appraise the overall technical efficiency of supply chains. The major advantage of the model lies on the fact that it can help to find out the most efficient production abilities in supply chains, by replacing or improving inefficient subsystems (supply chain members). The proposed model also directly identifies the benchmarking units for inefficient supply chains to improve their performance. A real case validates the reasonableness and acceptability of this approach. Copyright Springer Science+Business Media, LLC 2011

Suggested Citation

  • Feng Yang & Dexiang Wu & Liang Liang & Gongbing Bi & Desheng Wu, 2011. "Supply chain DEA: production possibility set and performance evaluation model," Annals of Operations Research, Springer, vol. 185(1), pages 195-211, May.
    • Handle: RePEc:spr:annopr:v:185:y:2011:i:1:p:195-211:10.1007/s10479-008-0511-2
    DOI: 10.1007/s10479-008-0511-2
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    Cited by:

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    3. Konstantinos Petridis & Prasanta Kumar Dey & Ali Emrouznejad, 2017. "A branch and efficiency algorithm for the optimal design of supply chain networks," Annals of Operations Research, Springer, vol. 253(1), pages 545-571, June.
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    5. Patel, Pankaj C. & Tsionas, Mike G., 2022. "Cultural interconnectedness in supply chain networks and change in performance: An internal efficiency perspective," International Journal of Production Economics, Elsevier, vol. 243(C).
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    8. Despotis, Dimitris K. & Koronakos, Gregory & Sotiros, Dimitris, 2016. "The “weak-link” approach to network DEA for two-stage processes," European Journal of Operational Research, Elsevier, vol. 254(2), pages 481-492.
    9. Ching-Chin Chern & Tzi-Yuan Chou & Bo Hsiao, 2016. "Assessing the efficiency of supply chain scheduling algorithms using data envelopment analysis," Information Systems and e-Business Management, Springer, vol. 14(4), pages 823-856, November.
    10. Zhongbao Zhou & Mei Wang & Hui Ding & Chaoqun Ma & Wenbin Liu, 2013. "Further study of production possibility set and performance evaluation model in supply chain DEA," Annals of Operations Research, Springer, vol. 206(1), pages 585-592, July.
    11. Ang, Sheng & Liu, Pei & Yang, Feng, 2020. "Intra-Organizational and inter-organizational resource allocation in two-stage network systems," Omega, Elsevier, vol. 91(C).
    12. Sebastian Lozano & Belarmino Adenso-Diaz, 2018. "Network DEA-based biobjective optimization of product flows in a supply chain," Annals of Operations Research, Springer, vol. 264(1), pages 307-323, May.
    13. Li, Xiang, 2017. "A fair evaluation of certain stage in a two-stage structure: revisiting the typical two-stage DEA approaches," Omega, Elsevier, vol. 68(C), pages 155-167.
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    16. Mehrdokht Pournader & Andrew Kach & Seyed Hossein Razavi Hajiagha & Ali Emrouznejad, 2017. "Investigating the impact of behavioral factors on supply network efficiency: insights from banking’s corporate bond networks," Annals of Operations Research, Springer, vol. 254(1), pages 277-302, July.
    17. HATAMI-MARBINI, Adel & AGRELL, Per & AGHAYI, Nazila, 2013. "Imprecise data envelopment analysis for the two-stage process," LIDAM Discussion Papers CORE 2013004, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    18. Jie Wu & Beibei Xiong & Qingxian An & Jiasen Sun & Huaqing Wu, 2017. "Total-factor energy efficiency evaluation of Chinese industry by using two-stage DEA model with shared inputs," Annals of Operations Research, Springer, vol. 255(1), pages 257-276, August.
    19. Fang, Lei, 2020. "Stage efficiency evaluation in a two-stage network data envelopment analysis model with weight priority," Omega, Elsevier, vol. 97(C).
    20. Koronakos, Gregory & Sotiros, Dimitris & Despotis, Dimitris K. & Kritikos, Manolis N., 2022. "Fair efficiency decomposition in network DEA: A compromise programming approach," Socio-Economic Planning Sciences, Elsevier, vol. 79(C).
    21. Fukuyama, Hirofumi & Matousek, Roman & Tzeremes, Nickolaos G., 2020. "A Nerlovian cost inefficiency two-stage DEA model for modeling banks’ production process: Evidence from the Turkish banking system," Omega, Elsevier, vol. 95(C).
    22. Mahdiloo, Mahdi & Toloo, Mehdi & Duong, Thach-Thao & Farzipoor Saen, Reza & Tatham, Peter, 2018. "Integrated data envelopment analysis: Linear vs. nonlinear model," European Journal of Operational Research, Elsevier, vol. 268(1), pages 255-267.

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