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Supply chain performance evaluation using fuzzy network data envelopment analysis: a case study in automotive industry

Author

Listed:
  • Yongbo Li

    (China University of Petroleum (East China))

  • Amir-Reza Abtahi

    (Kharazmi University)

  • Mahya Seyedan

    (Concordia University)

Abstract

Supply chain performance evaluation problems are evaluated using data envelopment analysis. This paper proposes a fuzzy network epsilon-based data envelopment analysis for supply chain performance evaluation. In the common data envelopment analysis models which are used for evaluation of decision-maker units efficiency, there are several inputs and outputs. One of the bugs of such models is that the intermediate products and linking activities are overlooked. Considering these intermediate activities and products, the current study evaluates the performance of decision-maker units in an automotive supply chain. There are ten decision-maker units in the supply chain in which there are three suppliers, two manufacturers, two distributors, and four customers. Moreover, the overall efficiency of input-oriented (input-based) model and input-oriented divisional efficiency are calculated. In order to improve the efficiencies, the projections onto the frontiers are obtained by using the outputs of the solved model and Lingo software. In order to show the applicability of the proposed model, it is applied on automotive industry, as a case study, to evaluate supply chain performance. Then, the overall efficiencies of DMUs and each sections (divisions) of DMUs were calculated separately. Therefore, every organization can apply this evaluation method for improving the performance of alternative factors.

Suggested Citation

  • Yongbo Li & Amir-Reza Abtahi & Mahya Seyedan, 2019. "Supply chain performance evaluation using fuzzy network data envelopment analysis: a case study in automotive industry," Annals of Operations Research, Springer, vol. 275(2), pages 461-484, April.
  • Handle: RePEc:spr:annopr:v:275:y:2019:i:2:d:10.1007_s10479-018-3027-4
    DOI: 10.1007/s10479-018-3027-4
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    1. Mehdi Toloo & Madjid Tavana, 2017. "A novel method for selecting a single efficient unit in data envelopment analysis without explicit inputs/outputs," Annals of Operations Research, Springer, vol. 253(1), pages 657-681, June.
    2. Mirhedayatian, Seyed Mostafa & Azadi, Majid & Farzipoor Saen, Reza, 2014. "A novel network data envelopment analysis model for evaluating green supply chain management," International Journal of Production Economics, Elsevier, vol. 147(PB), pages 544-554.
    3. Chen, Ci & Yan, Hong, 2011. "Network DEA model for supply chain performance evaluation," European Journal of Operational Research, Elsevier, vol. 213(1), pages 147-155, August.
    4. Govindan, K. & Jafarian, A. & Khodaverdi, R. & Devika, K., 2014. "Two-echelon multiple-vehicle location–routing problem with time windows for optimization of sustainable supply chain network of perishable food," International Journal of Production Economics, Elsevier, vol. 152(C), pages 9-28.
    5. Kannan, Devika & Jabbour, Ana Beatriz Lopes de Sousa & Jabbour, Charbel José Chiappetta, 2014. "Selecting green suppliers based on GSCM practices: Using fuzzy TOPSIS applied to a Brazilian electronics company," European Journal of Operational Research, Elsevier, vol. 233(2), pages 432-447.
    6. Azadi, Majid & Shabani, Amir & Khodakarami, Mohsen & Farzipoor Saen, Reza, 2014. "Planning in feasible region by two-stage target-setting DEA methods: An application in green supply chain management of public transportation service providers," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 70(C), pages 324-338.
    7. 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.
    8. Merja Halme & Tarja Joro & Pekka Korhonen & Seppo Salo & Jyrki Wallenius, 1999. "A Value Efficiency Approach to Incorporating Preference Information in Data Envelopment Analysis," Management Science, INFORMS, vol. 45(1), pages 103-115, January.
    9. Korhonen, Pekka & Tainio, Risto & Wallenius, Jyrki, 2001. "Value efficiency analysis of academic research," European Journal of Operational Research, Elsevier, vol. 130(1), pages 121-132, April.
    10. Olfat, Laya & Amiri, Maghsoud & Bamdad Soufi, Jahanyar & Pishdar, Mahsa, 2016. "A dynamic network efficiency measurement of airports performance considering sustainable development concept: A fuzzy dynamic network-DEA approach," Journal of Air Transport Management, Elsevier, vol. 57(C), pages 272-290.
    11. TAVANA, Madjid & SHIRAZ, Rashed Khanjani & HATAMI-MARBINI, Adel & AGRELL, Per J., 2012. "Fuzzy stochastic data envelopment analysis with application to base realignment and closure (BRAC)," LIDAM Reprints CORE 2412, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    12. Tone, Kaoru & Tsutsui, Miki, 2010. "An epsilon-based measure of efficiency in DEA - A third pole of technical efficiency," European Journal of Operational Research, Elsevier, vol. 207(3), pages 1554-1563, December.
    13. Tone, Kaoru & Tsutsui, Miki, 2009. "Network DEA: A slacks-based measure approach," European Journal of Operational Research, Elsevier, vol. 197(1), pages 243-252, August.
    14. Kannan, Devika, 2018. "Role of multiple stakeholders and the critical success factor theory for the sustainable supplier selection process," International Journal of Production Economics, Elsevier, vol. 195(C), pages 391-418.
    15. Govindan, Kannan & Soleimani, Hamed & Kannan, Devika, 2015. "Reverse logistics and closed-loop supply chain: A comprehensive review to explore the future," European Journal of Operational Research, Elsevier, vol. 240(3), pages 603-626.
    16. Kao, Ta-Wei (Daniel) & Simpson, N.C. & Shao, Benjamin B.M. & Lin, Winston T., 2017. "Relating supply network structure to productive efficiency: A multi-stage empirical investigation," European Journal of Operational Research, Elsevier, vol. 259(2), pages 469-485.
    17. Kaveh Khalili-Damghani & Mohammad Taghavifard, 2012. "A three-stage fuzzy DEA approach to measure performance of a serial process including JIT practices, agility indices, and goals in supply chains," International Journal of Services and Operations Management, Inderscience Enterprises Ltd, vol. 13(2), pages 147-188.
    18. Tone, Kaoru, 2001. "A slacks-based measure of efficiency in data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 130(3), pages 498-509, May.
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    Cited by:

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    4. Mohsen Abbaspour Onari & Mustafa Jahangoshai Rezaee, 2022. "A fuzzy cognitive map based on Nash bargaining game for supplier selection problem: a case study on auto parts industry," Operational Research, Springer, vol. 22(3), pages 2133-2171, July.
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    7. Shiva Moslemi & Abolfazl Mirzazadeh & Gerhard-Wilhelm Weber & Mohammad Ali Sobhanallahi, 2022. "Integration of neural network and AP-NDEA model for performance evaluation of sustainable pharmaceutical supply chain," OPSEARCH, Springer;Operational Research Society of India, vol. 59(3), pages 1116-1157, September.

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