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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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    References listed on IDEAS

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    Cited by:

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    2. da Silva, Aneirson Francisco & Miranda, Rafael de Carvalho & Marins, Fernando Augusto Silva & Dias, Erica Ximenes, 2024. "A new multiple criteria data envelopment analysis with variable return to scale: Applying bi-dimensional representation and super-efficiency analysis," European Journal of Operational Research, Elsevier, vol. 314(1), pages 308-322.
    3. 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.
    4. Aneirson Francisco Silva & Fernando Augusto S. Marins & Erica Ximenes Dias, 2020. "Improving the discrimination power with a new multi-criteria data envelopment model," Annals of Operations Research, Springer, vol. 287(1), pages 127-159, April.
    5. 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.
    6. Zohreh Sadeghi & Reza Farzipoor Saen & Mahdi Moradzadehfard, 2022. "RETRACTED ARTICLE: Developing a network data envelopment analysis model for appraising sustainable supply chains: a sustainability accounting approach," Operations Management Research, Springer, vol. 15(3), pages 809-824, December.
    7. Hamid Kiaei & Reza Kazemi Matin, 2022. "New common set of weights method in black-box and two-stage data envelopment analysis," Annals of Operations Research, Springer, vol. 309(1), pages 143-162, February.
    8. Gholam R. Amin & Mustapha Ibn Boamah, 2020. "A new inverse DEA cost efficiency model for estimating potential merger gains: a case of Canadian banks," Annals of Operations Research, Springer, vol. 295(1), pages 21-36, December.

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