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Supply chain performance measurement using SCOR model based on interval-valued fuzzy TOPSIS

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  • Arezoo Moharamkhani
  • Ali Bozorgi-Amiri
  • Hassan Mina

Abstract

Performance measurement is known to be the best way of investigating the supply chains' success. In this regard, managers can identify the root causes of weakness points and improve supply chain's performance through analysing and solving these problems. Concerning this problem, various supply chain performance evaluation models have been presented in literature. Through all of the models, this paper used supply chain operations reference (SCOR) model for performance measurement of three Iranian automotive supply chains. First SCOR model is employed to define the performance criteria. Afterwards, technique for order of preference by similarity to ideal solution (TOPSIS) is used to determine the supply chain that performs best. In this paper, expert's judgment is used to determine the criteria's value and weight but uncertainties in expert's judgment was unavoidable, also experts cannot reach an agreement on the method of defining linguistic variables based on fuzzy sets. So, paper used interval-valued fuzzy set to solve these problems. To the best of authors' knowledge, this is the first study that have applied interval-valued fuzzy TOPSIS in group decision-making in order to evaluate and improve the performance of supply chains on the basis of SCOR model.

Suggested Citation

  • Arezoo Moharamkhani & Ali Bozorgi-Amiri & Hassan Mina, 2017. "Supply chain performance measurement using SCOR model based on interval-valued fuzzy TOPSIS," International Journal of Logistics Systems and Management, Inderscience Enterprises Ltd, vol. 27(1), pages 115-132.
    • Handle: RePEc:ids:ijlsma:v:27:y:2017:i:1:p:115-132
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    Cited by:

    1. Thi Thuy Hanh Nguyen, 2024. "Measuring Supply Chain Performance Using the SCOR Model," SN Operations Research Forum, Springer, vol. 5(2), pages 1-28, June.
    2. Lima-Junior, Francisco Rodrigues & Carpinetti, Luiz Cesar Ribeiro, 2019. "Predicting supply chain performance based on SCOR® metrics and multilayer perceptron neural networks," International Journal of Production Economics, Elsevier, vol. 212(C), pages 19-38.

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