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A Bayesian BWM-Based Approach for Evaluating Sustainability Measurement Attributes in the Steel Industry

In: Advances in Best-Worst Method

Author

Listed:
  • Iman Ghasemian Sahebi

    (University of Tehran)

  • Seyed Pendar Toufighi

    (University of Tehran)

  • Alireza Arab

    (University of Tehran)

Abstract

Nowadays steel industry is one of the industries that plays an essential role in countries’ growth. Today, the integration of sustainability in the steel industry’s supply chain has become a significant concern of industry owners and researchers. Therefore, this study aims to identify and evaluate supply chain sustainability attributes in the steel industry. The experts’ panel in this study consisted of 7 senior and middle managers selected by the snowball sampling method. In the first step, the literature reviewed to identify supply chain sustainability attributes that 16 attributes extracted. In the second step, by using the Fuzzy Delphi method and using experts’ opinions, the extracted attributes were screened and customized. Five attributes in the economic dimension, four attributes in the environmental dimension, and five attributes in the social dimension were identified. In the third step, by using the Bayesian Best Worst Method (BWM), the customized attributes were weighted and prioritized. The results showed that the economical dimension was determined as the most important sustainability dimension. Also, among all attributes of the problem, market share, profitability, and waste recycling were recognized as the most important ones, respectively.

Suggested Citation

  • Iman Ghasemian Sahebi & Seyed Pendar Toufighi & Alireza Arab, 2022. "A Bayesian BWM-Based Approach for Evaluating Sustainability Measurement Attributes in the Steel Industry," Lecture Notes in Operations Research, in: Jafar Rezaei & Matteo Brunelli & Majid Mohammadi (ed.), Advances in Best-Worst Method, pages 175-193, Springer.
    • Handle: RePEc:spr:lnopch:978-3-030-89795-6_13
    DOI: 10.1007/978-3-030-89795-6_13
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