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

Iman Ghasemian Sahebi (), Seyed Pendar Toufighi and Alireza Arab
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Iman Ghasemian Sahebi: University of Tehran
Seyed Pendar Toufighi: University of Tehran
Alireza Arab: University of Tehran

A chapter in Advances in Best-Worst Method, 2022, pp 175-193 from Springer

Abstract: 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.

Keywords: Sustainable supply chain; Bayesian best worst method; Steel industry (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:spr:lnopch:978-3-030-89795-6_13

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DOI: 10.1007/978-3-030-89795-6_13

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