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

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Advances in Best-Worst Method (BWM 2021)

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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.

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Notes

  1. 1.

    This Table elicited from extensive sustainable supply chain literature (for more details, can contact to corresponding author).

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Authors and Affiliations

  1. Faculty of Management, University of Tehran, Tehran, Iran

    Iman Ghasemian Sahebi, Seyed Pendar Toufighi & Alireza Arab

Authors
  1. Iman Ghasemian Sahebi
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  2. Seyed Pendar Toufighi
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  3. Alireza Arab
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Corresponding author

Correspondence to Iman Ghasemian Sahebi .

Editor information

Editors and Affiliations

  1. Delft University of Technology, Delft, Zuid-Holland, The Netherlands

    Jafar Rezaei

  2. Department of Industrial Engineering, University of Trento, Trento, Italy

    Matteo Brunelli

  3. Department of Computer Science, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands

    Majid Mohammadi

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Sahebi, I.G., Toufighi, S.P., Arab, A. (2022). A Bayesian BWM-Based Approach for Evaluating Sustainability Measurement Attributes in the Steel Industry. In: Rezaei, J., Brunelli, M., Mohammadi, M. (eds) Advances in Best-Worst Method. BWM 2021. Lecture Notes in Operations Research. Springer, Cham. https://doi.org/10.1007/978-3-030-89795-6_13

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