Pourmehdi, Mohammad and Paydar, Mohammad Mahdi and Ghadimi, Pezhman and Azadnia, Amir Hossein
(2022)
Analysis and evaluation of challenges in the integration of Industry 4.0 and sustainable steel reverse logistics network.
Computers & Industrial Engineering, 163.
p. 107808.
ISSN 03608352
Abstract
Industry 4.0 (I4.0) is a comparatively new phenomenon, and it is most probable that developing countries would
face challenges in adapting it for improving the processes of supply chains and moving toward sustainability. The
steel industry is the core of industrial growth, and it has an indispensable role in the development of countries.
Steel is a highly recyclable product, meaning that it can be reused infinitely, increasing the significance of its
reverse logistics. Although many studies have been conducted in the area of I4.0 and supply chain management,
less attention has been devoted to finding and analyzing potential challenges of I4.0 technologies integration in
steel reverse logistics activities. Therefore, this study is conducted to identify and analyse the challenges to
efficient integration of I4.0 and sustainable steel reverse logistics system. Data collection is conducted with the
assistance of qualified experts familiar with the steel supply chain and I4.0 concept. The interrelations of
challenges are specified by Interpretive Structural Modeling, and the final ranking of challenges is determined
through the Fuzzy Analytical Network Process. After validating the completed questionnaires, the absence of
experts in I4.0, lack of clear comprehension of I4.0 concepts, training programs, and governmental policies and
support are determined as the most critical challenges. Finally, the results and discussion, which can help
practitioners in the efficient adoption of I4.0 to have a sustainable reverse logistics system, are presented.
| Item Type: |
Article
|
| Keywords: |
Steel industry;
Industry 4.0;
Adoption challenges;
Reverse logistics;
Sustainability;
Interpretive structural modelling;
Fuzzy analytical network process; |
| Academic Unit: |
Faculty of Social Sciences > School of Business |
| Item ID: |
17164 |
| Identification Number: |
https://doi.org/10.1016/j.cie.2021.107808 |
| Depositing User: |
Amir Azadnia
|
| Date Deposited: |
09 May 2023 11:38 |
| Journal or Publication Title: |
Computers & Industrial Engineering |
| Publisher: |
Elsevier |
| Refereed: |
Yes |
| URI: |
|
| Use Licence: |
This item is available under a Creative Commons Attribution Non Commercial Share Alike Licence (CC BY-NC-SA). Details of this licence are available
here |
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