Frontiers and trends of supply chain optimization in the age of industry 4.0: an operations research perspective

Zhitao Xu, Adel Elomri (), Roberto Baldacci, Laoucine Kerbache and Zhenyong Wu
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Zhitao Xu: Nanjing University of Aeronautics and Astronautics
Adel Elomri: Hamad Bin Khalifa University
Roberto Baldacci: Hamad Bin Khalifa University
Laoucine Kerbache: Hamad Bin Khalifa University
Zhenyong Wu: Nanjing University of Information Science and Technology

Annals of Operations Research, 2024, vol. 338, issue 2, No 19, 1359-1401

Abstract: Abstract Industrial 4.0 (I4.0) is believed to revolutionize supply chain (SC) management and the articles in this domain have experienced remarkable increments in recent years. However, the existing insights are scattered over different sub-topics and most of the existing review papers have ignored the underground decision-making process using OR methods. This paper aims to depict the current state of the art of the articles on SC optimization in I4.0 and identify the frontiers and limitations as well as the promising research avenue in this arena. In this study, the systematic literature review methodology combined with the content analysis is adopted to survey the literature between 2013 and 2022. It contributes to the literature by identifying the four OR innovations to typify the recent advances in SC optimization: new modeling conditions, new inputs, new decisions, and new algorithms. Furthermore, we recommend four promising research avenues in this interplay: (1) incorporating new decisions relevant to data-enabled SC decisions, (2) developing data-enabled modeling approaches, (3) preprocessing parameters, and (4) developing data-enabled algorithms. Scholars can take this investigation as a means to ignite collaborative research that tackles the emerging problems in business, whereas practitioners can glean a better understanding of how to employ their OR experts to support digital SC decision-making.

Keywords: Supply chain optimization; Operation research methods; Industrial 4.0; Literature review; Data analytics (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s10479-024-05879-9

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