Frontiers and Trends of Supply Chain Optimization in the Age of Industry 4.0 : An Operations Research Perspective

The growing implementation of industrial 4.0 (I4.0) equips the supply chain (SC) as a smart and data-driven system to deliver products and services in a more accessible and affordable manner. This need for better decision making has stimulated the SC community to explore data-intensive optimization. There is a remarkable increase in the number of articles in this domain; however, a review of the present status of I4.0 in SC management from an Operation Research (OR) perspective is still lacking. In this study, 213 articles were systematically reviewed to survey the recent advances in OR methodologies for SC optimization in the I4.0 context. In this review, we identified four new features to typify recent advances in SC optimization: new modeling conditions, new inputs, new decisions, and new algorithms. We recommend potential future research avenues for innovation in OR methods for SC optimization: 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. The deep analysis of the OR methods and the rich I4.0 context in which the models are embedded would provide valuable insights to the academic community and industry in exploiting SC optimization at different decision levels