Reshoring Decisions in Supply Chains and Industry 5.0 Optimization: AI Based Sustainable Decision Support Model

Global supply chains face increasingly uncertain phenomena and reshoring decisions have become a strategic necessity. This paper presents an artificial intelligence-based decision support model for optimizing reshoring processes in connection with sustainability and Industry 5.0 principles. The developed model supports multidimensional decision-making processes in supply chain management by using big data analytics, machine learning and optimization techniques. The proposed framework evaluates critical factors such as lead time, cost, operational risks, environmental impact, and resilience in an integrated approach. Combining different data sources, the model allows decision makers to determine the most appropriate reshoring strategies by conducting dynamic scenario analyses. This approach, which adopts the human-machine collaboration approach of Industry 5.0, not only increases economic and operational efficiency, but also contributes to the principles of sustainable production and supply management. With the model developed in the study, it is aimed to make significant contributions to academic literature and industrial applications by presenting a new perspective on supply chain management and optimization of reshoring decisions.