Artificial Intelligence Enabled Reshoring Decisions: An Industry 5.0 Based Framework
DOI:
https://doi.org/10.63556/tisej.2026.1591Keywords:
Supply chain, optimization, sustainability, industry 5.0, artificial intelligenceAbstract
Increasing uncertainties in global supply chains have made reshoring (the relocation of production activities back to the country of origin) operations a strategic necessity. The aim of this study is to present an AI-based integrated decision support model that optimizes reshoring decisions in terms of cost, resilience, and sustainability in line with Industry 5.0 principles. The methodology of the research is based on processing the strategic parameters identified through systematic literature synthesis in a five-stage hybrid analytical process. Within this framework, SVM and LSTM algorithms have been integrated for operational status detection, ARIMAX for forecasting future demand, Monte Carlo Simulation for risk analysis under uncertainty, and DES techniques for operational workflow optimization. Strategic risk scoring is performed using the RF algorithm, thereby establishing the model's decision-making mechanism. The model, reflecting the cyber-physical and human-centered paradigms of Industry 5.0, contributes to both performance optimization and sustainability compliance. Overall, this research contributes methodologically to the modeling of reshoring processes and provides actionable insights for both academic inquiry and industrial implementation.
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