An intelligent data-driven model for sustainable-resilient supplier scrutiny and selection in sustainable reconfigurable manufacturing systems

In the research stream of sustainable reconfigurable manufacturing systems, sustainable and resilient supplier scrutiny and selection emerges as a key strategy, emphasising the imperative of integrating sustainability and resiliency into the supply chain fabric. This paradigm extends beyond mere economic evaluations by incorporating environmental stewardship, social responsibility, and resilience against disruptions into the supplier assessment processes. While studies in these fields have made significant strides, there remains a scarcity of research investigating and providing performance appraisal approaches for sustainable-resilient suppliers within such systems. Motivated by addressing the recognised gap, this study aims to identify key criteria for scrutinising sustainable-resilient suppliers through a state-of-the-art review and develop an intelligent data-driven model that incorporates fuzzy logic to effectively navigate uncertainties, leveraging AI-based capabilities to enhance supplier scrutiny and selection in complex systems. To this end, the paper presents an illustrative case to demonstrate the practical configuration of the proposed approach as well as a case study to corroborate its validity. The case studies confirmed that the model effectively assesses sustainable-resilient suppliers, improving precision and execution time while providing insights for informed decision-making through its analytical depth and computational efficiency. Furthermore, sensitivity analysis and its integration into the model are discussed.