Interval Type-2 Fuzzy Sets Framework with a Digital Twin Prototype for Simulation-Supported Supplier Selection Under Industry 5.0

Supplier selection is a critical strategic decision in supply chain management, with a direct impact on operational performance, competitiveness, and long-term sustainability. The contemporary business environment, characterised by globalisation, climate change, and increasingly stringent regulatory requirements, necessitates an expanded supplier evaluation framework that encompasses the dimensions of resilience and sustainability. Industry 5.0 and digital twins (DT) are advanced concepts that enable improvements in the supplier selection process through the integration of advanced technologies and human knowledge. Using a five-member expert panel from different departments, both criterion importance and the qualitative supplier ratings were assessed individually by each expert; quantitative criteria were derived from a preliminary DT prototype running documented scenario simulations, in the case of an agricultural company operating in south-eastern Bosnia and Herzegovina. Interval type-2 fuzzy sets (IT2FS) were applied, with asymmetric fuzzy numbers used in the membership function. The results obtained using IT2FS Simple Weight Calculation (SiWeC) showed that total costs, recovery capability following disruptive events, and product and service quality were the most important criteria. Qualitative supplier ratings were aggregated by unweighted arithmetic mean, while criterion importance was determined through IT2FS SiWeC. The six selected suppliers were ranked using IT2FS Multi-Attributive Border Approximation Area Comparison (MABAC), which demonstrated that supplier SUPP1 achieved the best results according to the expert assessments and the DT-based scenario simulations. These findings were additionally confirmed through comparative and sensitivity analyses. This study contributes practical guidance regarding the criteria and suppliers that agricultural enterprises should select in order to improve the procurement process and reduce supply chain risks. This research presents a framework for supplier selection based on aggregated DT output data. The DT prototype used in this study is a preliminary, uncalibrated simulation system whose outputs are treated as simulation-based estimates rather than validated real-time measurements. This limitation is stated explicitly, and the calibration of the DT against actual supplier performance is identified as a direction for future research. This framework can be extended to real-time dynamic updating, which is a direction for future research.