Intelligent modelling of supplier assessment and selection in logistics based on fuzzy control technologies

The article aims to develop an intelligent model for assessing and selecting suppliers in logistics based on fuzzy control technologies. Methodologically, the research rests on Mamdani's theory of fuzzy sets and logical inference, which provides the formalisation of expert judgements and work with linguistic variables. The model uses membership functions that reflect the expression degree of factors such as supply stability, quality, price competitiveness, flexibility, financial stability, and digital maturity of a supplier. The information base includes scientific studies in the field of supply chain management, as well as expert assessments of logistical characteristics. The authors have implemented the model in Python, including the visualisation of key factors' impact on the final reliability assessment. The results allow identifying the most significant groups of factors affecting supplier reliability and serve as a decision- making support tool in forming supply chains. The model demonstrates interpretability, adaptability, and resilience to input data uncertainty.

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