This study introduces a fuzzy-ontological expert system for ranking pharmaceutical supply plans in healthcare facilities. The approach integrates fuzzy logic’s capability to model vague, incomplete information with an OWL 2 ontology that standardises criteria and supports future extensibility. Four key attributes—procurement cost, delivery time, residual shelf-life and supplier reliability—are mapped to five linguistic levels and connected through a knowledge base of 13 Mamdani rules. The resulting inference engine produces a unified score (plan_score) that enables decision-makers to compare heterogeneous plans on a common scale. A lightweight desktop prototype was developed in Python 3.11, employing scikit-fuzzy for rule evaluation and Tkinter for an intuitive graphical interface. Supply plans are imported from XML, evaluated in under 0.3, and displayed in a sortable table that highlights top-ranked alternatives. Pilot testing reduced expert analysis time by a factor and yielded rankings consistent with procurement committee decisions. The research demonstrates the practical value of combining fuzzy reasoning and ontological modelling in medical supply management. Planned extensions include automatic tuning of membership functions, incorporation of environmental and shortage-risk criteria, and deployment of a web microservice for seamless integration with existing ERP systems in practice.