Abstract
Prescriptive maintenance extends predictive maintenance by not only forecasting potential failures, but also recommending concrete maintenance actions, explicitly accounting for operational, organizational, and resource-related constraints. Existing approaches in this area are often fragmented, as they typically focus either on technical diagnostics or on isolated optimization criteria, without integrating production schedules, resource availability, and organizational decision constraints into a unified decision- making framework. This creates a research gap in the integration of diagnostic and organizational data, thereby limiting real-time decision-making. The aim of this study was to develop and validate a hybrid prescriptive maintenance algorithm, combining fuzzy logic, neural network classification, and Analytic Hierarchy Process (AHP)-based scenario evaluation, aligned with the principles of Industry 4.0. The algorithm was implemented in MATLAB. A central design feature was the integration of sensor-based measurement data with organizational inputs to dynamically generate decision scenarios, accounting for time, cost, and resource constraints. This integration enabled the algorithm to consistently generate recommendations that respected budgetary and temporal constraints, demonstrating its effectiveness in translating combined technical and organizational data into actionable maintenance decisions. The algorithm was validated using real-world production data, achieving decision accuracy in the range of 94–99%, defined as consistency with expert-validated reference decisions, with a relative prediction deviation below 0.05%. It was further observed that when organizational and contextual data were not considered (such as production schedules, resource availability, and personnel qualifications), over 85% of the recommended maintenance scenarios deviated from the reference decisions determined based on realized production outcomes, with deviation assessed in terms of completion time and cost. The findings demonstrate that the developed prescriptive maintenance algorithm significantly improves decision accuracy, optimizes resource utilization, and reduces both downtime and operational costs, highlighting its practical impact and value for industrial maintenance management.