Multi-criteria classification of spare parts in the steel industry

Goal: This research addresses the critical challenge of evaluating spare parts inventory in the steel industry to enhance maintenance efficiency and reduce operational costs. Design/methodology/approach: The study applies the Analytic Hierarchy Process (AHP), a widely recognized multi-criteria decision-making (MCDM) method, to develop a robust decision support system. A hierarchical structure of criteria and sub-criteria, along with alternatives (spare parts), was constructed based on an extensive literature review and validated through input from three maintenance and inventory management experts. The system was implemented in a Brazilian steel plant. Results: The AHP-based framework systematically classified spare parts, emphasizing their criticality. Spare Parts 1 and 2 were categorized as Class B, scoring 0.6 and 0.56, while Spare Parts 3 and 4 were classified as Class A, scoring 0.82 and 0.83. These findings confirm the effectiveness of the AHP methodology in prioritizing spare parts for improved inventory management and decision-making. Sensitivity analysis validated the framework's robustness, demonstrating stable classifications across varying criteria weights. Limitations of the investigation: While tailored to a Brazilian steel plant, the framework's scalability is evident. Limitations include its reliance on a specific context and the involvement of a limited number of experts, suggesting opportunities for broader validation. Practical implications: The simplified AHP framework gives managers an accessible tool for classifying spare parts, eliminating the need for complex hybrid methods. It enables efficient decision-making, particularly in industries with high operational demands. Originality: This research contributes a novel multi-criteria decision-making model for spare parts classification, significantly advancing maintenance efficiency and cost-effectiveness compared to traditional single-criterion approaches.