Multi-criteria classification of spare parts in the steel industry
DOI:
https://doi.org/10.14488/BJOPM.2344.2025Keywords:
Analytic hierarchy process, Multi-criteria decision-making, Inventory management, Steel industry, Spare parts classificationAbstract
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.
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