Optimising spare parts management in hydraulic excavators: a Fuzzy CODAS and ABC analysis approach

The reliable operation of heavy construction equipment is critical for project productivity; however, spare parts inventory decisions for such equipment are often made using intuition-based or oversimplified approaches, leading to unnecessary costs and equipment downtime. In particular, construction-specific spare parts management lacks systematic, data-driven frameworks that account for uncertainty, multiple decision criteria, and operational criticality. To address this gap, this study proposes a hybrid decision-support framework that integrates Fuzzy Combinative Distance-Based Assessment (Fuzzy CODAS) with Always Better Control (ABC) analysis to optimise spare parts inventory for hydraulic excavators. The framework is applied to the hydraulic system of excavators using 22 spare parts and 13 evaluation criteria assessed by industry experts. The results indicate that pump devices, gear pumps, and regulator pumps should be prioritised as Class A items due to their high criticality, cost impact, and failure consequences, while components such as cylinders, centre joints, and certain piping systems exhibit lower stocking priority. The findings demonstrate that component-specific inventory strategies can significantly reduce operational costs and improve equipment uptime. Theoretically, this study advances spare parts management literature by introducing a construction-oriented hybrid MCDM framework, while providing an evidence-based tool for strategic inventory planning and maintenance decision-making in heavy equipment operations.