Improving maintenance reliability: human-centric strategies for knowledge management and task assignment

Abstract

This study aims to improve maintenance reliability by strategically allocating the workforce, considering human factors and knowledge management in critical asset maintenance within the public transport sector. The study proposes a quantitative-applied approach integrating multi-criteria decision-making (MCDM), an extended risk priority number (ERPN) for task criticality and a modified priority matrix. The algorithm optimizes resource allocation by mathematically combining operator technical aptitude and willingness. Implementation of the algorithm resulted in an average 47% reduction in repair times for critical machinery. This operational efficiency generated an estimated annual saving of $91386.77, proving that 98% of the economic benefit stems directly from minimizing asset downtime rather than reducing direct labor costs. This study was applied in a single transport company, and the results are specific to its operational constraints, which limits direct generalizability to other industrial sectors. Tacit knowledge quantification and task prioritization relied on expert consensus. The model currently assumes full staff availability and does not account for simultaneous unexpected failures. The methodology provides maintenance managers with a structured, data-driven tool to transition from subjective, ad-hoc personnel assignments to an objective protocol. It allows for the systematic integration of knowledge management into computerized maintenance management systems (CMMS), optimizing hour-machine productivity across heavy fleets. The formal recognition of tacit knowledge promotes equity in task allocation and addresses the human reliability gap. By mitigating unequal workloads and recognizing individual technical aptitude, the proposed framework fosters a transparent, motivating and highly engaged work environment, which is critical for sectors operating under severe operational pressure. This study addresses a critical gap in industrial fleet maintenance by mathematically operationalizing not only verified tacit knowledge but also operator willingness, integrating human attitude as a quantifiable variable to reduce system execution entropy.