Maintenance strategy selection: a state of the art for practitioners

Abstract

In the face of a competitive industrial landscape, optimising maintenance strategies is critical for manufacturing companies striving to improve operational efficiency, safety and cost-effectiveness. Maintenance strategy selection (MSS) is crucial in mitigating downtime and enhancing system performance, where inadequate strategy selection can increase costs and risk. This paper investigates the current methodologies for selecting maintenance strategies, focusing on economic, technical and safety criteria that challenge decision-makers. Through a systematic literature review (SLR), recent developments in maintenance strategies are explored, identifying key approaches such as preventive, predictive and condition-based maintenance alongside newer predictive models powered by machine learning and artificial intelligence (AI). This study highlights the widespread use of MCDM methods – especially AHP, TOPSIS and DEMATEL – in supporting MSS, often enhanced with fuzzy or hybrid approaches. Traditional strategies like corrective and time-based maintenance remain common, but there is a growing shift towards data-driven approaches such as condition-based and predictive maintenance. Technical and economic criteria are the most frequently used, with safety and feasibility also playing key roles. Recent literature shows increased focus on environmental social and governance related factors like energy consumption and sustainability. The analysis reveals a trend from static decision models towards integrated, AI-supported frameworks, reflecting evolving industrial priorities. This study provides a comprehensive overview of state of the art maintenance strategies, offering insights for industrial managers and researchers aiming to refine decision-making in maintenance management.