Maintenance optimisation remains a persistent challenge in asset-intensive industries due to the combined uncertainties of failure behaviour and the economic consequences of strategy selection. Traditional decision frameworks typically rely on static cost comparisons or rule-based policies, which do not adequately reflect how hazard rates evolve across different lifecycle stages. This limitation creates a critical gap in linking risk-based reliability analysis with cost-driven evaluation, leaving decision makers without a robust basis for aligning maintenance strategies to both asset condition and economic impact. The objective of this study is to develop and test a framework that integrates hazard rate modelling with cost-based analysis to guide optimised maintenance decision-making. The approach applies simulation-based methods using Weibull distributions to characterise hazard rates across infant, random, and wear-out phases. Strategy performance is then evaluated through a process of normalisation and binning, which enables comparability across heterogeneous datasets and accounts for differences in data richness. Opportunity cost is introduced as the central decision metric, capturing the economic penalty incurred when a suboptimal strategy is implemented under specific hazard conditions. Findings reveal that no maintenance strategy is universally superior across all conditions. Corrective maintenance is viable only in low-hazard situations, but quickly becomes economically untenable as hazard rates increase. Time-based maintenance consistently demonstrates effectiveness in predictable environments and dominates in wear-out stages where escalating risk demands proactive intervention. Condition-based maintenance proves particularly valuable in situations where monitoring can offset uncertainty, offering a cost-effective balance between reactivity and prevention. Importantly, the results show that the framework is flexible: it remains meaningful in data-scarce contexts, where transparent assumptions allow useful insights, while in data-rich scenarios it produces stable parameter estimates and clearer economic benchmarks. This study contributes to the academic literature by bridging the gap between reliability modelling and economic evaluation. By integrating hazard functions with opportunity cost benchmarking, it advances beyond static cost comparisons to a dynamic, lifecycle-aware assessment of strategy performance. Practically, the framework provides maintenance managers with a transparent and adaptable decision-support tool that clarifies both when a strategy is most effective and what is lost when the wrong choice is made. In doing so, it enhances the ability of organisations to make defensible, data-informed decisions that minimise risk and maximise economic efficiency in asset management.
More et al. (Thu,) studied this question.