Facility sustainment funding with conditional reliability models

Purpose Existing Department of War methods for assigning facility maintenance budgets rely on uniform funding level models from an average facility of the corresponding building type. Such an approach does not factor in facility age, condition, unique inventory or environmental factors. Current optimization approaches tend to scale poorly in scenarios that may require millions of individual component-level decisions. There is a need for more prescriptive, reliability-focused facility sustainment funding methods capable of being applied at a large scale. Design/methodology/approach An enhanced statistical reliability-centered maintenance (RCM) framework is proposed that integrates meaningful covariates like climate zones and inspection data. These models are used to assign optimal maintenance intervals to maximize the return-on-investment (ROI) from renewal activities that extend equipment service life. This approach is further amended to incorporate budget constraints and adjust metrics to compensate for deferred maintenance. Findings Case studies against 30 facilities from general administrative buildings and barracks show that the average sustainment levels using the developed method are reasonable compared to existing budget levels and industry standards, but provide much more prescriptive funding tailored to the individual facility inventory. Time tests also show the approach to be a highly scalable method, capable of generating millions of component-level decisions without the need for high-performance computing resources. Practical implications The developed framework provides a scalable approach to assigning facility maintenance funding rooted in reliability-centered maintenance. Compared to competing approaches, it implements better risk-based decisions via covariate-based reliability models and can be scaled without high-performance computing resources or excessive decision heuristics. Social implications Because the developed model helps more efficiently allocate funding, it provides a better use of budgets, particularly in large-scale public domains. In addition, the method implements climate-based reliability models that can adjust risk-based decisions due to climate change. Originality/value The proposed approach develops a novel, scalable approach to assigning optimum maintenance activities suitable for large organizations. Reliability models with climate covariates are built from historical component inspections to inform the model. Compared to competing approaches that implement integer programming methods, this approach transforms the problem into a continuous variable optimization problem so that more efficient algorithms provide scalable solutions. Additionally, the method introduces an adjustment approach to better manage the risk of deferred maintenance.