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We study optimal data pooling for shared learning in two common maintenanceoperations: condition-based maintenance and spare parts management. We considera set of systems subject to Poisson input -- the degradation or demand process-- that are coupled through an a-priori unknown rate. Decision problemsinvolving these systems are high-dimensional Markov decision processes (MDPs)and hence notoriously difficult to solve. We present a decomposition resultthat reduces such an MDP to two-dimensional MDPs, enabling structural analysesand computations. Leveraging this decomposition, we (i) demonstrate thatpooling data can lead to significant cost reductions compared to not pooling,and (ii) show that the optimal policy for the condition-based maintenanceproblem is a control limit policy, while for the spare parts managementproblem, it is an order-up-to level policy, both dependent on the pooled data.

Optimal data pooling for shared learning in maintenance operations