An optimum condition-based replacement and spare provisioning policy based on Markov chains

Purpose ‐ The purpose of this paper is to develop a condition-based replacement and spare provisioning policy for deteriorating systems with a number of identical units. Design/methodology/approach ‐ The deterioration of units is modeled based on discrete-time Markov chains, which can be classified into one of a finite number of states. Then, a condition-based replacement and spare provisioning policy is proposed for deteriorating systems with a number of identical units. This policy combines the condition-based replacement policy and the (S, s) type inventory policy, where S is the maximum stock level and s is the reorder level. The Monte Carlo approach is utilized for evaluating the average cost rate of the system under the proposed policy. Finally, numerical examples are given to illustrate the performance of the proposed policy, as well as the sensitivity analysis of cost parameters. Findings ‐ The negative influences of increasing the lead time can be reduced by optimizing the decisions of condition-based replacement and spare order based on the proposed policy. Practical implications ‐ This policy would be applicable for jointly optimizing the spare provisioning decisions and the condition-based maintenance of the units in deteriorating systems (e.g. a group of identical motors included in a fleet of vehicles). Originality/value ‐ The paper considers simultaneously two aspects that influence condition-based maintenance decisions: the availability of spares and the deterioration state of units.