Lessees' satisfaction and optimal condition-based maintenance policy for leased system

Abstract To avoid the high cost of purchasing equipment, an increasing number of companies are willing to lease rather than own equipment. A lessor aims to improve lessees’ satisfaction, expand market share, increase total profits, and reduce maintenance costs. Owing to the Internet of Things and sensing technology, state detection data on leased equipment can technologically support the implementation of condition-based maintenance (CBM) policies. In this study, we examine optimal maintenance by considering lessees’ satisfaction with leased systems that are periodically inspected. We propose a CBM policy developed to have control limits for a leased system that undergoes periodic inspections, wherein the availability and operational performance are two objective indicators, and the lessees’ expectations concerning availability and operational performance are two subjective indicators. The indicators are used to forecast lessees’ satisfaction and the lessor's market share. Considering the overtime corrective maintenance penalty for each failure, we propose an analytical model to determine the optimal inspection cycle and the preventive maintenance threshold to maximize the lessor's profit. Finally, we use a leased system for cranes as an example in a numerical experiment. The result shows that the policy increases the lessor's market share and total profits.