Abstract
This article investigates spare parts service contracts for capital goods. We consider a single-item, single-location inventory system that serves one customer with multiple machines. During the contract execution phase, the true demand rate is observed. It can differ from the estimated demand rate because of two factors: increased demand variation in finite horizon settings and a shift in the mean utilization of the machines by the user during the contract. When the true demand rate is higher than the estimated demand rate, the Original Equipment Manufacturer (OEM) is faced with higher-than-expected costs for the execution of the contract, and the asset user is generally faced with a higher number of extreme long downtime events. Therefore, we introduce the flexible-time contract, which ends after a predetermined number of demands. Using a Markov decision process, we prove that a state-dependent base stock policy is optimal under a flexible-time contract. Using simulation, we compare the flexible-time contract with the standard fixed-time contract. Our results show that the flexible-time contract reduces the costs for the OEM by up to 35% and prevents not meeting the agreed-on service level. We obtain similar results in a multi-item setting.
Highlights
Users of capital goods rely on diverse equipment from Original Equipment Manufacturers (OEMs) to deliver their products or services
When the true demand rate is higher than the estimated demand rate, the Original Equipment Manufacturer (OEM) is faced with higher-than-expected costs for the execution of the contract, and the asset user is generally faced with a higher number of extreme long downtime events
We focus on the setting with ptrue 1⁄4 0:125 and a 1⁄4 0:5: Figures 7 and 8 show how the mean costs and the mean number of XLDs decrease depending on the path chosen by the OEM and the asset user
Summary
Users of capital goods rely on diverse equipment from Original Equipment Manufacturers (OEMs) to deliver their products or services. As penalty costs represent only a fraction of the real customer impact, underestimating the utilization levels of the systems by the asset user and, the spare parts demand rate by the OEM is a joint problem for the parties involved. This is in line with the findings of Gebauer et al (2017) for costplus contracts and Visnjic et al (2017) for performancebased contracts. By defining the contract over a predefined number of spare parts demand, determining the expected costs is more directly linked to the service being provided This should help reduce the risks for the OEMs in terms of costs and for the asset user in terms of performance. What is the impact of updating the demand rate forecast on the performance of the two contracts? And
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