Warranty Reserve Management

Warranty Reserve Management: Demand Learning and Funds Pooling

Wang, Xiao Lin (Business School of Sichuan University)
Zhong, Yuanguang (South China University of Technology)
Li, L. (TU Delft Air Transport & Operations)
Xie, Wei (South China University of Technology)
Ye, Zhi Sheng (National University of Singapore)

2022

Problem definition: Warranty reserves are funds used to fulfill future warranty obligations for a product. In this paper, we investigate the warranty reserve planning problem faced by a manufacturing firm who manages warranties for multiple products. Academic/practical relevance: It is nontrivial to determine a proper amount of reserves to hold, because warranty expenditures are random in nature and reserving either excess or insufficient cash would incur losses. How can warranty reserve levels be optimized and promptly adjusted is a focal issue, especially for firms selling multiple products. Methodology: Inspired by the general pattern of empirical warranty claims data, we first develop an aggregate warranty cost (AWC) forecasting model for a single product by coupling stochastic product sales and failure processes, which can be used to plan for warranty reserves periodically. The reserve levels are then optimized via a distributionally robust approach, because the exact distribution of AWC is generally unknown. To reduce the losses generated from managing the funds, we further investigate two potential loss-reduction approaches: demand learning and funds pooling. Results: For the demand learning algorithm, we prove that, as the sales period grows, the optimal learning parameter asymptotically converges to a constant in a fairly fast rate; our simulation experiments show that the performance of demand learning is promising and robust under general warranty claim patterns. Moreover, we find that the benefits of funds pooling change over different stages of the warranty life cycle; in particular, the relative pooling benefit in terms of reserve losses is nonincreasing over time. Managerial implications: This study offers guidelines on how manufacturers should adaptively forecast and dynamically plan warranty reserves over the warranty life cycle.

adaptive learning
distribution free
newsvendor
reserve management
risk pooling

http://resolver.tudelft.nl/uuid:3784d02c-1143-4f5c-b2de-7fdcb99c01af

https://doi.org/10.1287/msom.2022.1086

2023-07-01

1523-4614

Manufacturing and Service Operations Management, 24 (4), 2221-2239

Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

Institutional Repository

journal article

© 2022 Xiao Lin Wang, Yuanguang Zhong, L. Li, Wei Xie, Zhi Sheng Ye