http://orcid.org/0000-0001-7845-9936
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ABSTRACT
Warranty policy, as a marketing strategy, has been widely studied for several decades, but warranty models incorporating condition-based maintenance are still rare. In condition monitoring, product reliability in the warranty period can be tracked and predicted based on its degradation path. In this article, we first propose a condition-based renewable replacement warranty policy through the integration of Inverse Gaussian degradation model. The goal is to maximize the manufacturer's profit by optimizing the warranty period, sale price, and replacement threshold. In a monopoly market, we show that it is more profitable to let the replacement threshold equal the failure threshold. However, in the competitive market the optimal replacement threshold should be below and no more than the failure threshold. Second, depending on whether the historical degradation level is observable or not to the customer, optimal post-warranty maintenance policy considering hybrid preventative maintenance effect (i.e., both age and degradation level reduction) is derived. Numerical experiments show that a larger replacement threshold can increase the manufacturer's profit, reduce sale price and prolong warranty period, but it has less effect on saving the consumer's cost or extending the replacement age.
Acknowledgements
The authors sincerely thank the anonymous reviewers for constructive comments on this paper.
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Notes on contributors
Lijun Shang
Lijun Shang is a Ph.D. candidate in the Department of Industrial Engineering at Northwestern Polytechnical University. Currently, he is a visiting Ph.D. student at Western University in Canada. His research interests include warranty cost analysis, maintenance decisions, and component importance analysis.
Shubin Si
Shubin Si is a professor in the School of Mechanical Engineering, Northwestern Polytechnical University, China (NPU). He is also a senior member of IEEE and a vice chairman of the Reliability Committee of the Operation Research Society of China. He received his B.S. degree in 1997 and M.S. degree in 2002 at NPU; he majored in mechanical engineering. He also received his Ph.D. degree in 2006 at NPU, he majored in management science and engineering. He has published over 60 academic papers in journals and conferences in the past 5 years. He also headed and participated in six government-supported studies and more than 10 enterprise-supported projects. His research topics include system reliability theory, importance measures, and maintenance management.
Shudong Sun
Shudong Sun is a professor of the School of Mechanical Engineering, Northwestern Polytechnical University, China. He received his Ph.D. (1989) from the School of Mechanical Engineering, Nanjing University of Aeronautics and Astronautics, China. His research interests include production planning, maintenance management, and robotics.
Tongdan Jin
Tongdan Jin is an associate professor in the Ingram School of Engineering at Texas State University. He graduated with a Ph.D. in industrial and systems engineering from Rutgers University. His MS and BSEE are, respectively, from Beijing Institute of Technology and Shaanxi University of Science and Technology. Prior to joining academia, he spent 5-years working on the spare parts supply chain at Teradyne Inc., Boston. He is a recipient of best paper awards in several international conferences, including the Evans-McElroy best paper in the 2014 Reliability and Maintainability Conference. His research interests focus on reliability modeling, spare parts logistics, and distributed energy integration. His research projects are sponsored by NSF, the USDA, and the Department of Education. He is a member of IEEE, INFORMS, and IISE.