A Bayesian Inventory Model Using Real-Time Condition Monitoring Information
Version of Record online: 7 DEC 2010
© 2010 Production and Operations Management Society
Production and Operations Management
Volume 20, Issue 5, pages 754–771, September/October 2011
How to Cite
Li, R. and Ryan, J. K. (2011), A Bayesian Inventory Model Using Real-Time Condition Monitoring Information. Production and Operations Management, 20: 754–771. doi: 10.1111/j.1937-5956.2010.01200.x
- Issue online: 13 SEP 2011
- Version of Record online: 7 DEC 2010
- History: Received: January 2009; Accepted: June 2010 by Panos Kouvelis, after 2 revisions.
- spare parts;
- inventory management;
- real-time information;
- stochastic models;
- Wiener process
Lack of coordination between machinery fault diagnosis and inventory management for spare parts can lead to increased inventory costs and disruptions in production activity. We develop a framework for incorporating real-time condition monitoring information into inventory decisions for spare parts. We consider a manufacturer who periodically replenishes inventory for a machine part that is subject to deterioration. The deterioration is captured via condition monitoring and modeled using a Wiener process. The resulting degradation model is used to derive the life distribution of a functioning part and to estimate the demand distribution for spare parts. This estimation is periodically updated, in a Bayesian manner, as additional information on part deterioration is obtained. We develop an inventory model that incorporates this updated demand distribution and demonstrate that a dynamic base-stock policy, in which the optimal base-stock level is a function of some subset of the observed condition monitoring information, is optimal. We propose a myopic critical fractile policy that captures the essence of the optimal policy, but is easier to compute. Computational experiments indicate that this heuristic performs quite well relative to the optimal policy. Adaptive inventory policies such as these can help manufacturers to increase machine availability and reduce inventory costs.