A Bayesian inventory model using real-time condition monitoring information

Rong Li, Jennifer K. Ryan

Research output: Contribution to journalArticlepeer-review

64 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)754-771
Number of pages18
JournalProduction and Operations Management
Issue number5
StatePublished - Sep 2011
Externally publishedYes


  • Wiener process
  • inventory management
  • real-time information
  • spare parts
  • stochastic models

ASJC Scopus subject areas

  • Management Science and Operations Research
  • Industrial and Manufacturing Engineering
  • Management of Technology and Innovation


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