A Bayesian inventory model using real-time condition monitoring information

Zhi Zeng, Jennifer K. Ryan, Rong Li

Research output: Contribution to conferencePaperpeer-review

Abstract

Lack of coordination between machinery fault diagnosis and the inventory management of spare parts can lead to increased inventory costs and disruptions in production activity. To address this gap, we develop a framework for incorporating real-time condition monitoring information into inventory decisions for spare parts. We demonstrate that a dynamic base-stock policy, in which the optimal base-stock level is a function of some subset of the observed sensor information, is optimal. We then propose a myopic critical fractile policy, which captures the essence of the optimal policy, but is much easier to compute. Adaptive inventory policies such as those developed in this research can help manufacturers to increase machine availability and reduce inventory costs.

Original languageEnglish (US)
StatePublished - Jan 1 2011
Externally publishedYes
Event61st Annual Conference and Expo of the Institute of Industrial Engineers - Reno, NV, United States
Duration: May 21 2011May 25 2011

Other

Other61st Annual Conference and Expo of the Institute of Industrial Engineers
CountryUnited States
CityReno, NV
Period5/21/115/25/11

Keywords

  • Bayesian inventory model
  • Condition monitoring
  • Real-time information
  • Spare parts

ASJC Scopus subject areas

  • Industrial and Manufacturing Engineering

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