Machine tools with hidden defects: Optimal usage for maximum lifetime value

Alp Akcay*, Engin Topan, Geert-Jan van Houtum

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

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We consider randomly failing high-precision machine tools in a discrete manufacturing setting. Before a tool fails, it goes through a defective phase where it can continue processing new products. However, the products processed by a defective tool do not necessarily generate the same reward obtained from the ones processed by a normal tool. The defective phase of the tool is not visible and can only be detected by a costly inspection. The tool can be retired from production to avoid a tool failure and save its salvage value; however, doing so too early causes not fully using the production potential of the tool. We build a Markov decision model and study when it is the right moment to inspect or retire a tool with the objective of maximizing the total expected reward obtained from an individual tool. The structure of the optimal policy is characterized. The implementation of our model by using the real-world maintenance logs at the Philips shaver factory shows that the value of the optimal policy can be substantial compared to the policy currently used in practice.
Original languageEnglish
Pages (from-to)74-87
Number of pages14
JournalIISE Transactions
Issue number1
Early online date22 Apr 2020
Publication statusPublished - 2 Jan 2021


  • UT-Hybrid-D
  • manufacturing operations
  • inspection planning
  • tool management
  • Markov decision processes

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