2 Scopus citations


The upcoming industrial revolution 4.0 built on the internet of things, and prescriptive analytics paves the way for the spread of continuously monitored condition-based maintenance (CBM) in the industry. In the CBM implementations, it is essential to consider the impact of spare parts quality, lead-time, and inspection errors on maintenance cost and system availability. We propose a new maintenance model that incorporates the effects of these features in addition to the different cost factors, e.g., replacement cost, holding cost, and shortage cost. In a case study, we optimize our model by deciding on the degradation level at which a spare part is ordered. We show that a proper inspection of spare parts pays back up to 22% in maintenance cost savings as the spare parts' quality deteriorates. The vendor mean lead-time, the offered spare part price, and the mean degradation per time unit significantly impact the optimal maintenance cost. Finally, the costly detection processes of defective items installed in the system due to inspection errors have a limited cost reduction.

Original languageEnglish
Article number108534
JournalComputers and Industrial Engineering
StatePublished - Oct 2022

Bibliographical note

Funding Information:
This work has been supported by the Research Center on Smart Mobility and Logistics, King Fahd University of Petroleum & Minerals under grant INML2103.

Publisher Copyright:
© 2022 Elsevier Ltd


  • Condition-based maintenance
  • Continuous monitoring
  • Gamma process
  • Incoming quality control
  • Spare part quality
  • Type I and II inspection errors

ASJC Scopus subject areas

  • Computer Science (all)
  • Engineering (all)


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