Abstract
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.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
