Spare parts supply with incoming quality control and inspection errors in condition based maintenance

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.

Automated summary

The upcoming industrial revolution 4.0, based 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 CBM implementations, the impact of spare parts quality, lead-time, and inspection errors on maintenance cost and system availability must be considered. A new maintenance model is proposed to incorporate these factors along with different cost factors and optimize the degradation level at which spare parts are ordered.