Repairing smarter: Opportunistic maintenance for a closed-loop supply chain with spare parts dependency

Abstract

Adopting a closed-loop supply chain enhances spare part provisioning through repair, remanufacturing, and recycling. However, poor maintenance of components can have severe consequences. Unlike traditional opportunistic maintenance methods that assume regular inspections or precise degradation monitoring, we propose a model that leverages historical repair data to replace worn components preventively. It considers the real-world workflow where parts are often restored only to a functional level. We study maintenance strategies for repeatedly repaired multi-component systems by applying preventive operations only during corrective repairs. Our model considers component ages, failure time distributions, and structural and economic dependencies, favoring collective over individual replacements for cost efficiency. Stochastic dependencies are mapped using Nataf transformation for component subsets, and a genetic algorithm identifies optimal maintenance strategies to reduce long-term operational costs by balancing maintenance against potential failure penalties. We demonstrate the effectiveness of our approach with a case study on MRI power supply machines, showing that preventive actions can cut early life failures by up to 50% and extend useful life by over a year. Sensitivity analysis reveals that logistic costs, interest rates, and planning horizons influence decisions. Opportunistic maintenance can reduce logistic costs and increase the lifetime of spare parts after repair. Integrating stochastic dependency is computationally efficient for industrial systems and can help predict failures more accurately.