Maintenance Service Supplier Selection for Complex Equipment in Military–Civilian Collaborative Background Considering Two-Stage Matching Perspective

The maintenance of sophisticated equipment is of paramount importance, particularly in the military–civilian integration context. Traditional maintenance practices often rely on historical data and accumulated experience. In this study, we focus on the selection of suppliers for complex equipment within a military–civilian collaborative framework. The paper primarily addresses two key aspects: maintenance customer classification and supplier selection. We propose a two-stage matching approach to integrate these two components effectively. The initial stage involves classifying maintenance customers using an adaptive backpropagation (BP) neural network. This classification is crucial for tailoring maintenance services to specific customer needs. The subsequent stage encompasses the matching process between maintenance suppliers and customers. This stage is further divided into two parts: attribute matching and satisfaction assessment. Attribute matching is conducted through an entropy-based Group Decision-making Trial and Evaluation Laboratory (DEMATEL) analysis, which helps in identifying the critical factors that influence the supplier selection process. Satisfaction computation is then performed to evaluate the alignment of supplier capabilities with customer requirements. Finally, by employing a constructed matching model, we are able to identify suitable maintenance suppliers for complex equipment. This model not only streamlines the selection process but also enhances the efficiency and effectiveness of maintenance operations. The methodology and findings of this research are comprehensively detailed in the paper, providing a valuable contribution to the field of military–civilian integrated maintenance management.