An analysis of predictive maintenance strategies in supply chain management

The ability to proactively detect equipment problems before they become significant has led many manufacturers to choose predictive maintenance as an area of emphasis within their supply chain management strategies in recent years. An employ of ML methods in predictive maintenance is the focus of this paper's extensive analysis of its application to supply chain management. It defines reverse supply chain processes and their critical importance in maintaining industrial equipment by leveraging timely and systematic reverse logistics operations. The study highlights the economic impact of equipment maintenance, particularly for high-value and complex assets, and underscores the logistical challenges posed by remote and dispersed equipment locations. By advocating for predictive maintenance, the paper discusses how data-driven insights can enhance maintenance schedules, reduce unexpected failures, and extend equipment lifespan. Various ML methods—supervised, unsupervised, and reinforcement learning—are examined for their effectiveness in predicting equipment failures and optimising maintenance processes. The review also provides sector-specific examples, illustrating significant cost savings, improved reliability, and enhanced operational efficiency through predictive maintenance applications across industries such as automotive, aerospace, utilities, logistics, and healthcare.