Background: Distribution systems often face the dual challenge of delivering products to customers and retrieving damaged items for repair, especially when the service center is separate from the depot. An optimized solution to this logistics problem produces benefits in terms of costs,...

Predictive maintenance (PdM) is a data-driven maintenance strategy that aims to avoid unplanned downtimes by predicting the remaining lifetime of maintenance objects. Thus, unnecessary replacements of spare parts and critical process disturbances due to breakdowns can be avoided. Despite the...

Maintenance is pivotal in the industrial sector, influencing efficiency, reliability, safety, and profitability. An organized spare parts inventory supports maintenance efforts by minimizing downtime, ensuring safety, and optimizing maintenance budgets. Effective spare parts management enhances...

This study presents a prescriptive analytics framework for optimal intelligent predictive maintenance of railway tracks. We use machine learning and Graph Convolutional Networks (GCNs) to optimize the maintenance schedules for railway infrastructure and enhance operational efficiency and safety....

The evolution of Industry 5.0, along with its predecessor Industry 4.0, has significantly boosted the adoption of predictive maintenance through integrating Internet of Things (IoT) sensors and real-time big data analysis, enabling the identification and prevention of equipment failures. This...