IoT and Machine Learning-Driven Predictive Maintenance for Enhanced Supply Chain Performance

Abstract

In today’s competitive and dynamic industrial landscape, minimizing equipment downtime and ensuring uninterrupted supply chain operations are critical for sustaining productivity and profitability. Traditional maintenance strategies—reactive and preventive—are increasingly proving inadequate in the face of complex logistics and high-performance expectations. This research proposes a predictive maintenance framework that leverages the Internet of Things (IoT) and Machine Learning (ML) to anticipate machinery failures and schedule timely interventions in a supply chain context. The study outlines a multi-layered architecture integrating real-time sensor data acquisition, feature engineering, and advanced machine learning algorithms including Random Forest, XGBoost, and Long Short-Term Memory (LSTM) networks. A case study implementation on a conveyor system within a warehouse demonstrates the system’s capability to predict failures with an accuracy of 94.8% using LSTM, leading to a 60% reduction in unplanned downtime and an overall ROI of approximately 230% annually. Furthermore, the integration with ERP and WMS platforms facilitated automated maintenance planning and improved logistics efficiency. The results affirm that predictive maintenance, empowered by IoT and ML, can significantly enhance asset reliability, reduce operational costs, and increase supply chain resilience. This research also highlights practical implications, limitations, and directions for future work including digital twin integration and AI-driven automation