Abstract
Predictive maintenance has become an increasingly important application of machine learning across many sectors, including supply chain management and information-based service organizations such as academic libraries. Modern academic libraries rely heavily on interconnected digital infrastructures such as Integrated Library Systems (ILS), servers, RFID technologies, self-service circulation machines, and network facilities to support core services like access, circulation, and resource management. Together, these components form a library information service supply chain. When any part of this system fails, it can disrupt services, reduce operational efficiency, and negatively affect user satisfaction. This study examines the application of machine learning–driven predictive maintenance within the context of academic library information service supply chains. In many libraries, maintenance practices remain largely reactive or based on fixed schedules, which often leads to unexpected system breakdowns and unplanned downtime. Such failures not only interrupt daily library operations but can also trigger wider service disruptions across interconnected systems. This study therefore explores how predictive maintenance can help libraries anticipate potential failures and respond proactively. The study reviews various machine learning techniques used in predictive maintenance, including supervised learning, unsupervised learning, and deep learning approaches. It also examines data sources relevant to library environments, such as system usage logs, maintenance records, sensor-generated data, and environmental conditions that influence equipment performance. In addition, the study discusses key challenges associated with implementing predictive maintenance in academic libraries, including data quality and integration issues, cost constraints, real-time decision-making requirements, and limitations in technical expertise. Finally, the study highlights the potential role of emerging technologies such as the Internet of Things (IoT) and edge computing in improving system reliability, operational efficiency, and the overall resilience of academic library information service supply chains.
Keywords: Predictive Maintenance, Machine Learning, Artificial Intelligence, Academic Libraries Supply Chain, Internet of Things (IoT)
DOI: 10.36349/sojolics.2026.v02i01.001
author/Bello, M. I., Adamu, R., & Abubakar, S.
journal/Sokoto JOLICS 2(1) | June 2026 |
