ASSESSMENT OF ARTIFICIAL INTELLIGENCE-BASED PREDICTIVE MAINTENANCE FOR IMPROVING EQUIPMENT RELIABILITY IN MANUFACTURING INDUSTRIES IN NIGERIA

The manufacturing sector plays a critical role in Nigeria's industrial development and economic growth, yet frequent equipment failures and unplanned machine breakdowns continue to undermine productivity, increase maintenance costs, and reduce operational efficiency. Traditional maintenance approaches, such as corrective and preventive maintenance, are often inadequate because they either respond to failures after they occur or rely on fixed maintenance schedules that may result in unnecessary interventions. Consequently, the emergence of Artificial Intelligence (AI)-based Predictive Maintenance (PdM) has provided a more intelligent and proactive approach to equipment maintenance. This study examined the role of AI-based predictive maintenance in improving equipment reliability in manufacturing industries in Nigeria. Specifically, the study reviewed the concepts of Artificial Intelligence, Predictive Maintenance, and Equipment Reliability, and assessed the benefits and challenges associated with the adoption of AI-driven predictive maintenance technologies in the Nigerian manufacturing sector. The study adopted a descriptive review methodology based on the analysis of relevant scholarly literature, industry reports, and empirical studies on predictive maintenance and Industry 4.0 technologies. Findings from the review revealed that AI-based predictive maintenance significantly improves equipment reliability through continuous condition monitoring, early fault detection, and accurate prediction of equipment failures. The study further found that predictive maintenance can reduce maintenance costs by approximately 15–30%, decrease unplanned equipment downtime by 30–50%, improve equipment availability, enhance workplace safety, and increase manufacturing productivity. However, the study identified several barriers to the adoption of AI-based predictive maintenance in Nigeria, including high implementation costs, inadequate digital infrastructure, poor data availability, shortage of skilled personnel, and resistance to technological change. The study concluded that Artificial Intelligence-based Predictive Maintenance has enormous potential to transform maintenance management practices and improve the competitiveness and sustainability of Nigerian manufacturing industries. Therefore, the study recommends increased investment in digital infrastructure, capacity building, research and development, and supportive government policies to facilitate the effective implementation of AI-driven predictive maintenance systems in Nigeria.