Influence of Machine Learning on Predictive Maintenance in Manufacturing in Kenya

Purpose: The aim of the study was to evaluate the influence of machine learning on predictive maintenance in manufacturing in Kenya. Methodology: This study adopted a desk methodology. A desk study research design is commonly known as secondary data collection. This is basically collecting data from existing resources preferably because of its low cost advantage as compared to a field research. Our current study looked into already published studies and reports as the data was easily accessed through online journals and libraries. Findings:  Machine learning has significantly enhanced predictive maintenance in Kenya's manufacturing sector. By analyzing large datasets from equipment sensors, ML algorithms can predict equipment failures before they occur, minimizing downtime and maintenance costs. This proactive approach improves operational efficiency and extends equipment lifespan, benefiting manufacturers by reducing unplanned disruptions and enhancing overall productivity. Unique Contribution to Theory, Practice and Policy: Diffusion of innovations theory, resource-based view (RBV) & sociotechnical systems theory may be used to anchor future studies on the influence of machine learning on predictive maintenance in manufacturing in Kenya. Manufacturing firms should invest in comprehensive training programs to equip their workforce with the necessary skills to manage and interpret ML-driven maintenance systems. Governments and industry regulators should create incentives for the adoption of ML technologies in manufacturing.