Reactive to Predictive: A Hybrid Artificial Intelligence (AI) Framework for Fault Detection and Productivity Optimisation in Nigerian Biomanufacturing Industries

Abstract

The inclination toward AI-driven predictive maintenance by biomanufacturing industries universally is profound sequel to multiple benefits availability. These include 80% reduction in false alarms, 80% improvement in failure prediction, and 48 times increment in lead time for preventative actions. However, within Nigerian biomanufacturing industries, reactive maintenance practices are endemic; resulting in unplanned downtime, product quality deviations, and significant economic losses. This study conducts a qualitative analysis of data from relevant sources to develop a hybrid AI framework for transitioning from reactive to predictive maintenance. The adoption of a thematic analytical approach alongside schemas of Technology-Organisation-Environment (TOE) framework, Diffusion of Innovation (DOI) theory, and Technology Acceptance Model (TAM), offered a robust foregrounding. Findings reveal that of the over 150 registered pharmaceutical manufacturers in Nigeria, with key facilities concentrated in major Nigerian cities only. AI adoption is stalled by limited digital infrastructure, inadequate data collection systems, skills shortages, organisational, environmental and financial. This accentuates the timeliness of the introduction of a hybrid AI framework that integrates machine learning techniques for real-time fault detection and predictive modelling. A practical roadmap for practitioners and policymakers, infrastructure development, skills enhancement, and regulatory reform are proffered.