Assessing Impact of AI on Resource Efficiency in Manufacturing in West Africa

This study investigates the impact of Artificial Intelligence (AI) on resource efficiency in the manufacturing sector within West Africa, a region characterized by infrastructural challenges and resource constraints. Through integrating Transaction Cost Economics (TCE) and the Resource-Based View (RBV) theories, the research explores how AI-driven technologies, including predictive maintenance, production optimization, real-time data analytics, and smart manufacturing systems, enhance resource efficiency. A quantitative research design with a correlational approach was employed, surveying 381 professionals across various manufacturing sectors. The study also examined the moderating role of the regulatory environment and the mediating effect of employee training and adaptation on these relationships. Findings indicate that AI-driven technologies significantly improve resource efficiency by reducing downtime, optimizing production processes, and enhancing decision-making. The regulatory environment positively moderates the relationship between AI technologies and resource efficiency, particularly when coupled with employee training. This study contributes to the existing body of knowledge by providing empirical evidence on the strategic role of AI in resource-constrained environments, emphasizing the importance of supportive regulations and workforce adaptation. The results offer practical implications for policymakers and industry leaders in West Africa, highlighting the need for targeted investments in AI infrastructure, regulatory development, and capacity building to fully leverage AI's potential in manufacturing.