AI-DRIVEN SUPPLY CHAIN OPTIMIZATION FOR SUSTAINABILITY: EVIDENCE FROM NIGERIA MANUFACTURING INDUSTRY

Abstract

The growing demand for sustainability in manufacturing and the inefficiencies of traditional methods in the supply chain highlight the importance of finding smarter solutions. Even with the growing availability of digital tools and AI technologies, organizations are yet to fully utilize them to aid sustainability efforts, causing inefficient use of resources, waste, and environmental damage. The chapter examined AI supply chains optimizing for sustainability: evidence from Nigeria manufacturing industry.  The chapter utilized a survey research design with a population of 950 supply chain employees in Nigerian Breweries, Lagos State, with an estimated sample size of 281 using the Yamane (1967) formula. Data was collected using a structured questionnaire with a five-point Likert scale. Descriptive statistics and multiple regression analysis were used to analyze the data in SPSS version 27. The findings showed that AI-driven demand forecasting (coefficient = 4.287) and AI-based inventory optimization (coefficient = 3.549) have a positive significant on sustainability, with 56.7% of the variance in sustainability outcomes. The chapter concluded that AI-driven supply chain optimization plays a significant role in minimizing waste, optimizing resources, and meeting market demand.  The study recommended that Nigerian Breweries should utilize AI-powered demand forecasting tools for production planning and should implement AI-based inventory optimization systems to ensure that inventory levels are optimized and reducing excess stock.