Quantifying the Operational and Economic Impact of AI-Driven IoT Monitoring Systems in Brewery Logistics Networks

The proliferation of Artificial Intelligence (AI) and Internet of Things (IoT) technologies has transformed supply- chain monitoring, yet the tangible economic and operational impact of such systems in developing contexts remains under- documented. This study quantifies the financial returns and performance outcomes of an AI-IoT monitoring framework deployed in the logistics operations of Nigerian Breweries Plc. The framework integrates GPS, load, and temperature sensors with a neural network that predicts in-transit anomalies. Empirical evaluation across 10 000 trip records shows an 8.2:1 return-on-investment (ROI) ratio, 20 % reduction in transit losses, and 15 % improvement in fleet utilization. Operational efficiency metrics—including truck turnaround time and driver compliance—improved significantly. The findings demonstrate the dual value of AI-IoT adoption: immediate cost savings and sustained digital-transformation momentum within the brewery sector. The study provides a replicable quantitative model for evaluating AI logistics systems in emerging markets.