Authors

Engr. Joe Frank

Department of Electrical and Electronics Engineering, University of Port Harcourt, Nigeria

Dr. Ehikhamenle Matthew

Centre for Information and Telecommunication Engineering, University of Port Harcourt, Port Harcourt, Nigeria

Abstract

In emerging economies, losing products during transportation is a big financial and operational risk for brewery logistics. This paper introduces an Artificial-Intelligence-Enabled Internet of Things (AI-IoT) framework that amalgamates real-time sensing, predictive analytics, and cloud dashboards to avert product loss during transit within Nigerian Breweries Plc’s distribution network. The framework combines GPS, load-cell, and temperature sensors with an Artificial Neural Network (ANN) trained on 10 000 trip records (real and synthetic) to detect anomalies linked to pilferage, route deviation, or environmental abuse. Experimental evaluation on a hybrid dataset shows an overall accuracy of 96.7%, precision 95.4%, and recall 97.1%, outperforming logistic regression and decision-tree baselines by 8.3%.  Real-time monitoring reduced loss incidents by 21.4% and improved truck turnaround time by 17%.  The framework demonstrates how AI-IoT synergy enhances visibility, traceability, and economic sustainability across brewery supply chains.

Keywords

Internet of Things Artificial Intelligence Neural Network Brewery Logistics Supply-Chain Visibility Predictive Analytics

Citation of this Article

Engr. Joe Frank, & Dr. Ehikhamenle Matthew. (2025). An AI-Enabled Internet of Things Framework for Preventing Product Loss in Brewery Supply Chains: A Case Study of Nigerian Breweries Plc. International Current Journal of Engineering and Science (ICJES), 4(12), 7-12. Article DOI: https://doi.org/10.47001/ICJES/2025.412002

Licence Copyright (c) 2026 International Current Journal of Engineering and Science. This work is licensed under a Creative Commons Attribution Non Commercial 4.0 International Licence.

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