OPTIMIZING WAREHOUSE MONITORING WITH IOT SENSORS AND MACHINE LEARNING: AN EMPIRICAL STUDY

Abstract

Beyond technical performance, the paper examines the broader implications
of implementing such a system. It details the technical advantages, including enhanced
process transparency and improved real-time decision-making capabilities;
economic benefits, exemplified by a cost–benefit analysis that shows a substantial
return on investment (ROI) of approximately 108%; and social benefits, such as reduced
labor costs and improved workplace safety. A detailed process flow for alert
generation is presented, illustrating the end-to-end integration of sensor data processing
and automated response mechanisms. The findings underscore the potential for
IoT and ML integration to revolutionize warehouse management by reducing operational
inefficiencies, minimizing product losses, and contributing to a sustainable
supply chain in industrial environments.