Recent Papers

The worldwide retail sector loses $161 billion yearly due to perishable food waste, a situation in which inadequate inventory management, notably manual expiration tracking, is directly responsible for 42% of retail losses. This paper addresses the "rotting profit problem" front on, linking Nahmiasbasic decay dynamics theory to the revolutionary potential of Internet of Things (IoT) operational frameworks. While previous research gave theoretical promise and isolated case studies, this study provides the first large-scale, rigorous calculation of the return on investment possible with IoT-enabled real-time monitoring for perishables. Using a quasi-experimental methodology, the investigation analyzes 50 perfectly matched supermarket chains over 12 months25 using IoT sensor networks and predictive analytics, and 25 using traditional FIFO or LIFO systems. The granular data streams included spoiling rates, IoT system alert reaction times, dynamic price modifications, and operational expenses. Advanced difference-in-differences analysis shows that IoT-driven dynamic allocation, informed by continuous shelf-life monitoring and predictive decay modeling, outperforms traditional techniques. Most importantly, the approach reduced spoilage costs by 38% when compared to FIFO alone, while also improving service levels and markdown efficiency. For example, chains that use real-time data might dynamically route perishables approaching expiration to high-demand areas or trigger opportune promotions, increasing value recovery. This study provides irrefutable empirical evidence that IoT integration is a fundamental step forward in optimizing perishable inventory, resulting in significant financial savings, improved operational resilience, and significant contributions to sustainability goals. The findings provide a convincing framework for modernizing retail operations in the digital age.

Copyright © 2025 Simon Suwanzy Dzreke. This is an open access article distributed under the Creative Commons Attribution License.