Smart Warehouse Operations for Enhancing Storage Accuracy Through Intelligent Real-Time Inventory Monitoring Technologies Continuously - Afferdhy Ariffien (Malikussaleh University, Aceh, Indonesia, 2023)
Warehouse operations require accurate inventory visibility, reliable location tracking, efficient order picking, and continuous monitoring to ensure operational efficiency and uninterrupted customer service. The growing adoption of Internet of Things (IoT) technologies provides opportunities to enhance warehouse performance through real-time monitoring and data-driven decision support. This study presents a descriptive evaluation of smart warehouse operations using two complementary academic datasets representing a food-industry distribution center in Lhokseumawe, Indonesia. The first dataset compares warehouse performance under conventional manual monitoring and IoT-enabled monitoring across six storage zones. The second dataset summarizes 30 days of warehouse operations, including inbound and outbound transactions, tracked stock-keeping units (SKUs), environmental conditions, sensor alerts, and system downtime. Comparative analysis of the storage-zone dataset indicates that mean location accuracy increased from 82.43% under manual monitoring to 97.51% with IoT-based monitoring, representing an improvement of 15.09 percentage points. Mean order-picking time decreased from 14.52 minutes to 6.88 minutes, while the reported picking error rate declined from 6.30% to 0.96%. The daily operational dataset records 2,397 inbound and 2,069 outbound transactions, an average of 2,006.77 tracked SKUs, 64 sensor alerts, and 98 minutes of reported system downtime during the observation period. Because the storage-zone comparison and the daily operational records do not share a common implementation protocol, deployment timeline, or control warehouse, they are analyzed independently to avoid unsupported causal conclusions. The findings identify consistent operational patterns that integrate location accuracy, picking efficiency, transaction flow, environmental monitoring, sensor alerts, and downtime into a unified performance review framework. This study contributes a transparent evaluation model for assessing smart warehouse monitoring systems, providing a practical decision-support approach for warehouse performance assessment while avoiding causal claims beyond the available evidence