AWH_PERFORMANCE: Enhancing Auto Warehouse Efficiency through Performance Analytics

AWH_PERFORMANCE: Enhancing Auto Warehouse Efficiency through Performance Analytics

Keval R. Dodia
Parul University, Department of Computer Engineering, Vadodara, India

Himadri Vegad, Assistant Professor & Research Guide,

Department of Computer Science & Engineering, Parul University, Vadodara, India

Abstract—Warehouses play a crucial role in modern supply chain operations, where efficiency directly impacts productivity and cost-effectiveness. One of the primary challenges in warehouse management is downtime, which leads to inefficiencies, production delays, and financial losses. Traditional downtime tracking methods rely on manual record-keeping, which is often inaccurate and lacks real-time insights.

This research presents AWH_PERFORMANCE, an automated warehouse monitoring system that leverages MS SQL Server, .NET API, Angular, and Chart.js to track, analyze, and visualize downtime in real time. The system enhances data accuracy, minimizes human dependency, and provides instant performance metrics to optimize decision-making.

The study compares manual vs. automated downtime tracking, emphasizing improvements in real-time analytics, throughput, turnaround time, and predictive analysis. The findings demonstrate that automation significantly enhances warehouse efficiency. Future advancements include AI-driven predictive maintenance, IoT integration, and machine learning for anomaly detection.

Keywords—Warehouse Downtime, Automation, Performance Analytics, Real-Time Monitoring, AI Predictive Maintenance