Abstract:
In industrial 4.0, inventory management plays an important role in ensuring the
efficiency of production workflows in industrial environments. However, many
warehouses still rely on manual inventory management processes which are prone to
human error, inefficiencies and less operational productivity. These problems can lead to
delays, stock inaccuracies, and production line interruptions. All these issues can make the
business become problematic and make stakeholders unable to expand more advanced.
To overcome this obstacle, this project introduces “Warehouse Inventory
Management System”, a digitalization system of warehouse management that can help
stakeholders to manage their inventory with integrating semi automation, real time
monitoring, and user-friendly dashboard. This system architecture aims to solve the gap in
conventional methods by introducing a connected smart conveyor system with load cell
sensor, and machine learning object detection with YOLO algorithm approach which has
a good performance to track products in real time.
By providing an advanced approach with implementing trustworthy architecture,
the proposed Warehouse Inventory Management System successfully transforms
conventional warehouse inventory management workflows into modern and efficient
processes. It reduces the risk of human error, enhances operational visibility, and improves
efficiency through intelligent stock tracking and real-time inventory integration.
