Hours plant downtime saved per day
Model accuracy
Reduction in errors and operational costs
Businesses running huge supply chain operations need to do an accounting of inventory rigorously. Manual counting of inventory is an unreliable and slow process, delaying operations and increasing operational expenses. The movement of SKUs and color-coded palettes, both inbound and outbound through the exit docks, is often prone to errors. These errors arise due to manual dependency on scanning the labels. The client needed an automated inventory accounting process to address these challenges.
Python
TensorFlow
Quantiphi developed an automated solution for inventory counting using custom computer vision and deep neural networks. The automated solution was capable of identifying SKUs in transfer orders and flagging errors in shipment using image recognition AI techniques. The solution helped the client reduce overall operational costs and manual errors.
