An automotive OEM faced persistent challenges with stock and location accuracy within their warehouse, which adversely affected operational efficiency across critical processes such as receiving, putaway, picking, replenishment, and empty bin return. The client required a solution to enhance these accuracy metrics while minimizing hardware investments.
We developed a Digital Twin of the warehouse, integrating AI models with data platform engineering to achieve precise stock and location accuracy in real time. This engineering aspect processes large volumes of data from diverse sources, ensuring the digital twin is always up to date. The dynamic virtual representation facilitates continuous monitoring and analysis of inventory levels and locations, which enhances decision-making and operational efficiency. By utilizing these technologies, we can proactively address inventory discrepancies and bolster overall stock reliability.
AI models were implemented to optimize key warehouse processes, including:
To minimize hardware investments while maximizing operational effectiveness, we utilized a combination of computer vision and Material Movement Tracking Systems (MMTS). Computer vision technology allows for precise monitoring of stock levels and locations without the need for extensive physical sensors, leading to reduced costs and simplified implementation. Additionally, MMTS tracks the location of items and personnel within the warehouse, further enhancing stock and location accuracy. This approach not only ensures reliable inventory management but also creates a scalable solution that can adapt to future operational needs.