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In all warehouses, even with a Warehouse Management System (WMS) in place, the only way to confirm the inventory is to do a physical inventory count, by scanning or checking the digits with human operators during the cycle counting (such as with Radio Frequency (RF), voice and vision picking). Given the size of most warehouses, doing a physical inventory comes with many challenges. Manual inventories are extremely labour and time intensive, and as such are very expensive processes. Therefore, full wall to wall stock takes are only done periodically, whilst reliance on cycle counting for high value or fast moving items helps with inventory management on an ongoing basis.
The various processes of inventory management including stock takes and cycle counting, take considerable labour effort whilst also being subject to shortcomings in terms of accuracy or completeness.
Breakthroughs in computer vision AI in recent years have resulted in higher accuracy in object counting as a use case. However, when applied to visual inspection/counting of inventory goods in the warehouse, the use case is limited to pallet based counting and single deep racking type. In many warehouse operations within Singapore due to the distribution profile, handling and inventory are represented more by carton/case and split case consumption.
Therefore, we would like to extend the use case to provide a more accurate visual identification of inventory at the loose case/carton level, taking into consideration goods storage in double deep racking type as well as low lighting conditions within a warehouse.
Timeframe: 6-8 months
Potential market / business opportunity: All of Toll warehousing facilities could potentially apply this.
A working prototype product that is able to operate within one of our warehouse site fulfilling above requirements.
Toll can provide:
Contact / source: EnterpriseSG Trade and Connectivity - Trade and Connectivity Challenge – 4th Edition (innovation-challenge.sg)
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