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Project name:

How can we improve visual identification of Case/Carton-based inventory within warehouses for autonomous cycle counting systems?

Status: Idea
Creation date: 23-11-2022

Project objectives:

BACKGROUND

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.

REQUIREMENTS

  • A computer vision AI detection model/solution to visually identify and count each case/carton object on every rack. However, additional sensors could also be considered as part of the solution to improve detection in a challenging environment.
  • Counting accuracy should be of 95% or above
  • The count can be recorded but is required to be automatically matched against WMS data at this stage
  • Shelving type should include both single dip and double dip, also taking into consideration dark corners in the shelving unit under low lighting condition
  • Hardware solution, integrated with required camera and sensor recording unit that can move along the aisle between rackings, as well as traverse the racks from bottom to top. Drone is an option but not limited to it.

Cost: TBD

Timeframe: 6-8 months

Potential market / business opportunity: All of Toll warehousing facilities could potentially apply this.

DESIRED OUTCOME

A working prototype product that is able to operate within one of our warehouse site fulfilling above requirements.

ADDITIONAL INFORMATION

Toll can provide:

  • A test environment in our warehouse and network set up
  • A working office in our innovation centre
  • Developers experienced in mechanical and software design from the startup

 

Contact / source: EnterpriseSG Trade and Connectivity - Trade and Connectivity Challenge – 4th Edition (innovation-challenge.sg)

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