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Warehouse Management optimizations with AI algorithms

Reference number
Coordinator Kairos Logic AB
Funding from Vinnova SEK 300 000
Project duration June 2020 - June 2021
Status Completed
Venture Innovative Startups
Call Innovative Startups step 1 spring 2020

Important results from the project

Personnel costs are the single largest cost in most distribution and warehousing operations. With artificial intelligence, it is possible to increase the productivity of picking by optimizing the route and then reducing the picking time. The project´s objective was to create market awareness of the solution and to validate and verify the business model. We will generate a customer story from the first customer and potentially the converted sales that will impact the project and be used for digital marketing.

Expected long term effects

These project-results and effects are validated at Ahsell Norway. - Doubled productivity, reduced energy consumption, including fuel for forklifts and its maintenence. - Reduced tra vel contributes to better working conditions - Reduced staff costs up to an hour on reducing the numb er of pickers - Productivity for operations 30-70% which reduces the total cost of st orage, equipment - Eliminating the need for job training and experienced pickers and thus creating flexible labor - Cost savings from increased utilization of trucks and reduced maintenance

Approach and implementation

The project competed MVP and evaluated the solution in the production environment at Ahsell Norway. The maturity and the value of the solution is proven The project stablished a go to market strategy and a sales and marketing team Acceltech is an investor firm who we entered into contract for generatinf sales and to become an investor . Acceltech has expertize in logistics industry to generate awareness in the industry as well as generate sales. We created customer story from a case customer and is available for digital marketing.

External links

The project description has been provided by the project members themselves and the text has not been looked at by our editors.

Last updated 29 June 2021

Reference number 2020-01393