CONSAFE HELP LEADING LOGISTICS OPERATORS TO REDUCE TRAVEL TIME TO PICK UP BY 20% PER MONTH

PATRIK OLSSON | CHIEF PRODUCT OFFICER, CONSAFE | 2018

THE CHALLENGE

Consafe Logistics is a large player in warehouse automation software and has big companies among its clients. The company provides its clients with software that helps plan and handle processes in their warehouses. One of the main challenges in this industry is the speed of picking up goods. Traditionally, many warehouses locate goods based on type, size or supplier. However, this grouping often makes less sense when it comes to pickups. It leads to excessively long travel paths during the picking process and thus longer delivery lead times.

“The logistics sector is in constant need of innovation to make warehouse operations and shipping more efficient. The merge of our existing software and the 2021.AI platform gives our customers a tailored solution that makes the slotting and picking process much smarter and saves them lots of valuable time and resources.”

OUR SOLUTION

Consafe Logistics asked 2021.AI for an algorithm that could help their customers achieve a more efficient placement of products in the warehouse as well as reduce the time spent on pickups. The algorithm, which utilizes a type of association rule approach, analyzes historic order lists and associates between products. Based on the warehouse data, the algorithm suggests to the warehouse management which products need to be placed closer to each other to reduce picking time. The machine learning powered algorithm is now an add-on (called slotting module) to the existing Consafe Logistics’ software package. By using this add-on, the warehouses have reduced travel time by up to 300 hours per year, which corresponds to a 10 – 20% reduction and a saving of approximately two monthly salaries for each end-client.

WHAT WE DID

At the initial stage of the project, 2021.AI conducted a workshop to demystify AI followed by a deep-dive discussion of the use cases. The workshop covered a broad set of the use case scenarios for the algorithm implementation and acquisition of a pilot data set. With a clear understanding of the variables available and their inter-dependencies, 2021.AI’s data scientists developed a tailored pilot model for the Consafe Logistics use case. An important insight from the use case scenarios was that the warehouses would need a flexible algorithm, depending on seasonal changes in the stock. Therefore, 2021.AI created a flexible algorithm that allows Consafe Logistics’ customers to use different data from season to season.

After the initial Proof of Concept (PoC) was developed, Consafe Logistics experts were provided with additional personal advisory services, which enabled them to smoothly deploy the Grace AI Platform. Besides the algorithm and advisory, they got access to a version of the Grace platform to develop additional models for in-house use, as well as for educating their data scientists.

3 FACTS ABOUT THE MINIFINANS PROJECT

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2021.AI applied the Apriori Algorithm for computing purchased goods baskets, which allowed to establish association rules between the goods.

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The algorithm uses log files stored in Astro WMS, which describe orders of goods, their frequency, and other descriptive variables pertinent to the model development.

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The results of the PoC are being tested by one of the existing customers of Consafe Logistics.

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