AI Case Study

Schneider Electric saves €8 million by optimising its supply chain using machine learning

Schneider Electric uses machine learning to develop predictive supply chain models and discover optimal routes for international transportation from its 240 global manufacturing facilities and 110 distribution centers. They were able to identify the product flow through this exercise with a model that comprised 100,000s of transportation policies and flow constraints.




Project Overview

Schneider Electric needed to develop a model that will help them predict the best way to acquire raw materials and send their products to factories, distribution centres and warehouses around the world. They built a model to analyze all the data points such as existing lanes, rates, transportation policies, flow constraints etc. The model identifies the best path for product flow saving space, cost and time.

Reported Results

The company claims:

* €8 million savings in transportation costs
* Better container utilisation






Schneider Electric’s supply chain is complex. It includes 240 manufacturing facilities around the world and 110 distribution centers.



200,000 transportation policy data points, 130,000 flow and routing constraints, and more than 150 initial scenarios
Reduced 300,000 SKUs into 1,800 product groups based on attributes such as origin, stocking type, and product family to simplify the model.