Supply Chain Network Optimization & Cold Chain Transportation




The Client

SEUR is a pioneering parcel delivery company founded more than 75 years ago. They are market leaders in Spain with three main lines of business: international, e-commerce, and B2B. SEUR has over 1.2 million customers and delivers more than 300,000 parcels every day with a fleet of 4,700 vehicles.

The Challenge

SEUR were in need of a a solution that would allow them to optimize their cold transportation network by focusing on three main outcomes:

Assess the current state: Identify where there is more demand, the characteristics of high order concentration areas, and whether distribution centers (DCs) are strategically located.

Assess and quantify the impact of changes in their current network. Mainly, the impact of opening/closing DCs, and changes in delivery areas.

Build an optimization model to identify where DCs should be located and design their transportation network (supply chain network design).


By applying different Spatial Data Science techniques in an iterative way and adding complexity over time, we were able to provide meaningful insights and results with every step.

The optimization result gave us an average distance of 18.23 km/order, compared to the original 18.99 km/order. Considering every year SEUR delivers hundreds of thousands of orders only in cold transportation, this could translate into very significant savings. For example, for 500k orders, this improvement would mean 380k fewer kilometers, which would translate into significant savings in terms of fuel and fleet size, and better customer service.


By collaborating with CARTO, SEUR were able to leverage the expertise of our highly skilled Spatial Data Science team. Spatial Data Science provides a competitive advantage as it leverages the spatial components of many of the supply chain processes and provides the tools and techniques to assess and optimize them.

To read more about the techniques used in this analysis read our full in depth blog post.


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