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AI Case Study

Chronopost International ensures on-time deliveries during peak activity with machine learning predictive analytics

Chronopost can ensure on-time deliveries during peak activity with ease of delivery rating for all addresses based on parcel tracking and geographical data provided by Dataiku DSS. The custom solution implemented analyses historical internal delivery and retrieval data using a machine learning interface. The company has increased business intelligence productivity and optimised package delivery operational costs .



Freight And Logistics

Project Overview

Dataiku's solution involved ease of delivery rating for all addresses based on parcel tracking and geographical data. "Chronopost uses DSS to create a custom application that automatically generates an ease-of-delivery rating for each address.

The designed application:

* Takes into account historical internal delivery and retrieval data.
* Analyzes and enriches shipping and delivery data via data aggregation by geographic location.
* Enables easy modeling of a rating for each delivery.
* Incorporation of new deliveries to the existing model allows iteration and continuous production costs optimization."

Reported Results

The implementation resulted in optimised production costs and new commercial offers. "Since they developed the application with Data Science Studio:

* Increased BI productivity: BI teams around the world easily collect and use performance indicators on demand
* Optimize operational means and costs involved in package delivery
* Create new commercial offers at optimized production costs

Thanks to this in depth analysis of their data, Chronopost can ensure constant quality of their different offers (delivery before 1pm, before 8am...) at optimized production costs."


Dataiku Data Science Studio (DSS) is a powerful predictive analytics solution that allows users to connect to a wide variety of data, quickly clean that data (in the GUI or with code), and enables them to creatively factor-in datasets to create predictive models. These models can be configured and tweaked, as needed, in order to visualize your own unique business scenarios. The analytics process uses a rich machine learning interface to empower your company to build predictive services based on past and incoming data.


Information Technology

Business Intelligence


According to Dataiku's case study, Chronopost's challenge was to ensure on-time deliveries during peak activity. "Chronopost promises that all parcel deliveries in France will arrive by 1pm the day following an order. But as demand continues to grow and especially during critical periods such as Christmas or Mother’s Day, Chronopost wants to make sure they can always keep their promise and deliver parcels on time. With this in mind, Chronopost decided to look for a solution that would help them use and analyze historical data to optimize delivery operations and ensure delivery deadlines."



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