For food industry players, precisely managing a product reference system can be a sizeable challenge. Today, thanks to machine learning, it is possible to optimise the quality of product information.
That was precisely the aim of our customer, who entrusted our teams of data scientists with the task of setting up an automated system to detect and analyse errors in its product reference system.
When a company has a product catalogue comprising more than 6,000 products with over 250 variables, regularly updated and constantly changing, the quantity of product-related data just keeps increasing too…
And so does the number of errors! This is a critical situation that can throw the entire production line and distribution network supply chain into chaos.
There were too many errors in the reference system, and business function validation rule systems were not comprehensive or responsive enough…
Our customer wanted to make its catalogue more reliable and thus reduce costs stemming from errors. Our aim was therefore to offer a solution that would detect PIM (Product Information Management) errors and suggest relevant corrections without any explicit written rules.
Support with technical choices (databases, analysis tools, etc.)
Establishment of an efficient, responsive architecture
