The problem
Dairy cooperative Campina faced one of the largest (digital) projects in recent years: the migration of all its individual ERP systems into one central system. It was a complex process that involved several different departments.
During the preparation process, it soon became apparent that product data from spare parts in different ERP systems didn’t match. Incorrect information—such as incorrect descriptions and abbreviations—were found on a number of products.
This resulted in engineers ordering more inventory when items couldn’t be immediately found in the system, resulting in Campina carrying twice the number needed for a number of products.
The solution
Campina made use of solutions from Mydatafactory to store product data completely, correctly and consistently in their new ERP system.
One of the biggest challenges during the process was ensuring that several hundred thousand product items were transferred properly. The two largest stumbling blocks to doing so included the fact that the data was spread over dozens of systems, and that the data had moderately poor descriptions.
Our Mydatafactory team analyzed, consolidated and improved data in order to properly complete the transfer. Product data was generated and added to hundreds of thousands of product items. Data from multiple sources—all with their own modules and data sets—were combined in a consistent manner, resulting in a clear data set.
By structurally improving data, Campina ultimately succeeded in reducing the number of suppliers it needed as well as increasing the availability of spare parts.
One of the biggest challenges during the process was ensuring that several hundred thousand product items were transferred properly. The two largest stumbling blocks to doing so included the fact that the data was spread over dozens of systems, and that the data had moderately poor descriptions.
Our Mydatafactory team analyzed, consolidated and improved data in order to properly complete the transfer. Product data was generated and added to hundreds of thousands of product items. Data from multiple sources—all with their own modules and data sets—were combined in a consistent manner, resulting in a clear data set.
By structurally improving data, Campina ultimately succeeded in reducing the number of suppliers it needed as well as increasing the availability of spare parts.
The results
- A reduction in the number of suppliers
- Less inventory due to higher availability of spare parts
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