Case Study: HOERBIGER achieves automated, accurate material classification with A1 Digital machine learning

Hoerbiger assigns materials automatically and correctly with the help of machine learning

HOERBIGER, a global group specializing in compression technology, drive technology, and hydraulics, faced a challenge with its manual product categorization process in its ERP system. The hit rate for correctly assigning materials to product groups was unsatisfactory, and a manual review of all products was not feasible due to the vast number of items. They commissioned vendor A1 Digital to develop a machine learning solution to address this.

A1 Digital created a machine learning model based on a sample of correctly assigned materials. The solution involved a learning algorithm that calculated structures from known data, which then enabled an evaluating algorithm to automatically and correctly categorize new, unknown materials. This implementation by A1 Digital automated the previously manual and error-prone task, ensuring accurate product group assignments.

HOERBIGER

Robert Fruhwirth

Head of Purchasing Process and Spend Management