Precise planning data for mechanical engineering thanks to machine learning

Challenge

A German mid-volume manufacturer of tooling systems with around 400 employees faced the challenge that planning data for production as well as upstream and downstream areas were inaccurate and core data was incorrectly maintained. As a result, planning stability was lacking and deadlines could often not be met.

Solution

A regression/machine learning algorithm has been developed to accurately predict operation durations based on operation and PDC information.

Added value

  • An average of 30% more accurate estimation of operation durations.
  • Rapid identification of data quality and process problems in production data collection.

Highlights

  • Algorithm learns from over 1,000,000 PDC bookings and hundreds of thousands of completed operations.
  • In addition, it takes into account dozens of relevant input variables such as current resources, material or core data.
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