Our course "Data Mining for Manufacturing: Preventive Maintenance, Failure Prediction, Quality Control" provides an introduction into data mining concepts and methods and demonstrates how data mining can be employed in various tasks related to manufacturing. Production, machine, sensor, product, order, sales, and market data can be analyzed and lead to models for predicting machine failure, product quality, demand, supply, prices, sales, etc. reducing failure times and costs, improving quality, and maximizing profits. This course also describes the practical steps necessary to create such models with the open-source data mining software RapidMiner. The large amount of practical examples and exercises enable the participants to design appropriate data mining processes and apply the gained knowledge to data mining problems in their production environment and to solve them efficiently and successfully.

Details

• Course ID: 1204
• Number of days: 2 days
• Location: Dortmund, Germany
• Target audience: decision makers, practitioners, analysts, developers
• Previous knowledge: basic knowledge of computer programs and mathematics
• Methods: lectures, discussions, individual and group work, exercises on realistic data. Participants may introduce own work and project specific questions in order to find particular solutions together with the trainer and other participants.
• Content: this course includes a compact introduction into the foundations of data mining and into the software RapidMiner. It addresses beginners and intermediate learners. Topics of this course are
  • Foundations of data mining and the data mining process
  • Applications of data mining in the production process
  • Using sensor data and other inputs to predict, avoid, and reduce machine failure
  • Predicting and improving product quality and avoiding production failures
  • Predicting demand, resources, and supply needed
  • Predicting supply prices
  • Monitoring production processes and recognition of critical situations
  Extensive exercises on different data sets will be performed for all topics.

Prices

Value added tax (VAT) may have to be added to these prices.