Self-learning Production Control Using Algorithms of Artificial Intelligence

  • Ben LuetkehoffEmail author
  • Matthias Blum
  • Moritz Schroeter
Conference paper
Part of the IFIP Advances in Information and Communication Technology book series (IFIPAICT, volume 506)


Manufacturing companies are facing an increasingly turbulent market – a market defined by products growing in complexity and shrinking product life cycles. This leads to a boost in planning complexity accompanied by higher error sensitivity. In practice, IT systems and sensors integrated into the shop floor in the context of Industry 4.0 are used to deal with these challenges. However, while existing research provides solutions in the field of pattern recognition or recommended actions, a combination of the two approaches is neglected. This leads to an overwhelming amount of data without contributing to an improvement of processes. To address this problem, this study presents a new platform-based concept to collect and analyze the high-resolution data with the use of self-learning algorithms. Herby, patterns can be identified and reproduced, allowing an exact prediction of the future system behavior. Artificial intelligence maximizes the automation of the reduction and compensation of disruptive factors.


Production control Self-learning algorithms Data analytics 


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Copyright information

© IFIP International Federation for Information Processing 2017

Authors and Affiliations

  • Ben Luetkehoff
    • 1
    Email author
  • Matthias Blum
    • 1
  • Moritz Schroeter
    • 1
  1. 1.Production ManagementFIR at RWTH Aachen UniversityAachenGermany

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