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Minimizing Overprocessing Waste in Business Processes via Predictive Activity Ordering

  • Ilya VerenichEmail author
  • Marlon Dumas
  • Marcello La Rosa
  • Fabrizio Maria Maggi
  • Chiara Di Francescomarino
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9694)

Abstract

Overprocessing waste occurs in a business process when effort is spent in a way that does not add value to the customer nor to the business. Previous studies have identified a recurrent overprocessing pattern in business processes with so-called “knockout checks”, meaning activities that classify a case into “accepted” or “rejected”, such that if the case is accepted it proceeds forward, while if rejected, it is cancelled and all work performed in the case is considered unnecessary. Thus, when a knockout check rejects a case, the effort spent in other (previous) checks becomes overprocessing waste. Traditional process redesign methods propose to order knockout checks according to their mean effort and rejection rate. This paper presents a more fine-grained approach where knockout checks are ordered at runtime based on predictive machine learning models. Experiments on two real-life processes show that this predictive approach outperforms traditional methods while incurring minimal runtime overhead.

Keywords

Process mining Process optimization Overprocessing waste 

Notes

Acknowledgments

This research is funded by the Australian Research Council Discovery Project DP150103356 and the Estonian Research Council.

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Ilya Verenich
    • 1
    • 2
    Email author
  • Marlon Dumas
    • 1
    • 2
  • Marcello La Rosa
    • 1
  • Fabrizio Maria Maggi
    • 2
  • Chiara Di Francescomarino
    • 3
  1. 1.Queensland University of TechnologyBrisbaneAustralia
  2. 2.University of TartuTartuEstonia
  3. 3.FBK-IRSTTrentoItaly

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