Advertisement

Minimizing Overprocessing Waste in Business Processes via Predictive Activity Ordering

Conference paper
First Online:
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 
This is a preview of subscription content, log in to check access.

Notes

Acknowledgments

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

References

  1. 1.
    Wang, J.X.: Lean Manufacturing: Business Bottom-Line Based. CRC Press, London (2010)CrossRefGoogle Scholar
  2. 2.
    Bauch, C.: Lean product development: making waste transparent. Master’s thesis, Massachusetts Institute of Technology and Technical University of Munich (2004)Google Scholar
  3. 3.
    van der Aalst, W.M.P.: Re-engineering knock-out processes. Decis. Support Syst. 30(4), 451–468 (2001)CrossRefGoogle Scholar
  4. 4.
    Jansen-Vullers, M.H., Netjes, M., Reijers, H.A.: Business process redesign for effective e-commerce. In: Proceedings of the 6th International Conference on Electronic Commerce, pp. 382–391. ACM (2004)Google Scholar
  5. 5.
    Reijers, H.A., Mansar, S.L.: Best practices in business process redesign: an overview and qualitative evaluation of successful redesign heuristics. Omega 33(4), 283–306 (2005)CrossRefGoogle Scholar
  6. 6.
    Lohrmann, M., Reichert, M.: Effective application of process improvement patterns to business processes. Softw. Syst. Model. 15, 353–375 (2014)CrossRefGoogle Scholar
  7. 7.
    Pourshahid, A., Mussbacher, G., Amyot, D., Weiss, M.: An aspect-oriented framework for business process improvement. In: Babin, G., Kropf, P., Weiss, M. (eds.) E-Technologies: Innovation in an Open World. LNBIP, vol. 26, pp. 290–305. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  8. 8.
    Niedermann, F., Radeschütz, S., Mitschang, B.: Business process optimization using formalized optimization patterns. In: Abramowicz, W. (ed.) BIS 2011. LNBIP, vol. 87, pp. 123–135. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  9. 9.
    Maggi, F.M., Di Francescomarino, C., Dumas, M., Ghidini, C.: Predictive monitoring of business processes. In: Jarke, M., Mylopoulos, J., Quix, C., Rolland, C., Manolopoulos, Y., Mouratidis, H., Horkoff, J. (eds.) CAiSE 2014. LNCS, vol. 8484, pp. 457–472. Springer, Heidelberg (2014)Google Scholar
  10. 10.
    der Aalst, W.M.P., Schonenberg, M.H., Song, M.: Time prediction based on process mining. Inf. Syst. 36(2), 450–475 (2011)CrossRefGoogle Scholar
  11. 11.
    Conforti, R., de Leoni, M., La Rosa, M., van der Aalst, W.M.P., ter Hofstede, A.H.M.: A recommendation system for predicting risks across multiple business process instances. Decis. Support Syst. 69, 1–19 (2015)CrossRefGoogle Scholar
  12. 12.
    Leontjeva, A., Conforti, R., Di Francescomarino, C., Dumas, M., Maggi, F.M.: Complex symbolic sequence encodings for predictive monitoring of business processes. In: Motahari-Nezhad, H.R., Recker, J., Weidlich, M. (eds.) bpm 2015. LNCS, pp. 297–313. Springer, Cham (2015)CrossRefGoogle Scholar
  13. 13.
    Verenich, I., Dumas, M., Rosa, M.L., Maggi, F.M., Francescomarino, C.D.: Complex symbolic sequence clustering and multiple classifiers for predictive process monitoring. In: BPI 2015 Workshop, pp. 1–12 (2016)Google Scholar
  14. 14.
    Zeng, S., Melville, P., Lang, C.A., Boier-Martin, I., Murphy, C.: Using predictive analysis to improve invoice-to-cash collection. In: Proceeding of the 14th ACM SIGKDD International Conference, KDD 2008, p. 1043 (2008)Google Scholar
  15. 15.
    Hwang, J.P., Park, S., Kim, E.: A new weighted approach to imbalanced data classification problem via support vector machine with quadratic cost function. Expert Syst. Appl. 38(7), 8580–8585 (2011)CrossRefGoogle Scholar
  16. 16.
    Grigori, D., Casati, F., Castellanos, M., Dayal, U., Sayal, M., Shan, M.C.: Business process intelligence. Comput. Ind. 53(3), 321–343 (2004)CrossRefGoogle Scholar
  17. 17.
    Bradley, A.P.: The use of the area under the ROC curve in the evaluation of machine learning algorithms. Pattern Recogn. 30(7), 1145–1159 (1997)CrossRefGoogle Scholar
  18. 18.
    Bondora: Loan Dataset. https://www.bondora.ee/en/invest/statistics/data_export. Accessed 23 Oct 2015
  19. 19.
    Buijs, J.: 3TU.DC Dataset: Receipt phase of an environmental permit applicationprocess (WABO). https://data.3tu.nl/repository/uuid:a07386a5-7be3-4367-9535-70bc9e77dbe6. Accessed 30 Oct 2015
  20. 20.
    Meyer, D., Dimitriadou, E., Hornik, K., Weingessel, A.: e1071: Misc Functions of the Department of Statistics, Probability Theory Group, TU Wien (2015)Google Scholar
  21. 21.
    Hill, S., Provost, F., Volinsky, C.: Network-Based Marketing: Identifying Likely Adopters via Consumer Networks. Stat. Sci. 21(2), 256–276 (2006)MathSciNetCrossRefzbMATHGoogle Scholar
  22. 22.
    Weiss, G.M., Provost, F.: Learning when training data are costly: the effect of class distribution on tree induction. J. Artif. Intell. Res. 19, 315–354 (2003)zbMATHGoogle Scholar
  23. 23.
    Kull, M., Flach, P.A.: Reliability maps: a tool to enhance probability estimates and improve classification accuracy. In: Calders, T., Esposito, F., Hüllermeier, E., Meo, R. (eds.) ECML PKDD 2014, Part II. LNCS, vol. 8725, pp. 18–33. Springer, Heidelberg (2014)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Queensland University of TechnologyBrisbaneAustralia
  2. 2.University of TartuTartuEstonia
  3. 3.FBK-IRSTTrentoItaly

Personalised recommendations

Cite paper

Buy options