Abstract
After implementing and deploying a redesigned business process, it may happen that the new process does not meet our expectations. For example, certain types of unforeseen exceptions may arise, the processing time of some tasks may be much higher than expected due to these exceptions, and queues may build up to the extent that process participants start taking shortcuts due to high pressure, while customers become unsatisfied due to long waiting times. A first step to address these issues is to understand what is actually happening during the execution of the process. This is the goal of the process monitoring phase of the BPM lifecycle. This chapter gives an overview of process monitoring techniques and tools. The chapter first focuses on performance dashboards, both for offline and online monitoring. Next, it dives into process mining techniques, including methods for automated process discovery, conformance checking, performance mining, and variants analysis.
If you can’t measure something, you can’t understand it. If you can’t understand it, you can’t control it. If you can’t control it, you can’t improve it.
H. James Harrington (1929–)
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References
W.M.P. van der Aalst, Process Mining: Data Science in Action, 2nd edn. (Springer, Berlin, 2016)
A. Adriansyah, Aligning observed and modeled behavior. PhD thesis, Eindhoven University of Technology (2014)
S. Aguirre, C. Parra, M. Sepúlveda, Methodological proposal for process mining projects. Int. J. Bus. Process Integr. Manag. 8(2), 102–113 (2017)
A. Augusto, R. Conforti, M. Dumas, M. La Rosa, Split miner: Discovering accurate and simple business process models from event logs, in Proceedings of the IEEE International Conference on Data Mining (ICDM) (IEEE Computer Society, 2017)
A. Augusto, R. Conforti, M. Dumas, M. La Rosa, G. Bruno, Automated discovery of structured process models: Discover structured vs. discover and structure, in Proc. of the 35th International Conference on Conceptual Modeling (ER), Cham, Switzerland, 2016 (Springer, Berlin, 2016)
A. Augusto, R. Conforti, M. Dumas, M. La Rosa, F.M. Maggi, A. Marrella, M. Mecella, A. Soo, Automated discovery of process models from event logs: Review and benchmark. CoRR, abs/1705.02288 (2017)
W. Eckerson, Performance Dashboards: Measuring, Monitoring, and Managing Your Business, 2nd edn. (Wiley, New York, 2010)
L. García-Bañuelos, N.R.T.P. van Beest, M. Dumas, M. La Rosa, Complete and interpretable conformance checking of business processes. IEEE Trans. Softw. Eng. (2017). https://doi-org.vu-nl.idm.oclc.org/10.1109/TSE.2017.2668418
P. Harmon, Analyzing activities. BPTrends Newsl. 1(4) (2003). http://www.bptrends.com
IEEE TaskForce on Process Mining, Process Mining Manifesto. http://www.win.tue.nl/ieeetfpm/doku.php?id=shared:process_mining_manifesto. Accessed October 2017, 2011
C.N. Knaflic, Storytelling with Data: A Data Visualization Guide for Business Professionals (Wiley, New York, 2015)
S.J.J. Leemans, D. Fahland, W.M.P. van der Aalst, Discovering Block-Structured Process Models from Event Logs Containing Infrequent Behaviour (Springer, Cham, 2014), pp. 66–78
M. Leyer, D. Heckl, J. Moormann, Process performance measurement. Handbook on Business Process Management, Volume 2 (2015), pp. 227–241
J. Munoz-Gama, Conformance Checking and Diagnosis in Process Mining: Comparing Observed and Modeled Processes (Springer, Berlin, 2016)
A. Rozinat, W.M.P. van der Aalst, Conformance checking of processes based on monitoring real behavior. Inf. Syst. 33(1), 64–95 (2008)
S. Suriadi, M.T. Wynn, C. Ouyang, A.H.M. ter Hofstede, N.J. van Dijk, Understanding process behaviours in a large insurance company in australia: A case study, in Proceedings of the 25th International Conference on Advanced Information Systems Engineering (CAiSE). Lecture Notes in Computer Science, vol. 7908 (Springer, Berlin, 2013), pp. 449–464
N.R.T.P. van Beest, M. Dumas, L. García-Bañuelos, M. La Rosa, Log delta analysis: Interpretable differencing of business process event logs, in Proceedings of the 13th International Conference on Business Process Management (BPM) (Springer, Berlin, 2015), pp. 386–405
M.L. van Eck, X. Lu, S.J.J. Leemans, W.M.P. van der Aalst, PM ˆ2 : A process mining project methodology, in Proceedings of the International Conference on Advanced Information Systems Engineering (CAiSE) (Springer, Berlin, 2015), pp. 297–313
A. Weijters, J. Ribeiro, Flexible Heuristics Miner (FHM), in Proceedings of the International Conference on Computational Intelligence and Data Mining (CIDM) (IEEE Computer Society, 2011)
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Dumas, M., La Rosa, M., Mendling, J., Reijers, H.A. (2018). Process Monitoring. In: Fundamentals of Business Process Management. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-56509-4_11
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DOI: https://doi.org/10.1007/978-3-662-56509-4_11
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