Enabling Process Innovation via Deviance Mining and Predictive Monitoring

Marlon Dumas () and Fabrizio Maria Maggi ()
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Marlon Dumas: University of Tartu
Fabrizio Maria Maggi: University of Tartu

A chapter in BPM - Driving Innovation in a Digital World, 2015, pp 145-154 from Springer

Abstract: Abstract A long-standing challenge in the field of business process management is how to deal with processes that exhibit high levels of variability, such as customer lead management, product design or healthcare processes. One thing that is understood about these processes is that they require process designs and support environments that leave considerable freedom so that process workers can readily deviate from pre-established paths. At the same time, consistent management of these processes requires workers and process owners to understand the implications of their actions and decisions on the performance of the process. We present two emerging techniques—deviance mining and predictive monitoring—that leverage information hidden in business process execution logs in order to provide guidance to stakeholders so that they can steer the process towards consistent and compliant outcomes and higher process performance. Deviance mining deals with the analysis of process execution logs offline in order to identify typical deviant executions and to characterize deviance that leads to better or to worse performance. Predictive monitoring meanwhile aims at predicting—at runtime—the impact of actions and decisions of process participants on the probable outcomes of ongoing process executions. Together, these two techniques enable evidence-based management of business processes, where process workers and analysts continuously receive guidance to achieve more consistent and compliant process outcomes and a higher performance.

Keywords: Business Process; Recommender System; Process Worker; Business Process Management; Business Goal (search for similar items in EconPapers)
Date: 2015
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Persistent link: https://EconPapers.repec.org/RePEc:spr:mgmchp:978-3-319-14430-6_10

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DOI: 10.1007/978-3-319-14430-6_10

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