Paper Number

1032

Paper Type

CRP

Abstract

Business process improvement (BPI) is crucial to every business, as inefficiencies jeopardise an organisation’s success. Predominant methods for BPI build on static process models, which are often incomplete, outdated, and lack execution-related insights. Process mining bears the potential to add execution-related insights into the process. However, organisations often lack the methodological expertise to apply process mining systematically to find process improvement options. Automating parts of BPI thus holds the potential to assist users without BPI expertise and enables data-driven BPI at scale. We introduce the FLAC method, which guides users in transforming conceptual BPI patterns into specific rulesets. Once transformed, they can be repeatedly applied to event logs to generate options for process improvement. An instantiation of the FLAC method on several BPI patterns and evaluation of its subsequent application to an event log confirmed its applicability and high relevance to practice by significantly reducing the time-to-insight.

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Jun 14th, 12:00 AM

The FLAC Method: Data-Facilitated Discovery of Business Process Improvement Options

Business process improvement (BPI) is crucial to every business, as inefficiencies jeopardise an organisation’s success. Predominant methods for BPI build on static process models, which are often incomplete, outdated, and lack execution-related insights. Process mining bears the potential to add execution-related insights into the process. However, organisations often lack the methodological expertise to apply process mining systematically to find process improvement options. Automating parts of BPI thus holds the potential to assist users without BPI expertise and enables data-driven BPI at scale. We introduce the FLAC method, which guides users in transforming conceptual BPI patterns into specific rulesets. Once transformed, they can be repeatedly applied to event logs to generate options for process improvement. An instantiation of the FLAC method on several BPI patterns and evaluation of its subsequent application to an event log confirmed its applicability and high relevance to practice by significantly reducing the time-to-insight.

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