From automatic workaround detection to process improvement: a case study

Nesi Outmazgin, Wouter van der Waal*, Iris Beerepoot, Irith Hadar, Inge van de Weerd, Pnina Soffer

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

Abstract

The improvement of business processes through learning and investigating workarounds has attracted research attention in recent years. Workarounds can be considered as a symptom of needed process improvements but adopting them does not necessarily lead to an appropriate one. Hence, identifying and understanding the underlying problems or perceived barriers that motivate workarounds is essential for suggesting an appropriate process improvement solution. In this paper, we propose a streamlined end-to-end approach that attempts to leverage workarounds to improve processes. This approach is based on two pillars: (1) a semi-automated workarounds detection by using the SWORD framework, which consists of twenty-two patterns to detect workarounds from events logs. (2) workarounds investigation and analysis using a motivational model that serves to reveal problems that lie under the identified workarounds. This analysis contributes toward proposing tailored and targeted process improvements. We report on an industrial case study that demonstrates the proposed approach, from workaround detection to proposing tailored process improvements. The improvements have been accepted by the organization and are currently being implemented.
Original languageEnglish
Title of host publicationBusiness Process Management Forum - BPM 2023 Forum, Proceedings
PublisherSpringer
Pages372-390
Number of pages19
Volume490
ISBN (Print)9783031416224
DOIs
Publication statusPublished - 1 Sept 2023

Publication series

NameLecture Notes in Business Information Processing
Volume490 LNBIP
ISSN (Print)1865-1348
ISSN (Electronic)1865-1356

Keywords

  • Business process improvements
  • Workarounds
  • Automatic detection
  • Motivational analysis
  • Event logs
  • Case study

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