Putting the SWORD to the Test: Finding Workarounds with Process Mining

Wouter van der Waal; Inge van de Weerd; Iris Beerepoot; Xixi Lu; Teus Kappen; Saskia Haitjema; Hajo Reijers

doi:10.1007/s12599-023-00846-3

Putting the SWORD to the Test: Finding Workarounds with Process Mining

Wouter van der Waal^*, Inge van de Weerd, Iris Beerepoot, Xixi Lu, Teus Kappen, Saskia Haitjema, Hajo Reijers

^*Corresponding author for this work

Process Management and Analytics

Research output: Contribution to journal › Article › Academic › peer-review

Abstract

Workarounds, or deviations from standardized operating procedures, can indicate discrepancies between theory and practice in work processes. Traditionally, observations and interviews have been used to identify workarounds, but these methods can be time-consuming and may not capture all workarounds. The paper presents the Semi-automated WORkaround Detection (SWORD) framework, which leverages event log traces to help process analysts identify workarounds. The framework is evaluated in a multiple-case study of two hospital departments. The results of the study indicate that with SWORD we were able to identify 11 unique workaround types, with limited knowledge about the actual processes. The framework thus supports the discovery of workarounds while minimizing the dependence on domain knowledge, which limits the time investment required by domain experts. The findings highlight the importance of leveraging technology to improve the detection of workarounds and to support process improvement efforts in organizations.

Original language	English
Pages (from-to)	171–190
Number of pages	20
Journal	Business Informatics Systems Engineering
Volume	67
Issue number	2
Early online date	29 Jan 2024
DOIs	https://doi.org/10.1007/s12599-023-00846-3
Publication status	Published - 2025

Bibliographical note

Publisher Copyright:
© 2024, The Author(s).

Funding

This publication is part of the WorkAround Mining (WAM!) project with project number 18490 which is partly financed by the Dutch Research Council (NWO). We would like to thank M.C.H. de Groot, PhD for his help during data extraction, as well as B. Vrijsen, MD and L.G. Exalto, MD, PhD for their time to evaluate our findings.

Funders	Funder number
Nederlandse Organisatie voor Wetenschappelijk Onderzoek

Keywords

Event data
Pattern detection
Process mining
Workaround

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s12599-023-00846-3 (1)Final published version, 1.11 MBLicence: CC BY

Cite this

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title = "Putting the SWORD to the Test: Finding Workarounds with Process Mining",

abstract = "Workarounds, or deviations from standardized operating procedures, can indicate discrepancies between theory and practice in work processes. Traditionally, observations and interviews have been used to identify workarounds, but these methods can be time-consuming and may not capture all workarounds. The paper presents the Semi-automated WORkaround Detection (SWORD) framework, which leverages event log traces to help process analysts identify workarounds. The framework is evaluated in a multiple-case study of two hospital departments. The results of the study indicate that with SWORD we were able to identify 11 unique workaround types, with limited knowledge about the actual processes. The framework thus supports the discovery of workarounds while minimizing the dependence on domain knowledge, which limits the time investment required by domain experts. The findings highlight the importance of leveraging technology to improve the detection of workarounds and to support process improvement efforts in organizations.",

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AU - van der Waal, Wouter

AU - van de Weerd, Inge

AU - Beerepoot, Iris

AU - Lu, Xixi

AU - Kappen, Teus

AU - Haitjema, Saskia

AU - Reijers, Hajo

PY - 2025

Y1 - 2025

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AB - Workarounds, or deviations from standardized operating procedures, can indicate discrepancies between theory and practice in work processes. Traditionally, observations and interviews have been used to identify workarounds, but these methods can be time-consuming and may not capture all workarounds. The paper presents the Semi-automated WORkaround Detection (SWORD) framework, which leverages event log traces to help process analysts identify workarounds. The framework is evaluated in a multiple-case study of two hospital departments. The results of the study indicate that with SWORD we were able to identify 11 unique workaround types, with limited knowledge about the actual processes. The framework thus supports the discovery of workarounds while minimizing the dependence on domain knowledge, which limits the time investment required by domain experts. The findings highlight the importance of leveraging technology to improve the detection of workarounds and to support process improvement efforts in organizations.

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Putting the SWORD to the Test: Finding Workarounds with Process Mining

Abstract

Bibliographical note

Funding

Keywords

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