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Identifying Candidate Tasks for Robotic Process Automation in Textual Process Descriptions

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Enterprise, Business-Process and Information Systems Modeling (BPMDS 2018, EMMSAD 2018)

Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 318))

Abstract

The continuous digitization requires organizations to improve the automation of their business processes. Among others, this has lead to an increased interest in Robotic Process Automation (RPA). RPA solutions emerge in the form of software that automatically executes repetitive and routine tasks. While the benefits of RPA on cost savings and other relevant performance indicators have been demonstrated in different contexts, one of the key challenges for RPA endeavors is to effectively identify processes and tasks that are suitable for automation. Textual process descriptions, such as work instructions, provide rich and important insights about this matter. However, organizations often maintain hundreds or even thousands of them, which makes a manual analysis unfeasible for larger organizations. Recognizing the large manual effort required to determine the current degree of automation in an organization’s business processes, we use this paper to propose an approach that is able to automatically do so. More specifically, we leverage supervised machine learning to automatically identify whether a task described in a textual process description is manual, an interaction of a human with an information system or automated. An evaluation with a set of 424 activities from a total of 47 textual process descriptions demonstrates that our approach produces satisfactory results.

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Notes

  1. 1.

    http://www.math.utah.edu/~wisnia/glossary.html.

  2. 2.

    Note that the way Weka generates ROC curves results in only as many threshold values as there are distinct probability values assigned to the positive class. Therefore, the ROC curves from Fig. 3 are only based on three data points. This, however, does not reduce their informative value.

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Authors and Affiliations

  1. Vrije Universiteit Amsterdam, De Boelelaan 1081, 1081 HV, Amsterdam, The Netherlands

    Henrik Leopold, Han van der Aa & Hajo A. Reijers

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  1. Henrik Leopold
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  2. Han van der Aa
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  3. Hajo A. Reijers
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Corresponding author

Correspondence to Han van der Aa .

Editor information

Editors and Affiliations

  1. University of Duisburg-Essen, Essen, Germany

    Jens Gulden

  2. University of Haifa, Haifa, Israel

    Iris Reinhartz-Berger

  3. Munich University of Applied Sciences, Munich, Germany

    Rainer Schmidt

  4. INESC-ID, University of Lisbon, Lisbon, Portugal

    SĂ©rgio Guerreiro

  5. Luxembourg Institute of Science and Technology, Esch-sur-Alzette, Luxembourg

    Wided Guédria

  6. Saint Louis University, St. Louis, Missouri, USA

    Palash Bera

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Leopold, H., van der Aa, H., Reijers, H.A. (2018). Identifying Candidate Tasks for Robotic Process Automation in Textual Process Descriptions. In: Gulden, J., Reinhartz-Berger, I., Schmidt, R., Guerreiro, S., Guédria, W., Bera, P. (eds) Enterprise, Business-Process and Information Systems Modeling. BPMDS EMMSAD 2018 2018. Lecture Notes in Business Information Processing, vol 318. Springer, Cham. https://doi.org/10.1007/978-3-319-91704-7_5

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  • DOI: https://doi.org/10.1007/978-3-319-91704-7_5

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