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Predictive Monitoring of Business Process Execution Delays

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Advances in Information Systems, Artificial Intelligence and Knowledge Management (ICIKS 2023)

Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 486))

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Abstract

Nowadays, organizations are becoming increasingly aware of the importance of better utilizing their knowledge assets and adopting a quality management model based on a process approach. This can be achieved by adopting a multidisciplinary approach that combines the fields of Knowledge Management, Business Process Management and Process Mining. Therefore, to improve their performance and increase their responsiveness, organizations must identify, manage and monitor all the business processes (BP) that are likely to mobilize crucial knowledge. In fact, implementing an IT system that automates business processes is necessary to achieve these goals. In this context, we propose a new method for predicting the execution times of business processes baptized BPETPM. This method is based on the CRISP-DM approach. We used a Process Mining techniques particularly the machine learning to exploit the execution data of a workflow engine. In order to prove the applicability of this method we have developed an intelligent system for predicting execution time of BP named iBPMS4PET. The applicative framework of this research work is the incoming mail management process as part of a group health insurance.

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Notes

  1. 1.

    https://www.objetconnecte.com/bpm-presentation.

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Author information

Authors and Affiliations

  1. MIRACL Laboratory, ISIMS, University of Sfax, P.O. Box 242, 3021, Sfax, Tunisia

    Walid Ben Fradj & Mohamed Turki

Authors
  1. Walid Ben Fradj
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  2. Mohamed Turki
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Corresponding author

Correspondence to Walid Ben Fradj .

Editor information

Editors and Affiliations

  1. ESC Amiens and University of Picardie Jules Verne, Amiens, France

    Inès Saad

  2. University of Paris Dauphine-PSL, Paris, France

    Camille Rosenthal-Sabroux

  3. University of Sfax, Sfax, Tunisia

    Faiez Gargouri

  4. University of Portsmouth, Portsmouth, UK

    Salem Chakhar

  5. University of Portsmouth, Portsmouth, UK

    Nigel Williams

  6. University of Portsmouth, Portsmouth, UK

    Ella Haig

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Ben Fradj, W., Turki, M. (2024). Predictive Monitoring of Business Process Execution Delays. In: Saad, I., Rosenthal-Sabroux, C., Gargouri, F., Chakhar, S., Williams, N., Haig, E. (eds) Advances in Information Systems, Artificial Intelligence and Knowledge Management. ICIKS 2023. Lecture Notes in Business Information Processing, vol 486. Springer, Cham. https://doi.org/10.1007/978-3-031-51664-1_8

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  • DOI: https://doi.org/10.1007/978-3-031-51664-1_8

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