Leveraging Process Mining to Optimize Internal Employee Mobility Strategies

Simon Vos (), Johannes Smedt (), Chris Wuytens () and Wouter Verbeke ()
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Simon Vos: KU Leuven
Johannes Smedt: KU Leuven
Chris Wuytens: Acerta Consult
Wouter Verbeke: KU Leuven

A chapter in Business Process Management Cases Vol. 3, 2025, pp 15-28 from Springer

Abstract: Abstract (a) Situation faced: The significance of human resource (HR) analytics in facilitating data-driven decision-making for managing internal employee mobility has been emphasized by the recent increasing competition in attracting and retaining the best employees referred to as the war for talent. Existing HR analytics methods typically provide support for operational and tactical decision-making. However, there is a need for long-term strategic decision support. Additionally, as current methods for managing internal mobility are being challenged, the development of new appropriate HR analytics methods is necessary. (b) Action taken: In collaboration with KU Leuven, Acerta Consult implemented process mining techniques to address this issue. Specifically, process discovery techniques were applied to the event logs of HR data to generate employee journey maps (EJMs) that depict the different historic paths employees have taken within an organization. (c) Results achieved: These EJMs demonstrated the difference between idealized career paths and the actual complexity of employee mobility. These discrepancies have the potential to reshape the incorrect assumptions held by HR managers. The data-driven insight gained through these EJMs can assist HR professionals by providing decision support for a wide range of cases including the identification of infrequent growth paths, analyzing hard-to-fill positions, and better understanding the causes of turnover. (d) Lessons learned: The process perspective on internal mobility provides valuable insights for HR managers and was able to shed light on the general complexity of careers. As a result, this perspective can serve as a foundation for further analyses, including predictive and prescriptive modeling, while taking into account HR-specific constraints and challenges.

Date: 2025
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DOI: 10.1007/978-3-031-80793-0_2

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