Data-Driven Identification and Analysis of Waiting Times in Business Processes

Muhammad Awais Ali (), Fredrik Milani () and Marlon Dumas ()
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Muhammad Awais Ali: University of Tartu
Fredrik Milani: University of Tartu
Marlon Dumas: University of Tartu

Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, 2025, vol. 67, issue 2, No 4, 208 pages

Abstract: Abstract Reducing waiting times in end-to-end business processes is a recurrent concern in the field of business process management. The uptake of data-driven approaches in this field in the past two decades, most notably process mining, has created new opportunities for fine-grained analysis of waiting times based on execution data. As a result, a wide range of approaches for waiting time identification and analysis on the basis of business process execution data have been reported in the literature. In many instances, different approaches have considered different notions of waiting time and different causes for waiting time. At present, there is a lack of a consolidated overview of these manifold approaches, and how they relate to or complement each other. The article presents a literature review that starts with the question of what approaches for identification and analysis of waiting time are available in the literature, and then refines this question by adding questions which shed light onto different causes and notions of waiting time. The survey leads to a multidimensional taxonomy of data-driven waiting time analysis techniques, in terms of purpose, causes, and measures. The survey identifies gaps in the field, chiefly a scarcity of integrated multi-causal approaches to analyze waiting times in business processes, and a lack of empirically validated approaches in the field.

Keywords: Business process management; Process mining; Waiting time (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s12599-024-00868-5

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