The value of process mining in strategic-decision making

Abstract

Process mining is becoming a popular big data analytics and artificial intelligence tool in business today as a result of technological advancements being accelerated by digitalization. Fast process adaptation to keep up with the pace of the quickly evolving business environment, however, is a significant challenge for the majority of organisations. Process mining has the potential to make big data-driven insights accessible, transforming data mining into a process mining capability therefore raising the value of process mining. Additionally, process mining insights give businesses the ability to improve and optimise processes to gain a competitive advantage. However, the value of process mining in organisational strategic decision-making is underexplored; this study aimed to fill this research gap. A qualitative research design was adopted to explore new insights and answer three research questions. This study put forward the exploratory design using the philosophy of interpretivism and the inductive approach to developing new theory. Semi-structured interviews with eleven participants who have implemented process mining were conducted to gain further insights into this phenomenon. With respect to the value of process mining this research found benefits that include better decision making, cost savings, business process transparency and enhancement of business processes through automation. However, organisations still need to rely on human intelligence and expert intelligence to get value from process mining. On the context of how organisations get value from process mining four main stages of process mining implementation were found in this study. The steps identified include: planning, data extraction and processing, mining and analysis, and process improvement. With respect to the value of process mining in strategic decision-making, this study found process mining to be applicable in this context while providing some examples. Generally, the findings of this study contribute to the extant literature related to process mining.