Using Big Data to Improve Safety Performance: An Application of Process Mining to Enhance Data Visualisation

• Published in: Big data research Vol. 25; p. 100210
• Main Authors: Pika, Anastasiia, ter Hofstede, Arthur H.M., Perrons, Robert K., Grossmann, Georg, Stumptner, Markus, Cooley, Jim
• Format: Journal Article
• Language: English
• Published: Elsevier Inc 15.07.2021
• Subjects: Big Data; Industrial safety; Permit to Work; Process mining; Process mining; Big Data; Industrial safety; Permit to Work
• ISSN: 2214-5796; 2214-580X
• DOI: 10.1016/j.bdr.2021.100210

The management of health and safety plays an important role in safety performance, and is therefore an important foundational element in an organisation's overall sustainable development. Many organisations are now able to collect vast amounts of data to shed light on the underlying causes behind accidents and safety-related incidents, and to spot patterns that can lead to solutions. Despite these well-intentioned Big Data collection efforts, however, accident statistics in asset-intensive industries remain stubbornly high as the data frequently fails to reveal actionable insights. In this paper, we answer Wang and Wu's (2020) [60] and Wang et al.'s (2019) [61] calls for the application of Big Data science to the safety domain by exploring the potential of applying tools and techniques from process mining, a research area concerned with analysing process execution data, to derive novel insights from and improve the visualisation of safety process data. We demonstrate how these tools can yield useful insights in the occupational health and safety domain by analysing process execution data from a Permit to Work system in an Australian energy company. Specifically, the analysis presented here highlights the underlying complexity of the organisation's Permit to Work process, reveals conformance and performance issues, and uncovers resources associated with conformance issues and changes in the frequency of such issues over time, thereby underlining the need to simplify the system. Encouraged by these fresh perspectives and insights delivered by process mining, we hope that this novel application will be a catalyst for further research at the interface between these research disciplines.