Process Mining in Manufacturing: Goals, Techniques and Applications

Abstract

Process mining is a discipline positioned between business process management and data mining. It applies algorithms on real event data extracted from information systems that support business processes, to construct as-is process models, and improve them automatically. The benefits can be versatile, from gaining insight into the real execution of a process, to detecting process bottlenecks, activity loops, or social networks of process resources. Several literature reviews have focused on the application of process mining in the healthcare industry and on process mining discipline in general, without the reviews of other application domains. This paper presents the results of a systematic literature review on case studies of process mining projects applied in the manufacturing industry. Case studies are analyzed according to the following aspects: project goals, information systems or devices/equipment that generate event data, particular business processes, event log characteristics, different types and perspectives of process mining performed, tools and techniques used for preprocessing activities, discovery, conformance checking, process enhancement, and social network analysis. Finally, an attempt is made to discover the impact of goals, types of processes, and event log characteristics on the selection of process mining types, perspectives, tools, and techniques.

Empowering Manufacturing Environments with Process Mining-Based Statistical Process Control

“…The objective of PM is to govern underlying processes by leveraging various technologies and methodologies to achieve diverse goals, including identifying actual process flows, analyzing social networks, and comparing actual and desired processes using event logs [12]. The versatility and benefits of process mining have been recognized across industries, with applications ranging from healthcare and business process management to manufacturing [2,[36][37][38].…”

“…In modern manufacturing, employing advanced technology is essential to enhance both the efficiency of processes and the quality of products. Additionally, the contemporary manufacturing setting produces vast amounts of data from various sources such as sensors and machinery [2]. In order to effectively monitor and enhance industrial processes in this data-rich environment, it is vital to use appropriate controls such as statistical process control (SPC).…”

Exploiting a combined process mining approach to enhance the discovery and analysis of support processes in manufacturing

“…Several PM approaches can deal with the discovery of an operational process, and there are successful implementations in the manufacturing domain (Stefanovic et al, 2021). However, most approaches focus on generating models that highlight the overall process view or the average behaviour, while assigning minor importance to accenting critical points.…”

New Approach to the Analysis of Manufacturing Processes with the Support of Data Science

“…This approach combines statistics, information sciences, and mathematical computations to determine the relationship between key factors such as utilization, timing parameters, and others. Moreover, it offers the possibility of a quick identification of the main root causes of waste throughout the process [3].…”