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

Abstract

Recently, production plants have become very complex environments, in which the final output is the result of the favourable interplay of several processes. Successful management of such domains strictly depends on the ability to grasp the current disposition of both the physical and the managerial processes. To achieve this goal, the use of structured data-based methods has proven to be very effective. Yet, literature lacks successful applications, especially regarding production support processes (e.g., order acquisition, procure-to-pay, product development), which are directly connected to the overall system performance. This work proposes an approach to enabling automated mapping and controlling of production support processes starting from execution data recorded by IT systems. The limitations of existing methodologies are addressed by exploiting the combined application of two process mining algorithms: heuristic and inductive miner. This approach's managerial implications are described within the application to a real case study, in which the main phases of the procure-to-pay process (P2P) of a manufacturing company are identified and analysed automatically. The proposed approach proves its effectiveness in the context of application. The numerical results demonstrate that process mining can effectively bring tangible benefits in terms of viable improvements not only to physical production processes, but also to information flows and production support processes that are highly crucial for guaranteeing the prosperity of an enterprise, yet extremely hard to manage and control.

Data-Driven Discovery of Manufacturing Processes and Performance from Worker Localisation

“…Overall, our approach shows how data coming from worker localisation sensors can be exploited to discover data-driven process models of manufacturing activities, which then can be used to improve the efficiency of manufacturing systems, which can be particularly valuable for complex systems that are not fully automated. Given that process mining is mainly applied for processes where orders/products are tracked [14,15,16], the success of our methodology shows that these techniques can also be used when workers' locations are tracked, without tracking orders/products.…”

“…Process mining techniques have been widely applied in the manufacturing industry for identifying process deviations and detecting bottlenecks, with manufacturing being the third largest application domain for these techniques [13], as demonstrated in previous studies [14,15,16]. Process mining applications in manufacturing typically rely on event logs of activities performed on specific products, which are often generated using IT tracking systems.…”

“…Process mining applications in manufacturing typically rely on event logs of activities performed on specific products, which are often generated using IT tracking systems. [14] uses an order status tracking system to compose event logs on ordered products and the activities performed on them. [15] relies on code readers placed on machines that automatically scan product IDs and record a timestamp when they process them in their event logs.…”

Using Worker Position Data for Human-Driven Decision Support in Labour-Intensive Manufacturing

“…In manufacturing, it is the third largest application domain and is used to identify process deviations and detect bottlenecks, typically relying on event logs generated by IT tracking systems of activities performed on specific products [ 4 ]. For instance, [ 24 ] uses an order status tracking system to create event logs for ordered products and the activities performed on them, while [ 25 ] relies on code readers placed on machines to automatically scan product IDs and record timestamps. [ 26 ] also includes product information.…”