Process Mining for Dynamic Modeling of Smart Manufacturing Systems: Data Requirements

• Published in: Procedia CIRP Vol. 107; pp. 546 - 551
• Main Authors: Friederich, Jonas, Lugaresi, Giovanni, Lazarova-Molnar, Sanja, Matta, Andrea
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 2022; ELSEVIER
• Subjects: Computational Engineering, Finance, and Science; Computer Aided Engineering; Computer Science; Data Structures and Algorithms; discrete event simulation; machine behavior; Model generation; process mining; reliability models; reliability models; Model generation; process mining; discrete event simulation; machine behavior; Model generation discrete event simulation process mining machine behavior reliability models
• ISSN: 2212-8271; 2212-8271
• DOI: 10.1016/j.procir.2022.05.023

Modern manufacturing systems can benefit from the use of digital tools to support both short- and long-term decisions. Meanwhile, such systems reached a high level of complexity and are frequently subject to modifications that can quickly make the digital tools obsolete. In this context, the ability to dynamically generate models of production systems is essential to guarantee their exploitation on the shop-floors as decision-support systems. The literature offers approaches for generating digital models based on real-time data streams. These models can represent a system more precisely at any point in time, as they are continuously updated based on the data. However, most approaches consider only isolated aspects of systems (e.g., reliability models) and focus on a specific modeling purpose (e.g., material flow identification). The research challenge is therefore to develop a novel framework that systematically enables the combination of models extracted through different process mining algorithms. To tackle this challenge, it is critical to define the requirements that enable the emergence of automated modeling and simulation tasks. In this paper, we therefore derive and define data requirements for the models that need to be extracted. We include aspects such as the structure of the manufacturing system and the behavior of its machines. The paper aims at guiding practitioners in designing coherent data structures to enable the coupling of model generation techniques within the digital support system of manufacturing companies.