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

Jonas Friederich*, Giovanni Lugaresi, Sanja Lazarova-Molnar, Andrea Matta

*Corresponding author for this work

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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.

Original languageEnglish
JournalProcedia CIRP
Pages (from-to)546-551
Publication statusPublished - 2022
Event55th CIRP Conference on Manufacturing Systems, CIRP CMS 2022 - Lugano, Switzerland
Duration: 29. Jun 20221. Jul 2022


Conference55th CIRP Conference on Manufacturing Systems, CIRP CMS 2022


  • discrete event simulation
  • machine behavior
  • Model generation
  • process mining
  • reliability models


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