Towards Data-Driven Digital Twins for Smart Manufacturing

Deena P. Francis*, Sanja Lazarova-Molnar, Nader Mohamed

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review


The adoption of a digital twin for a smart factory offers several advantages, such as improved production and reduced costs, and energy consumption. Due to the growing demands of the market, factories have adopted the reconfigurable manufacturing paradigm, wherein the structure of the factory is constantly changing. This situation presents a unique challenge to traditional modeling and simulation approaches. To deal with this scenario, we propose a generic data-driven framework for automated construction of digital twins for smart factories. The novel aspects of our proposed framework include a pure data-driven approach incorporating machine learning and process mining techniques, and continuous model improvement and validation.

Original languageEnglish
Title of host publicationProceedings of the 27th International Conference on Systems Engineering
EditorsHenry Selvaraj, Grzegorz Chmaj, Dawid Zydek
Publication date2021
ISBN (Print)9783030657956
Publication statusPublished - 2021
Event27th International Conference on Systems Engineering - Las Vegas, United States
Duration: 14 Dec 202016 Dec 2020
Conference number: 27


Conference27th International Conference on Systems Engineering
Country/TerritoryUnited States
CityLas Vegas
SeriesLecture Notes in Networks and Systems

Bibliographical note

Publisher Copyright:
© 2021, Springer Nature Switzerland AG.


  • Data-driven
  • Digital twin
  • Machine learning
  • Process mining
  • Reconfigurable manufacturing
  • Simulation
  • Smart factory


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