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The Uncertain Enterprise: Achieving Adaptation Through Digital Twins and Machine Learning Extended Abstract

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Book cover The Practice of Enterprise Modeling (PoEM 2020)

Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 400))

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Abstract

Systems, such as production plants, logistics networks, IT service companies, and international financial companies, are complex systems operating in highly dynamic environments that need to respond quickly to a variety of change drivers.

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Notes

  1. 1.

    https://deloitte.com/us/en/insights/focus/tech-trends/2020/digital-twin-applicat-ions-bridging-the-physical-and-digital.html.

  2. 2.

    https://simio.com/blog/2019/11/14/top-trends-in-simulation-and-digital-twins-technology-for-2020/.

  3. 3.

    https://forbes.com/sites/bernardmarr/2019/04/23/7-amazing-examples-of-digital-twin-technology-in-practice/#4cd0672d6443.

  4. 4.

    https://www.gartner.com/en/documents/3957042/market-trends-software-provi-ders-ramp-up-to-serve-the-em.

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Author information

Authors and Affiliations

  1. Aston University, Birmingham, UK

    Tony Clark

Authors
  1. Tony Clark
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Correspondence to Tony Clark .

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Editors and Affiliations

  1. Riga Technical University, Riga, Latvia

    Jānis Grabis

  2. TU Wien, Vienna, Wien, Austria

    Dominik Bork

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© 2020 IFIP International Federation for Information Processing

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Clark, T. (2020). The Uncertain Enterprise: Achieving Adaptation Through Digital Twins and Machine Learning Extended Abstract. In: Grabis, J., Bork, D. (eds) The Practice of Enterprise Modeling. PoEM 2020. Lecture Notes in Business Information Processing, vol 400. Springer, Cham. https://doi.org/10.1007/978-3-030-63479-7_1

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  • DOI: https://doi.org/10.1007/978-3-030-63479-7_1

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-63478-0

  • Online ISBN: 978-3-030-63479-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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