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

Tony Clark

doi:10.1007/978-3-030-63479-7_1

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

Tony Clark^*

^*Corresponding author for this work

College of Engineering and Physical Sciences

Research output: Chapter in Book/Published conference output › Conference publication

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.

Original language	English
Title of host publication	The Practice of Enterprise Modeling - 13th IFIP Working Conference, PoEM 2020, Proceedings
Editors	Jānis Grabis, Dominik Bork
Publisher	Springer
Pages	3-7
Number of pages	5
Volume	400
ISBN (Electronic)	978-3-030-63479-7
ISBN (Print)	9783030634780
DOIs	https://doi.org/10.1007/978-3-030-63479-7_1
Publication status	Published - 18 Nov 2020
Event	13th IFIP Working Conference on the Practice of Enterprise Modeling, PoEM 2020 - Riga, Latvia Duration: 25 Nov 2020 → 27 Nov 2020

Publication series

Name	Lecture Notes in Business Information Processing
Volume	400
ISSN (Print)	1865-1348
ISSN (Electronic)	1865-1356

Conference

Conference	13th IFIP Working Conference on the Practice of Enterprise Modeling, PoEM 2020
Country/Territory	Latvia
City	Riga
Period	25/11/20 → 27/11/20

Access to Document

10.1007/978-3-030-63479-7_1

Cite this

Clark, T. (2020). The Uncertain Enterprise: Achieving Adaptation Through Digital Twins and Machine Learning Extended Abstract. In J. Grabis, & D. Bork (Eds.), The Practice of Enterprise Modeling - 13th IFIP Working Conference, PoEM 2020, Proceedings (Vol. 400, pp. 3-7). (Lecture Notes in Business Information Processing; Vol. 400). Springer. https://doi.org/10.1007/978-3-030-63479-7_1

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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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year = "2020",

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booktitle = "The Practice of Enterprise Modeling - 13th IFIP Working Conference, PoEM 2020, Proceedings",

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note = "13th IFIP Working Conference on the Practice of Enterprise Modeling, PoEM 2020 ; Conference date: 25-11-2020 Through 27-11-2020",

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Clark, T 2020, The Uncertain Enterprise: Achieving Adaptation Through Digital Twins and Machine Learning Extended Abstract. in J Grabis & D Bork (eds), The Practice of Enterprise Modeling - 13th IFIP Working Conference, PoEM 2020, Proceedings. vol. 400, Lecture Notes in Business Information Processing, vol. 400, Springer, pp. 3-7, 13th IFIP Working Conference on the Practice of Enterprise Modeling, PoEM 2020, Riga, Latvia, 25/11/20. https://doi.org/10.1007/978-3-030-63479-7_1

The Uncertain Enterprise: Achieving Adaptation Through Digital Twins and Machine Learning Extended Abstract. / Clark, Tony.
The Practice of Enterprise Modeling - 13th IFIP Working Conference, PoEM 2020, Proceedings. ed. / Jānis Grabis; Dominik Bork. Vol. 400 Springer, 2020. p. 3-7 (Lecture Notes in Business Information Processing; Vol. 400).

Research output: Chapter in Book/Published conference output › Conference publication

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

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