TOWARDS ADAPTIVE ENTERPRISES USING DIGITAL TWINS

Vinay Kulkarni; Tony Clark; Souvik Barat

doi:10.1109/WSC40007.2019.9004956

TOWARDS ADAPTIVE ENTERPRISES USING DIGITAL TWINS

Vinay Kulkarni
, Tony Clark
, Souvik Barat

College of Engineering and Physical Sciences

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

18 Citations (Scopus)

97 Downloads (Pure)

Abstract

Modern enterprises are large complex systems operating in highly dynamic environments thus requiring quick response to a variety of change drivers. Moreover, they are systems of systems wherein understanding is available in localized contexts only and that too is typically partial and uncertain. With the overall system behaviour hard to know a-priori and conventional techniques for system-wide analysis either lacking in rigour or defeated by the scale of the problem, the current practice often exclusively relies on human expertise for monitoring and adaptation. We present an approach that combines ideas from modeling & simulation, reinforcement learning and control theory to make enterprises adaptive. The approach hinges on the concept of Digital Twin - a set of relevant models that are amenable to analysis and simulation. The paper describes illustration of approach in two real world use cases.

Original language	English
Title of host publication	Proceedings of the 2019 Winter Simulation Conference
Publisher	IEEE
Pages	60-74
Number of pages	15
ISBN (Electronic)	978-172813283-9
DOIs	https://doi.org/10.1109/WSC40007.2019.9004956
Publication status	Published - 11 Dec 2019
Event	Winter Simulation Conference 2019 - National Harbor, United States Duration: 8 Dec 2019 → 11 Dec 2019

Conference

Conference	Winter Simulation Conference 2019
Country/Territory	United States
City	National Harbor
Period	8/12/19 → 11/12/19

Bibliographical note

© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

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10.1109/WSC40007.2019.9004956

TOWARDS ADAPTIVE ENTERPRISES USING DIGITAL TWINSAccepted author manuscript, 985 KB

Cite this

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title = "TOWARDS ADAPTIVE ENTERPRISES USING DIGITAL TWINS",

abstract = "Modern enterprises are large complex systems operating in highly dynamic environments thus requiring quick response to a variety of change drivers. Moreover, they are systems of systems wherein understanding is available in localized contexts only and that too is typically partial and uncertain. With the overall system behaviour hard to know a-priori and conventional techniques for system-wide analysis either lacking in rigour or defeated by the scale of the problem, the current practice often exclusively relies on human expertise for monitoring and adaptation. We present an approach that combines ideas from modeling \& simulation, reinforcement learning and control theory to make enterprises adaptive. The approach hinges on the concept of Digital Twin - a set of relevant models that are amenable to analysis and simulation. The paper describes illustration of approach in two real world use cases.",

author = "Vinay Kulkarni and Tony Clark and Souvik Barat",

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N1 - © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

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N2 - Modern enterprises are large complex systems operating in highly dynamic environments thus requiring quick response to a variety of change drivers. Moreover, they are systems of systems wherein understanding is available in localized contexts only and that too is typically partial and uncertain. With the overall system behaviour hard to know a-priori and conventional techniques for system-wide analysis either lacking in rigour or defeated by the scale of the problem, the current practice often exclusively relies on human expertise for monitoring and adaptation. We present an approach that combines ideas from modeling & simulation, reinforcement learning and control theory to make enterprises adaptive. The approach hinges on the concept of Digital Twin - a set of relevant models that are amenable to analysis and simulation. The paper describes illustration of approach in two real world use cases.

AB - Modern enterprises are large complex systems operating in highly dynamic environments thus requiring quick response to a variety of change drivers. Moreover, they are systems of systems wherein understanding is available in localized contexts only and that too is typically partial and uncertain. With the overall system behaviour hard to know a-priori and conventional techniques for system-wide analysis either lacking in rigour or defeated by the scale of the problem, the current practice often exclusively relies on human expertise for monitoring and adaptation. We present an approach that combines ideas from modeling & simulation, reinforcement learning and control theory to make enterprises adaptive. The approach hinges on the concept of Digital Twin - a set of relevant models that are amenable to analysis and simulation. The paper describes illustration of approach in two real world use cases.

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Y2 - 8 December 2019 through 11 December 2019

ER -

TOWARDS ADAPTIVE ENTERPRISES USING DIGITAL TWINS

Abstract

Conference

Bibliographical note

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