digital twin thesis

A FRAMEWORK TO INVESTIGATE KEY CHARACTERISTICS OF DIGITAL TWINS AND THEIR IMPACT ON PERFORMANCE

The modern world of manufacturing is in the middle of an industrial revolution with the digital and physical worlds integrating through cyber-physical systems.  Through a virtual model that is able to communicate with its physical system known as the Digital Twin, catered decisions can be made based on the current state of the system.  The digital twin presents immense opportunities and challenges as there is a greater need to understand how these new technologies work together. 

This thesis is an experimental investigation of the characteristics of the essential components of the Digital Twin.  A Digital Twin Framework is developed to explore the impacts of model accuracy and update frequency on the system’s performance measure. A simple inventory management system and a more complex manufacturing plant is modeled through the framework providing a method to study the interactions of the physical and digital systems with empirical data.

As the decision policies are affected by the state changes in the system, designing the Digital Twin must account for the direct and indirect impact of its components. 

Furthermore, we show the importance of communication and information exchange between the Digital Twin and its physical system.  A key characteristic for developing and applying a digital twin is to monitor the update frequency and its impact on performance.  Through the study there are implications of optimal combinations of the digital twin components and how the physical system responds.  There are also limits to how effective the Digital Twin can be in certain instances and is an area of research that needs further investigation.  

The goal of this work is to help practitioners and researchers implement and use the Digital Twin more effectively.  Better understanding the interactions of the model components will help guide designing Digital Twins to be more effective as they become an integral part of the future of manufacturing.

Degree Type

• Doctor of Philosophy
• Industrial Engineering

Campus location

• West Lafayette

Advisor/Supervisor/Committee Chair

Additional committee member 2, additional committee member 3, additional committee member 4, usage metrics.

• Modelling and simulation
• Manufacturing processes and technologies (excl. textiles)
• Manufacturing engineering not elsewhere classified
• Manufacturing management

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• Perspective
• Published: 18 July 2022

Potential and limitations of digital twins to achieve the Sustainable Development Goals

• Asaf Tzachor   ORCID: orcid.org/0000-0002-4032-4996 1 , 2 ,
• Soheil Sabri   ORCID: orcid.org/0000-0002-2167-2717 3 ,
• Catherine E. Richards 1 , 4 ,
• Abbas Rajabifard 3 &
• Michele Acuto   ORCID: orcid.org/0000-0003-4320-0531 5  

Nature Sustainability volume  5 ,  pages 822–829 ( 2022 ) Cite this article

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• Civil engineering
• Information technology
• Interdisciplinary studies
• Sustainability

Could computer simulation models drive our ambitions to sustainability in urban and non-urban environments? Digital twins, defined here as real-time, virtual replicas of physical and biological entities, may do just that. However, despite their touted potential, digital twins have not been examined critically in urban sustainability paradigms—not least in the Sustainable Development Goals framework. Accordingly, in this Perspective, we examine their benefits in promoting the Sustainable Development Goals. Then, we discuss critical limitations when modelling socio-technical and socio-ecological systems and go on to discuss measures to treat these limitations and design inclusive, reliable and responsible computer simulations for achieving sustainable development.

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Acknowledgements

This paper was made possible through the support of a grant from Templeton World Charity Foundation, Inc. The opinions expressed in this publication are those of the author(s) and do not necessarily reflect the views of Templeton World Charity Foundation, Inc. We thank Y. Chen from the University of Melbourne for supporting the production of Figs. 1 – 3 .

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• Asaf Tzachor

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Soheil Sabri & Abbas Rajabifard

University of Cambridge, Department of Engineering, Cambridge, UK

• Catherine E. Richards

University of Melbourne, Faculty of Architecture, Building and Planning, Melbourne, Victoria, Australia

Michele Acuto

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A.T., S.S., C.E.R., A.R. and M.A. developed the paper jointly and all contributed equally to the writing of the text.

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A.T., C.E.R., S.S. and M.A. declare no competing interests. A.R. manages the Centre for Spatial Data Infrastructures and Land Administration (CSDILA) at the University of Melbourne. CSDILA developed the Fishermans Bend Digital Twin proof of concept in partnership with the Department of Environment, Land, Water and Planning (DELWP), State Government of Victoria, Australia.

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Tzachor, A., Sabri, S., Richards, C.E. et al. Potential and limitations of digital twins to achieve the Sustainable Development Goals. Nat Sustain 5 , 822–829 (2022). https://doi.org/10.1038/s41893-022-00923-7

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Semantic Digital Twins in the Industrial Internet of Things

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Date of exam, date of publication, involved institutions, citable links.

• Handle: https://hdl.handle.net/20.500.11811/9884
• URN: https://nbn-resolving.org/urn:nbn:de:hbz:5-66298

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Home > Robinson College of Business > Robinson College of Business Executive Programs > Business Administration > BUS_ADMIN_DISS > 162

Business Administration Dissertations

The concept of digital twins and its impact on organizational performance.

Deepa Vivekanandan , Georgia State University Follow

Author ORCID Identifier

https://orcid.org/0000-0002-4714-9713

Date of Award

Spring 5-6-2022

Degree Type

Dissertation

Degree Name

Doctor of Business Administration (DBA)

First Advisor

Dr. Richard L. Baskerville

Second Advisor

Dr. Lars Mathiassen

Third Advisor

Dr. Subhashish Samaddar

The world has become “digital” in many aspects and due to which witnessed a great deal of innovation in products, services, and experiences. The innovation using digital technologies has served meaningfully for many organizations. Digital Twin is an emerging concept that was introduced two decades ago which creates a virtual replica by extending the use of some of the advanced digital techniques and technologies.

Limited research is available to conceptualize Digital Twins clearly, analyze the stages of maturity of Digital Twins, and articulate the novel aspects of the technology with an emphasis on similarities, differences, and connections between the Digital Twin and other existing technologies such as modeling, simulation, artificial intelligence, and the internet of things which would be the first focus of the study. The characterization can provide clarity on Digital Twin technology to the academicians, practitioners, and organizational leadership.

Digital Twin has started to transition from being an idea or a prototype to a real-world implementation tied to one or more applications. Research studies, academic papers, and many case studies are available to demonstrate existing and potential applications in multiple industries and fields. There is very limited research that ties the applications to organizational performance constructs, namely financial performance, operational excellence, and customer perspective which would be the second focus of the study. The elaboration on how the Digital Twin technology impacts organizational performance aspects would benefit the audience looking for proven approaches and models to realize business value from the technology.

Organizations experience a number of challenges during the planning and implementation of Digital Twins that spans across data, business processes, systems and applications, infrastructure, stakeholders, business operating model, technology adoption, business value realization, regulatory aspects, and more. The third focus of the study would explore the Digital Twin related challenges using academic literature, practice literature, and practitioner experiences. The exploration of challenges would benefit the practitioners to effectively manage issues, mitigate risks, set realistic expectations with leadership, and plan communications with stakeholders.

Qualitative grounded theory research involving academic literature, practice sources, and practitioner interviews as the primary data collection techniques is used for the three focus areas of the study. The primary data analysis approaches involve coding, memo writing, and analytic induction. The study involves an iterative data collection and analysis from all three data sources. The record of the study is documented as a thesis for use by the relevant audience.

https://doi.org/10.57709/28941911

Recommended Citation

Vivekanandan, Deepa, "The Concept of Digital Twins and its Impact on Organizational Performance." Dissertation, Georgia State University, 2022. doi: https://doi.org/10.57709/28941911

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Home > Theses and Dissertations > 6483

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Simulation-based and data-driven approaches to industrial digital twinning towards autonomous smart manufacturing systems, date of award.

Summer 2021

Document Type

Open Access Dissertation

Mechanical Engineering

First Advisor

A manufacturing paradigm shift from conventional control pyramids to decentralized, service-oriented, and cyber-physical systems (CPSs) is taking place in today’s Industry 4.0 revolution. Generally accepted roles and implementation recipes of cyber systems are expected to be standardized in the future of manufacturing industry. Developing affordable and customizable cyber-physical production system (CPPS) and digital twin implementations infuses new vitality for current Industry 4.0 and Smart Manufacturing initiatives. Specially, Smart Manufacturing systems are currently looking for methods to connect factories to control processes in a more dynamic and open environment by filling the gaps between virtual and physical systems.

The work presented in this dissertation first utilizes industrial digital transformation methods for the automation of robotic manufacturing systems, constructing a simulation-based surrogate system as a digital twin to visually represent manufacturing cells, accurately simulate robot behaviors, promptly predict system faults and adaptively control manipulated variables. Then, a CPS-enabled control architecture is presented that accommodates: intelligent information systems involving domain knowledge, empirical model, and simulation; fast and secured industrial communication networks; cognitive automation by rapid signal analytics and machine learning (ML) based feature extraction; and interoperability between machine and human. A successful semantic integration of process indicators is fundamental to future control autonomy. Hence, a product-centered signature mapping approach to automated digital twinning is further presented featuring a hybrid implementation of smart sensing, signature-based 3D shape feature extractor, and knowledge taxonomy. Furthermore, capabilities of members in the family of Deep Reinforcement Learning (DRL) are explored within the context of manufacturing operational control intelligence. Preliminary training results are presented in this work as a trial to incorporate DRL-based Artificial Intelligence (AI) to industrial control processes.

The results of this dissertation demonstrate a digital thread of autonomous Smart Manufacturing lifecycle that enables complex signal processing, semantic integration, automatic derivation of manufacturing strategies, intelligent scheduling of operations and virtual verification at a system level. The successful integration of currently available industrial platforms not only provides facile environments for process verification and optimization, but also facilitates derived strategies to be readily deployable to physical shop floor.

The dissertation finishes with summary, conclusions, and suggestions for further work.

© 2021, Kaishu Xia

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Xia, K.(2021). Simulation-Based and Data-Driven Approaches to Industrial Digital Twinning Towards Autonomous Smart Manufacturing Systems. (Doctoral dissertation). Retrieved from https://scholarcommons.sc.edu/etd/6483

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Digital twins: When and why to use one

April 30, 2024 by Mickael Brossard , Mithun Kamat , Tomás Lajous , Kayvaun Rowshankish , and Cenk Tunasar

This blog post is the second in a three-part series on digital twins. The first post explained how generative AI and digital twins make a powerful pairing. The third post in the series will explore an in-depth case study on how generative AI and digital twins could be used to enhance customer experience.

When most people consider digital twins, they think of the visualization and simulation tools used to develop innovative new products. Offshore oil platforms, racing sailboats, luxury automobile engines, hotels, and train stations are just some of the things that have been designed and refined using advanced digital simulations.

These uses represent a fraction of what digital twins can accomplish. It is helpful to think of digital twins less as a tool for designers, engineers, and manufacturers and more as a laboratory in which nearly any organization can optimize its most precious resource—information—to continually push the boundaries of what it can accomplish. By using data to mirror real-world situations, digital twins can be deployed to create, fine-tune, or entirely reimagine nearly any complex process or system, including supply chains, public transit systems, and assembly lines.

For example, a global retailer recently set out to rethink its supply chain with an eye toward cutting costs, optimizing service, and boosting sustainability. It was a complex problem that involved optimizing an array of key levers, such as inventory positioning, product flow optimization, supply planning, and carbon emissions. Drawing on the organization’s vast quantities of real-time data, a team created a digital twin of its global supply chain operations—a sprawling system of manufacturing facilities, freight and cargo operations, third-party contractors, and distribution centers. The digital replica allowed the retailer to test more than 50 scenarios a day, examining potential outcomes for various large and small choices along the supply chain, all without any real-life disruptions. An optimization engine embedded within the digital twin provided users with informed recommendations in the meantime. Ultimately, the company made a series of optimized decisions that sparked a 7 percent reduction in carbon emissions and a 5 percent improvement in customer orders received on time.

Digital twins offer immense value, but only under the right circumstances

Clearly, digital twins have come a long way since the 1960s, when NASA engineers tested simulations of Apollo spacecraft using early digital models. It’s also clear that a growing number of organizations are intrigued by the technology and are looking at ways to adopt it. Yet it’s important to understand that digital twins are not a one-size-fits-all solution for business problems. Digital twins are as complex and nuanced as the systems and objects they seek to emulate and can deliver value only in particular circumstances. How, then, can a business determine when and where to implement a digital twin? There are several key characteristics to look for:

• High-stakes areas with high costs and real revenue on the line. The problem to be solved must be relevant enough to warrant significant investment. Building digital twins requires considerable effort and resources, and businesses should be fairly certain they can derive value from the effort.
• A complex or dynamic environment. Some products and systems are so complicated that inefficiencies and their root causes are difficult to identify. In the supply chain, for example, a change to one element can lead to second- or third-order effects that manifest over time. This is the kind of situation that could benefit from a digital twin. Simpler environments, characterized by more direct and apparent chains of cause and effect, can likely be optimized manually through more standard forms of investigation and analysis.
• The availability of high-quality data. Digital twins require a substantial amount of high-quality data to accurately represent their real-world counterparts and simulate the underlying product, system, or process. The data set must be robust and reliable across all the problem’s parameters for the issue to be solved; data that is incomplete or otherwise flawed can undermine, or even doom, a twin’s effectiveness.
• The environment being simulated is recurring. Digital twins are built to be used and reused, to repeatedly simulate and optimize multivariable problems. If the objective is to solve for a one-time optimization of a single variable, an optimization model would work better than a digital twin.

If these requirements are met, a digital twin can deliver considerable impact. For example, a leading semiconductor manufacturer was repeatedly losing bids because it relied on slow-moving, inefficient design and production processes. This was a high-stakes, complex problem with no immediately apparent root cause—precisely the kind of problem that digital twins are best suited to address. Using historical data, the business built a digital twin that leveraged AI and machine learning to rapidly run multiple simulations of potential designs. This process generated insights that decreased time to market and increased first-time-right designs by up to 25 percent. Engineering capacity saw a 20 percent boost as time spent on manual physics models, which previously had used traditional methods and took hours, was reduced to seconds through the creation of a deep learning surrogate model.

Even when it meets the foundational prerequisites (including a viable business case, high-quality data, and ongoing operational demand), a digital twin is far from a sure thing. Digital twins can fail to deliver for a number of reasons. One common pitfall is the incorrect configuration of technical components, which can compromise the accuracy and reliability of the digital twin’s simulations and insights. Additionally, the long-term viability of the business case—and thus the digital twin—may be limited as market conditions, technological advancements, or organizational priorities shift over time. Finally, a lack of sustained maintenance and updates to the digital twin can lead to its gradual obsolescence. These challenges underscore the importance of holistic planning, ongoing evaluation, and agile adaptation strategies to ensure the enduring success of digital-twin initiatives.

Taking the next step

With a clear understanding of business objectives and meticulous diligence in implementation, a digital twin can unlock new realms of possibilities. The emergence of generative AI, with its ability to analyze vast quantities of structured and unstructured data and generate insights via user-friendly interfaces, promises to supercharge the promise even more (see the first post in this series, “ Digital twins and generative AI: A powerful pairing ”). If your company has a complex and thorny problem that is holding back performance, the technology is well worth your consideration.

Mickael Brossard is a partner in McKinsey’s Paris office; Mithun Kamat is a partner in the Dallas office; Tomás Lajous and Kayvaun Rowshankish are senior partners in the New York office; and Cenk Tunasar is a partner in the Boston office.

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