Digital Twins as Software and Service Development Ecosystems in Industry 4.0: Towards a Research Agenda

Xu, Yi; Päivärinta, Tero; Kuvaja, Pasi

doi:10.1007/978-981-15-7530-3_5

Cited by 4 publications

(8 citation statements)

References 37 publications

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“…A third implication of IDTs for innovation management originates from the alternatives of orchestrating digital platforms (Otto, 2022; Xu et al, 2020). IDTs distributed among different actors necessitate suitable governance models that account for all divergent interests in the innovation process (Silva et al, 2022).…”

Section: Discussion and Implications For Future Innovation Managementmentioning

confidence: 99%

“…IDTs could be either governed by one keystone player (Iansiti & Levien, 2004), the established model for many central data platforms (Projection no. 02), or by an alliance of actors managing a data space together in a decentralized and federated governance mode (Otto, 2022; Silva et al, 2022; Xu et al, 2020).…”

Section: Discussion and Implications For Future Innovation Managementmentioning

confidence: 99%

“…The analysis of IDTs can be structured along with these four attributes (see Table 1). Utilizing DTs across organizational borders can yield new dimensions of industrial value creation and thus innovation through digital platforms when the right interfaces are designed, required capabilities are accessible, a specific organizational structure is established, and an adequate governance mode is in place that is accepted by all members of the platform ecosystem (Gawer, 2014; Silva et al, 2022; Xu et al, 2020).…”

Section: Technical and Theoretical Backgroundmentioning

confidence: 99%

“…However, research lacks implication on how IDTs impact the amount and quality of external knowledge inflows into the innovation process while governing the resulting openness (Michael et al, 2022; West et al, 2021). IDTs build on the idea of open data exchange between all connected entities following a platform logic (Gawer, 2014; Xu et al, 2020) to create value and drive innovation jointly (Redeker et al, 2021; Silva et al, 2022). But, data openness implies that manufacturers surrender control over their data and its resource advantages (Alexy et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

“…Companies also face uncertainties about the effects of IDTs on their business model, or how IDTs can become drivers for business model innovation (Stark, 2022; Xu et al, 2020). So far, it is uncertain how IDTs can overcome some of the limitations that humans face in innovation management, such as information searching, processing, and evaluation enabling data‐driven decision‐making while collaborating and cocreating across different domains and external stakeholders (Michael et al, 2022; West et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Interconnected digital twins and the future of digital manufacturing: Insights from a Delphi study

Dyck

Lüttgens

Piller

et al. 2023

J of Product Innov Manag

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Digital twins (DTs) are virtual representations of real-world entities like production assets, processes, or products. They are updated at a defined fidelity and frequency along the entire life cycle from development and engineering over the production or implementation of a product or process until its usage stage. Interconnected digital twins (IDTs) are DTs shared and connected across organizations with the objective to create holistic simulation and decision models of an entire physical system. In this paper, we investigate how IDTs shape future digital manufacturing scenarios and impact innovation management. We present the results of a real-time Delphi study, analyzing quantitative and qualitative estimates on a set of 24 projections, forecasting the future of digital manufacturing with a projection horizon towards 2030. Using this data and 22 additional use cases of IDTs in manufacturing companies, we present a baseline scenario where our Delphi panel reached a consensus, representing a likely future of digital manufacturing in 2030. By analyzing projections where our expert panels' evaluations vary widely, we identify key design decisions that may impact innovation management along the dimensions of variation, choice, and control in digital manufacturing. We explain how IDTs will impact external knowledge inflows, the emergence and governance of industrial data spaces, and the potential of data-driven and AI-enabled applications for prediction and regulation to drive better decisionmaking and continuous innovation.

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Section: Discussion and Implications For Future Innovation Managementmentioning

confidence: 99%

Section: Discussion and Implications For Future Innovation Managementmentioning

confidence: 99%

Section: Technical and Theoretical Backgroundmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Interconnected digital twins and the future of digital manufacturing: Insights from a Delphi study

Dyck

Lüttgens

Piller

et al. 2023

J of Product Innov Manag

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Digital Twin: A Conceptual View

Stjepandić¹,

Sommer²,

Stobrawa

2021

Springer Series in Advanced Manufacturing

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Digital Twin Ecosystems: Potential Stakeholders and Their Requirements

Liyanage

Tripathi

Päivärinta

et al. 2022

Lecture Notes in Business Information Processing

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Context: As industries are heading for digital transformation through Industry 4.0, the concept of Digital Twin (DT) -a software for digital transformation, has become popular. Many industries use DT for its advantages, such as predictive maintenance and real-time remote monitoring. Within DT domain, an emerging topic is the concept of an ecosystem-a digital platform that would create value for different stakeholders in an ecosystem of DT-driven products and services. However, current empirical research on Digital Twin Ecosystems (DTEs) is still in its infancy, with a limited amount of knowledge on potential stakeholders and their requirements. Objective/Methodology: Thus, the objective of this research was to explore potential stakeholders and their requirements. The research employed an empirical research methodology in which semi-structured interviews were conducted with DT professionals for data collection. Results: Data analysis of the study revealed 13 potential stakeholders who were categorized as primary (manufacturers, suppliers, subcontractors, and intelligent robots), secondary (maintenance service providers, platform integration service providers, tech companies, etc.), and tertiary (research organizations, third-party value-added service providers, cyber security firms, etc.). This study also presents the different requirements of these stakeholders in detail. Contribution:The study contributes to both research and industry by identifying possible stakeholders and their requirements. It contributes to the literature by adding new knowledge on DTEs and fills a research gap while contributing industry by providing ample knowledge to the industry's practitioners that is useful in the development and maintenance of a healthy DTE.

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Digital Twins as Software and Service Development Ecosystems in Industry 4.0: Towards a Research Agenda

Cited by 4 publications

References 37 publications

Interconnected digital twins and the future of digital manufacturing: Insights from a Delphi study

Interconnected digital twins and the future of digital manufacturing: Insights from a Delphi study

Digital Twin: A Conceptual View

Digital Twin Ecosystems: Potential Stakeholders and Their Requirements

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