The Digital Twin as a Service Enabler: From the Service Ecosystem to the Simulation Model

Meierhofer, Jürg; West, Shaun; Rapaccini, Mario; Barbieri, Cosimo

doi:10.1007/978-3-030-38724-2_25

Cited by 27 publications

(27 citation statements)

References 25 publications

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“…All these actors have different needs for the data, and the data also has different impacts on their businesses. Therefore, the sales of complex data-based solutions is multifaceted, while the value may be different for different actors (e.g., [4]; [5]) and thus, there may appear challenges to identify the parties or persons who should, or are willing to pay for the data-based solution or persons who actually initiate the decision-making process.…”

Section: Introductionmentioning

confidence: 99%

Selling the Value of Complex Data-Based Solution for Industrial Customers

Rantala¹,

Valjakka²,

Kokkonen³

et al. 2021

IFIP Advances in Information and Communication Technology

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Selling complex data-based solutions is multifaceted as ecosystem actors perceive the value differently during the product lifecycle. The purpose of this paper is to study data-based solution sales in business ecosystems by presenting findings from nine recent interviews with manufacturing industry professionals. The results are demonstrated in two categories: 1) challenges in sharing, selling and buying data, and 2) the value of data for different actors in an ecosystem-like business environment. The managerial implications consist of clarifying the scattered viewpoints for selling data-based solutions and value formulation for different actors in an ecosystem. Theoretical contributions provide important aspects for the gap between business and sales research of data-based solutions, as current literature mainly focuses on the technical aspects.

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Section: Introductionmentioning

confidence: 99%

Selling the Value of Complex Data-Based Solution for Industrial Customers

Rantala¹,

Valjakka²,

Kokkonen³

et al. 2021

IFIP Advances in Information and Communication Technology

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show abstract

“…DTs at this level elevate the productivity, resilience, and transparency of production processes, which enable end-to-end availability of data along the entire value chain as well as a holistic sustainability assessment on this basis. From a business development perspective, AI-enabled DT can additionally be considered a service agent [ 113 ], providing innovative smart services via DT network platforms [ 114 ] and subscription business models [ 115 ], thus contributing sustainably to long-term innovation for manufacturers, and helping them in accomplishing a paradigm shift from the one-time provision of production hardware to the ongoing delivery of manufacturing solutions ( SG -factor). The importance of a diversified product and service portfolio is particularly evident in times of global crisis.…”

Section: Sustainable Resilient Manufacturingmentioning

confidence: 99%

A Survey on AI-Driven Digital Twins in Industry 4.0: Smart Manufacturing and Advanced Robotics

Huang

Shen²,

et al. 2021

Sensors

189

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Digital twin (DT) and artificial intelligence (AI) technologies have grown rapidly in recent years and are considered by both academia and industry to be key enablers for Industry 4.0. As a digital replica of a physical entity, the basis of DT is the infrastructure and data, the core is the algorithm and model, and the application is the software and service. The grounding of DT and AI in industrial sectors is even more dependent on the systematic and in-depth integration of domain-specific expertise. This survey comprehensively reviews over 300 manuscripts on AI-driven DT technologies of Industry 4.0 used over the past five years and summarizes their general developments and the current state of AI-integration in the fields of smart manufacturing and advanced robotics. These cover conventional sophisticated metal machining and industrial automation as well as emerging techniques, such as 3D printing and human–robot interaction/cooperation. Furthermore, advantages of AI-driven DTs in the context of sustainable development are elaborated. Practical challenges and development prospects of AI-driven DTs are discussed with a respective focus on different levels. A route for AI-integration in multiscale/fidelity DTs with multiscale/fidelity data sources in Industry 4.0 is outlined.

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“…It might reach over several underlying hierarchy levels depending on its scope, as well as on the system topology. A systematic development procedure consisting of six steps for the DT sequence is discussed in [35]. While isolated digital twins might have a significant value-add to the process owners, fostering the full potential requires connecting and integrating all the digital twins.…”

Section: Integrating the Domains To A Hierarchical Conceptmentioning

confidence: 99%

“…This procedure resulted in a collection of elements of digital twins as shown in Figure 4. In [35], this collection is referred to as the "family of twins", together making up a digital twin concept that is derived from the business decision problems that need to be solved.…”

Section: Theoretical and Practical Implicationsmentioning

confidence: 99%

Digital Twin-Enabled Decision Support Services in Industrial Ecosystems

Meierhofer

Schweiger

et al. 2021

Applied Sciences

Self Cite

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The goal of this paper is to further elaborate a new concept for value creation by decision support services in industrial service ecosystems using digital twins and to apply it to an extended case study. The aim of the original model was to design and integrate an architecture of digital twins derived from business needs that leveraged the potential of the synergies in the ecosystem. The conceptual framework presented in this paper extends the semantic ontology model for integrating the digital twins. For the original model, technical modeling approaches were developed and integrated into an ecosystem perspective based on a modeling of the ecosystem and the actors’ decision jobs. In a service ecosystem comprising several enterprises and a multitude of actors, decision making is based on the interlinkage of the digital twins of the equipment and the processes, which is achieved by the semantic ontology model further elaborated in this paper. The implementation of the digital twin architecture is shown in the example of a manufacturing SME (small and medium-sized enterprise) case that was introduced in. The mixed semantic modeling and model-based systems engineering for this implementation is discussed in further detail in this paper. The findings of this detailed study provide a theoretical concept for implementing digital twins on the level of service ecosystems and integrating digital twins based on a unified ontology. This provides a practical blueprint to companies for developing digital twin based services in their own operations and beyond in their ecosystem.

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The Digital Twin as a Service Enabler: From the Service Ecosystem to the Simulation Model

Cited by 27 publications

References 25 publications

Selling the Value of Complex Data-Based Solution for Industrial Customers

Selling the Value of Complex Data-Based Solution for Industrial Customers

A Survey on AI-Driven Digital Twins in Industry 4.0: Smart Manufacturing and Advanced Robotics

Digital Twin-Enabled Decision Support Services in Industrial Ecosystems

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