With the advancing creation and networking of digital twins, the potential of the Industrial Internet of Things (IIoT) is becoming ever more apparent. Beyond their product-related, technical focus, digital twins serve to safeguard, control and optimize the processes, functions and interactions associated with them. This makes them central cogs within digital value chains. In this blog post, we shall be taking an introductory look at this development and the importance of digital twins for B2B integration.
From development tool to an important part of business
Digital twins come from the CAx world, where they are used to simulate technical objects in development and construction processes. They are still used there today to virtually put the form, function and behavior of components through their paces.
From there, digital twins quickly found their way into digital factories, where they align the performance and capacity of production resources to production processes. This optimizes time, costs and quality. In the corporate world, digital twins mainly support holistic approaches to developing products and production systems. This may mean virtual commissioning, or running a hardware-in-the-loop (HiL) simulation to test technical processes in a hybrid, real/virtual environment.
With the advent of industry 4.0, digital twins started evolving away from just being used to test a design in the planning phase. These days, digital twins are digital images of real objects over their full life cycle.
Networking digital twins in an Asset Administration Shell (AAS)
An important, emerging use of digital twins is the concept of the Asset Administration Shell (AAS). Developed by Plattform Industrie 4.0, the Industrial Digital Twin Association (IDTA) developed it into usable OSS components. This involves provisioning machine-readable data on various aspects of digital twins via standardized sub models and APIs. In recent years, the AAS has increasingly become the technology at the basis of representing and incorporating real instances and virtual (type) images of technical items in numerous applications. These serve as references for platforms and gateways alike. In IIoT, digital twins are increasingly being networked in between several business partners in order to map the product instance as it is really being used by several different actors.
This gives us a better view of the actual use and impact of the object in question, especially if interacting with other digital twins. The data and information we gain over the product lifecycle gives rise to new ways of running a process and new business models. These include product-specific digital services to align and optimize behavior and capabilities, as well as specific improvements. Digital twins are thus becoming a cog right at the center of data-driven value creation in IIoT, changing the entire logistics of data and information.
Digital twins for smart services in after sales
We can already see the influence of digital twins on creating value creation in digital ecosystems, as well as internal (business) applications. This should not be underestimated. These areas are dynamically evolving towards end-to-end architectures and infrastructures. Don’t forget to make sure that your inter-company data exchange and B2B integrations are up to the job of orchestrating and integrating the data from digital twins, for example for smart services or into higher-level control processes.
SEEBURGER is participating in the SEAMLESS project
As part of the SEAMLESS project from the Federal Ministry of Education and Research, SEEBURGER is joining with users, technology providers and research partners to research new mechanisms for the cross-company and cross-level networking of simulation-based digital twins and their use in after-sales services. The aim is to enable machine and equipment manufacturers to establish their own, cooperative data-driven services and to initiate new digital value creation processes.
One of the use cases, for example, looks at how to develop services to precisely adjust production capacity and resources to what is needed. The digital twin for the production facilities as a whole is expanded by digital twins for individual stations. Using behavioral simulation, these can deliver precise values for changeover and cycle times. In turn, the simulation can use something called a virtual commissioning function to test various configurations itself. It does this by integrating functional mock-up units (FMU), essentially external, standardized simulation components, into its analysis. The SEAMLESS platform integrates the simulation systems, including the FMUs. Both areas deliver data to the SEAMLESS platform’s data hub, reporting aggregated KPIs through customized data pipelines. These are, in turn, made available to a higher-level value stream mapping system.
The results of this monitoring and analysis can then be used by the engineers who design the production facilities, by the workers on the shop floor, and the production scheduler in their own contexts.
Another use case is optimizing operations. The digital twins use simulation environments and AI models to learn procedures and to apply these in other environments. One example would be collecting data on how machines behave in their real environment, and using this to optimize the machine control tools or to create machine-learning based individual machine profiles, as used in CAM planning.
The project also explores the use of digital twins in augmented reality (AR) applications. The digital twins aggregate the services running on the platform and make them usable for service deployment.
Focus on integration
The SEAMLESS project places special requirements on the mechanisms used to efficiently and reliably provide, supply and handle these types of interaction between the partners. SEEBURGER’s focus is primarily on cross-company integration. In addition to looking at data exchange between digital twins via individual services in cloud and edge environments, we have also been further developing data exchange formats and investigating platform-based business models.
The project is due to end at the beginning of 2023. By then the consortium wish to have taken their current platform and function prototypes to a higher level of maturity, as well as moving them into a neutral demonstrator which can be used even once the project has ended. This will also support a partnership-based implementation of the project results.
Business Integration in IIoT
 This stands for Simulation-supported Assistance-system based Engineering and Maintenance services for Lean After Sales Services. cf. https://seamless.fzi.de/wordpress/?page_id=75 (accessed 27.09.2022).
 c.f. https://www.degruyter.com/document/doi/10.1515/zwf-2022-1009/pdf (accessed 27.09.2022).
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Written by: Viktor SchubertViktor Schubert is Product Manager for IoT/Industry 4.0 at SEEBURGER and has been working in the field of data and process management (B2B/PLM/MES) for over 10 years. He is active in the standards committee for mechanical engineering NAM (Normenausschuss Maschinenbau) of the German Institute for Standardization DIN (Deutsches Institut für Normung) as well as in task forces of the German Engineering Federation VDMA (Verband Deutscher Maschinen- und Anlagenbau) and is involved in the standardization and development of these areas in the "Industry 4.0" environment.