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On 07.10.2021, the 15th web seminar of the CEresearchNRW network took place as part of the Prosperkolleg event series “Circular October”. Around 40 participants discussed with the speakers Anna Preut and Saban Ünlü how the digital twin can drive the Circular Economy.
Anna Preut (Fraunhofer Institute for Material Flow and Logistics – Department of Environment and Resource Logistics) first discussed the concept of the digital twin in her presentation. Based on the descriptions of Grieves and Vickers, a working definition of the digital twin was developed as part of the Cluster Circular Plastics Economy (CCPE®) project. Thus, the digital twin is “a virtual collection of information that comprehensively describes a specific, planned or existing product.” Other features of the digital twin are that it provides information about a product in a decentralized manner throughout the product lifecycle based “on an information link with the physical product.”
The software and hardware structure of the digital twin can be divided into three superordinate functional areas: 1) data collection and access to data, 2) the data management and the provision of the data as well as 3) the data evaluation and analysis. For example, a user can access the collected data by means of an app. These are provided in a protected infrastructure in data stores and evaluated via web interfaces as well as via analysis modules.
Even though the digital twin has gained significant interest in research over the last decade, there are still very different interpretations of the concept. It remains unclear how exactly products will be mapped in the digital twin. Is this about track and trace location tracking or a much more detailed three-dimensional visualization of the product? The areas of application also remain unclear, such as the type of processes, the number of stakeholders involved, and the product lifecycle phases.
In the second part of the presentation, Ms. Preut addressed the question of how the digital twin can contribute to the recycling of products and materials. In this context, product design, processes and technologies, legal and organizational framework conditions, and information are seen as influencing factors for successful closed-loop management. Such relevant information could support the cycling of products at different points in the project life cycle. For example, they can help with remanufacturing and refurbishment to determine what spare parts are needed, but also where they can be obtained. In addition, relevant information can also show in the procurement of resources what quality and quantities of secondary material can be expected from product returns. However, this relevant information is often missing due to a multitude of heterogeneous information systems, an insufficient exchange of information between the actors or a lack of information collection and storage.
The digital twin can contribute significantly to circularity by collecting and providing the information: one example is adapting product design based on usage data and enabling circular business models, such as sharing systems or predictive maintenance.
However, there are major challenges and unanswered questions when using digital twins in practice. Due to the high number of stakeholders, many different requirements have to be taken into account. Stakeholders need to be brought together to create synergies. In addition, many different data formats and IT systems are used. Economic efficiency and ecological aspects must also not be neglected.
In a second presentation, Saban Ünlü (Founder netTrek GmbH & Co. KG) talked about “how the digital twin connects the real and virtual worlds”. In its definition , the digital twin combines virtual models of real processes, products, services and objects with the real world. Here, the digital twin is composed of a virtual data model of a real object, the analysis of the data, and knowledge as the interface between the real and virtual worlds. There are various ways to obtain data: On the one hand, this can be done fully automatically via sensors & edge computing, in which case IoT hub data is made accessible in order to obtain real-time data on the status and environment in the best case. The machines must be networked in such a way that one is able to obtain status values to which one can react immediately. On the other hand, the structure of the data model can be partially automated, i.e. objects are digitized, such as a package. It scans what is in the package, who is transporting it and when it will arrive. Here, an interaction between human and scanner takes place.
The digital twin is only worth something if we can analyze and process this data in a meaningful way. Bulk data are often large, complex data sets that have little structure. Analysis is possible via Artificial Intelligence (AI), where conditional conditions can be analyzed, but also Machine Learning can be applied to later detect patterns from data and learn multilayered (Deep Learning). Digital twins offer the advantage that analysis results can make statements about the current state as well as future predictions. They can support operational and strategic decisions as well as the development of new business models. The analysis of operational data also offers a possible increase in efficiency.
A look at the practice shows different application examples of the digital twin. As explained by Ms. Preut, according to Mr. Ünlü, they provide data at different stages of the product lifecycle that can be used for design alternatives, increased efficiency, retrieval of availabilities, recycling activities and more. But they are also used in the healthcare sector and in smart buildings, factories, cities and logistics activities. As an example, Mr. Ünlü mentions Lutec-PKS GmbH, a logistics specialist, especially for pallet circulation systems (PKS). The company analyzes customer requirements and designs its own transport solutions using returnable load carriers. After the customer-specific design, the PKS are digitally traceable by labels or RFID chips. This makes it possible to check where the product is at any time, both for the end customer and for the manufacturer. Repair of the multi-way load carriers as well as modifications of the attachment are possible without any problems. At the end of the life cycle, the returnable load carriers are disassembled into the base pallet and attachment and returned to the cycle. By logging beam and rack data, maintenance management is optimized to identify weak points and breakages at an early stage, as are quality assurance and the recycling process. Digital tools can be used here to optimize the entire value chain.