The mobility sector is the world’s second-largest producer of energy-related CO₂ emissions, and it is facing a global resource shortage. The demand for circular products, the use of secondary materials in future vehicles, and the need for sustainable business models in the mobility sector is increasing. However, a transparent and end-to-end data exchange throughout the entire value network is missing, which is hindering an efficient circular economy. Relevant information on the vehicle, its components, and materials at the end of the product life cycle are often missing. In this context, this paper presents a decision support system based on Digital Twin data for a circular economy solution as a software application. It was developed within the German research project Catena-X following an integrated approach of user-centered design, the V-model, and within the Scaled Agile Framework. By combining these methodological approaches, customer-oriented solutions were developed and continuously improved at each stage of development to shorten the time-to-market. Catena-X is based on Gaia-X principles. In Gaia-X, necessary core services are developed, and contraction negotiation for data exchange and usage policies is enabled and implemented. The decision support system provides important information about the exact composition and condition of the vehicle, its components, and its materials. Thus, it helps to improve efficiency, sustainability, and the implementation of the circular economy. The decision support system was tested and validated with a use case that provided Digital Twin data on the end-of-life vehicle.

In 2021, sales of electric bicycles in Germany increased compared to the previous year, leading to a 43% share of electric bicycles in the total German bicycle market. The German Bicycle Industry Association (ZIV) expects this growth to continue and that by 2025 every second bicycle sold in Germany will have an electric motor. Therefore, due to the increasing number of sales, both the built-in resources and the possible treatments after the use phase need to be considered regarding sustainable market development. In this context, remanufacturing is one promising end-of-life strategy that enables a new life cycle of products and components, thus reducing the consumption of natural resources and minimizing waste production. The used products are restored to their original conditions by disassembling, cleaning, sorting, reworking, and reassembling. Disassembly is a decisive process step as it creates the prerequisites for all further steps in the process chain and significantly determines the economic feasibility of a remanufacturing process. Therefore, this paper aims to evaluate the ease of disassembly of electric bicycle motors and thus determine their suitability for remanufacturing. To evaluate the disassemblability, the ease of Disassembly Metric (eDiM), a quantitative assessment method for evaluating disassembly effort, was adapted to the needs of the electric bicycle motors. The method was finally applied to five electric bicycle motors from established manufacturers and assessed their ease of disassembly. The results show that the electric bicycle motors can be disassembled without significant damage and classified as suitable for remanufacturing.

Remanufacturing plays a key role in the circular economy by reducing material consumption and extending product life cycles. However, a major challenge in remanufacturing is accurately forecasting the availability of cores, particularly regarding their quantity, timing, and condition. Although machine learning (ML) offers promising approaches for addressing this challenge, there is limited clarity on which influencing factors are most critical and which ML approaches are best suited to remanufacturing-specific forecasting tasks. This study addresses this gap through a mixed-method approach combining expert interviews with two systematic literature reviews. The interviews with professionals from remanufacturing companies identified key influencing factors affecting product returns, which were structured into an adapted Ishikawa diagram. In parallel, the literature reviews analyzed 125 peer-reviewed publications on ML-based forecasting in related domains—specifically, spare parts logistics and manufacturing quality prediction. The review categorized data sources into real-world, simulated, and benchmark datasets and examined commonly applied ML models, including traditional methods and deep learning architectures. The findings highlight transferable methodologies and critical gaps, particularly a lack of remanufacturing-specific datasets and integrated models. This study contributes a structured overview of ML forecasting in remanufacturing and outlines future research directions for enhancing predictive accuracy and practical applicability.

With an expected growth of global waste to 3.40 billion tonnes by 2050 and a circularity today of only 8.6% of the world, the earth’s sustainable resources are being exploited beyond their regeneration capacity. Hence, it is necessary to step away from a take – make – dispose principal and transform from a linear towards a circular economy to close product cycles to optimize resource consumption and reduce waste. Product-Service-Systems (PSSs), based on multiple product life cycles combined with remanufacturing, offer a solution to close product cycles. In such PSS, the responsibility for returning, remanufacturing, and repairing used products remains with the Original Equipment Manufacturer (OEM) and increases its need in (reverse) supply chain activities. Essential factors for (reverse) supply chains are, e.g., determining the distribution network, the location of recovery facilities, the geographical dispersion of the customers, and the information flows between the different stakeholders. In this context, this work proposes a multi-method simulation model to support practitioners in determining the optimal infrastructure for storing, remanufacturing, and repairing the used products regarding economic and ecological target criteria. The applicability of the proposed approach is illustrated through a case study of a white goods manufacturing company. This case study highlights the importance of determining the optimal infrastructure in a (reverse) supply chain in PSS business models.

In 2021, sales of electric bicycles in Germany increased compared to the previous year, leading to a 43% share of electric bicycles in the total bicycle market. The German Bicycle Industry Association (ZIV) expects this growth to continue and that by 2025 every second bicycle sold in Germany will have an electric motor. Therefore, due to the increasing popularity, both the built-in resources and the possible treatments after the use phase need to be considered regarding sustainable market development. In this context, remanufacturing is one promising end-of-life strategy that enables a new life cycle of products and components, thus reducing the consumption of natural resources and minimizing waste production. The used products are restored to their original conditions by disassembling, cleaning, sorting, reconditioning, and reassembling. Thereby, disassembly is a decisive process step as it creates the prerequisites for all further steps in the process chain and significantly determines the economic feasibility of a remanufacturing process. Therefore, this paper aims to evaluate the ease of disassembly of electric bicycle motors and thus determine their suitability for remanufacturing. To evaluate the disassemblability, the ease of Disassembly Metric (eDiM), a quantitative assessment method for evaluating disassembly effort, was adapted to the needs of the electric bicycle motors. The method was finally applied to five electric bicycle motors from established manufacturers and assessed their ease of disassembly. The results showed that the electric bicycle motors can be disassembled without significant damage and classified as suitable for remanufacturing based on their disassembling ability.