sustainability

Sustainability-enhancing transformation processes are necessary in all sectors if we are to remain within planetary boundaries. This also applies to the industrial sector as a significant emitter of greenhouse gases. Employees need new competencies to master this complex task of industrial transformation. These range from CO2 equivalents accounting to the development and evaluation of transformation scenarios, including technical measures. The learning module developed here addresses these competency requirements and uses the example of the ETA factory to show how a competency-oriented learning module for industrial transformation can be structured. It essentially comprises four phases: data collection and CO2 equivalents accounting, cause analysis, development of measures and evaluation of measures.

In today’s manufacturing industry, digital twins are a key enabler for optimizing production processes and efficient resource use. However, creating digital twins is often associated with high or difficult-to-estimate costs and typically requires unknown characteristic values, such as material parameters, making practical implementation challenging. With RAPIDZ, we present a tool for creating and using digital twins that overcomes these barriers through its modular structure. The virtual modeling of physical systems enables comprehensive analysis and real-time forecasting of material flows, energy consumption and machine performance. The use of RAPIDZ increases production line efficiency, enhances flexibility and response time, and enables proactive maintenance to minimize downtime.

Strategic Product Planning must adapt to current challenges such as circular economy, digital business models and interdisciplinarity. Established process models, for example, can only be applied to Product-Service Systems to a limited extent. This article presents a new SPP model developed through an analysis of 230 existing approaches and enhanced by the integration of digital twins, enabling continuous feedback throughout the entire product life cycle. This allows product monitoring and dynamic adjustments to the SPP. The model adopts an agile, iterative framework consisting of five cyclical key activities, guided by five control points aligned with increasing levels of maturity. By factoring in circularity from the outset, the model promotes resource-efficient products and production processes. Its emphasis on flexibility, information circularity and sustainability ensures future value and adaptability across industries of the proposed SPP model.

Decisions affecting the sustainability of products are made during the engineering process. As product engineering progresses, statements on sustainability can also be substantiated. Initially, only estimates based on related products and processes are possible, but later, operational and machine data can be used. When metrics are used for key figures, the traceability of the data should be ensured. For this purpose, relevant data quality criteria and indicators are selected and analyzed for correlations. Data availability can be increased by relying on partners within data ecosystems for product engineering. Data spaces such as Gaia-X, Catena-X and Manufacturing-X form a basis for this ambition.

Current developments in digitalization and data economy, especially multilateral data sharing platforms, offer the potential to accelerate the implementation of circular economy practices in the manufacturing industry. This article systematically examines the extent to which digitalization could serve as a catalyst for circular economy in the procurement of such companies. As a basis for the following research, eight experts from five leading global manufacturers and suppliers in the automotive and aviation industries were interviewed. This article demonstrates practical hypotheses for the sustainable design of supply chains and proposes two specific use cases for circular economy practices that can proactively counteract the use of resources.