Product Development

AI opens up entirely new forms of value creation, but most business model tools have not kept pace. They overlook both the strategic potential that AI holds and the ethical challenges that it introduces. This study introduces a dual-card toolkit that helps interdisciplinary teams design AI-enabled business models with built-in ethical reflection. The key insight: to harness AI responsibly, we must rethink how we innovate, starting from the business model itself.

The engineer-to-order (ETO) production approach is gaining relevance in response to increasing demand for individualized products and small batch sizes. However, ETO inherently reduces the economies of scale typically achieved in series production, as each order requires tailored engineering and production steps. This loss of efficiency can be mitigated through demand-driven and context-aware information provision throughout the product development process. A recommendation system based on semantic artificial intelligence (AI) and machine learning can support this by i) analyzing historical data and prior knowledge, for example drawings or a bill of materials from previous projects, and ii) making automated suggestions, like reusing existing designs or proposing design alternatives, thus compensating for the aforementioned effects.

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.

Technical systems are characterized by increasing interdisciplinarity, complexity and networking. A product and its corresponding production systems require interdisciplinary multi-objective optimization. Sustainability and recyclability demands increase said complexity. The efficiency of previously established engineering methods is reaching its limits, which can only be overcome by systematic integration of extreme data. The aim of "hybrid decision support" is as follows: Data science and artificial intelligence should be used to supplement human capabilities in conjunction with existing heuristics, methods, modeling and simulation to increase the efficiency of product creation.

Products cannot be included early enough in the planning of a circular manufacturing process. However, because early incorporation brings additional complexity into play, product decisions are often set aside for later manufacturing stages. At this point, a decision-making tool that systematically reduces complexity and generally simplifies the process is therefore of great value – especially if it can also be used as a source of inspiration and orientation aid in the innovation phase.

In order to realize future improvements in circular product properties such as lifespan extension, continued use or high-quality recycling, industrial product development and design processes must take the entire ecological and economic life cycle of products into account. This article uses a company example to explain how such processes can be captured and analyzed using the Makigami method to support a comprehensive “Design for Circularity” concept. The chosen approach facilitates the identification of the application points of circular design decisions and the implementation of validated circular economy principles.

The development of textile products is associated with high material, time, personnel and cost expenditure. The paper describes the digital twinning for materials and processes and their application in a digital product development process, which can accelerate the ramp-up phase and thus reduce development costs by up to 60%.

The political and social request for environmentally compatible products is putting companies under increasing pressure. Small and medium-sized companies (SME) in particular have to quickly find or develop solutions to these demands. This paper presents a methodological approach that combines the proven strategies of agile development with the specific activities of so-called ecodesign. The methodology is first discussed theoretically and then experimentally evaluated and discussed by means of a case study in a real company context.