product development

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.

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 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.

The increasing complexity forces industrial companies to look for new strategies for a future-proof product development. One approach to this is agile approaches in product development in combination with additive manufacturing processes. Physical product increments can thus be produced during sprints and analyzed and improved directly with customers. This improves the product understanding of the development team and customers. The benefits are shorter development times, better customer orientation of the products and a lower project risk.