{"id":103419,"date":"2024-02-20T13:33:09","date_gmt":"2024-02-20T12:33:09","guid":{"rendered":"https:\/\/industry-science.com\/?page_id=103419"},"modified":"2024-05-13T19:51:26","modified_gmt":"2024-05-13T17:51:26","slug":"product-development","status":"publish","type":"page","link":"https:\/\/industry-science.com\/en\/functions\/product-development\/","title":{"rendered":"Product Development"},"content":{"rendered":"\n\n\n
\n
\n \n
\n \n \n \n \"Ideating\n <\/picture>\n <\/div>\n
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Ideating Ethical AI Business Models<\/h4>\n
A dual card approach for the ethical development of AI business models<\/div>
Marie-Christin Barton<\/a> \n \"ORCID<\/a>, Lisa Skrzyppek<\/a>, Kathrin Nauth<\/a> \n \"ORCID<\/a>, Jens P\u00f6ppelbu\u00df<\/a> \n \"ORCID<\/a>, J\u00fcrgen Mazarov<\/a> \n \"ORCID<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
\n 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. <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 42 | Edition 1 | Pages 40-49 | DOI 10.30844\/I4SE.26.1.38<\/a><\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n
\n \n
\n \n \n \n \"AI-Based\n <\/picture>\n <\/div>\n
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AI-Based Recommender Systems in Product Development<\/h4>\n
A framework for knowledge discovery from multimodal data in industrial applications<\/div>
Sebastian Kreuter<\/a> \n \"ORCID<\/a>, Philipp Besinger<\/a>, Alexander Lichtenberg<\/a>, Fazel Ansari<\/a> \n \"ORCID<\/a>, Wilfried Sihn<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
\n 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. <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 41 | 2025 | Edition 5 | Pages 94-101 | DOI 10.30844\/I4SE.25.5.94<\/a><\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n
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\n \n \n \n \"Strategic\n <\/picture>\n <\/div>\n
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Strategic Product Planning Model<\/h4>\n
Digital twins for circular products and production processes<\/div>
Iris Gr\u00e4\u00dfler<\/a> \n \"ORCID<\/a>, Sven Rarbach<\/a>, Benedikt Grewe<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
\n 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. <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 41 | Edition 3 | Pages 24-31 | DOI 10.30844\/I4SE.25.3.24<\/a><\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n
\n \n
\n \n \n \n \"Data\n <\/picture>\n <\/div>\n
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Data Quality in the Engineering of Circular Products<\/h4>\n
Decision support for circular value creation through data ecosystems<\/div>
Iris Gr\u00e4\u00dfler<\/a> \n \"ORCID<\/a>, Sven Rarbach<\/a>, Jens Pottebaum<\/a> \n \"ORCID<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
\n 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. <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 41 | 2025 | Edition 2 | Pages 12-19 | DOI 10.30844\/I4SE.25.2.12<\/a><\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n
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\n \n \n \n \"Hybrid\n <\/picture>\n <\/div>\n
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Hybrid Decision Support in Product Creation<\/h4>\n
Improving performance with data science and artificial intelligence<\/div>
Iris Gr\u00e4\u00dfler<\/a> \n \"ORCID<\/a>, Jens Pottebaum<\/a> \n \"ORCID<\/a>, Peter Nyhuis<\/a> \n \"ORCID<\/a>, Rainer Stark<\/a> \n \"ORCID<\/a>, Klaus-Dieter Thoben<\/a> \n \"ORCID<\/a>, Petra Wiederkehr<\/a> \n \"ORCID<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
\n 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. <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 41 | Edition 1 | Pages 18-25 | DOI 10.30844\/I4SE.25.1.18<\/a><\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n
\n \n
\n \n \n \n \"Process\n <\/picture>\n <\/div>\n
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\t\t

Process Reference Model (PRM) for AI Development in Vehicles<\/h4>\n
Practical guide to the development of AI functionalities in the automotive industry<\/div>
Sebastian Grundstein<\/a> \n \"ORCID<\/a>, Bernhard Burger<\/a>, Andreas Aichele<\/a> \n \"ORCID<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
\n
\n\t\t\t \n\t\t\t <\/div>Artificial intelligence is increasingly being integrated into vehicles, but conventional product development processes often do not fully capture the specific requirements of AI projects. In order to meet these requirements, a process reference model (PRM) has been developed specifically for the development of AI functionalities in the automotive industry. This model is intended to support companies in adapting their conventional software development processes more easily to the special features of AI projects. <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 40 | 2024 | Edition 6 | Pages 96-101<\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n
\n \n
\n \n \n \n \"Circularity\n <\/picture>\n <\/div>\n
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Circularity Navigator<\/h4>\n
Digital decision support for anchoring design for circularity in product development<\/div>
Anina Kusch<\/a> \n \"ORCID<\/a>, Annika Pruhs<\/a> \n \"ORCID<\/a>, J\u00f6rg Woidasky<\/a> \n \"ORCID<\/a>, Jonas Brinker<\/a> \n \"ORCID<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
\n 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 \u2013 especially if it can also be used as a source of inspiration and orientation aid in the innovation phase. <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 40 | 2024 | Edition 1 | Pages 6-13 | DOI 10.30844\/I4SE.24.1.6<\/a><\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n
\n \n
\n \n \n \n \"Makigami\n <\/picture>\n <\/div>\n
\n \t \n
\t\t

Makigami in the Product Development Process<\/h4>\n
Using a lean methodology to integrate sustainable and circular product design<\/div>
Annika Pruhs<\/a> \n \"ORCID<\/a>, Anina Kusch<\/a> \n \"ORCID<\/a>, Frank Bertagnolli<\/a> \n \"ORCID<\/a>, Tobias Viere<\/a>, J\u00f6rg Woidasky<\/a> \n \"ORCID<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
\n 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 \u201cDesign for Circularity\u201d concept. The chosen approach facilitates the identification of the application points of circular design decisions and the implementation of validated circular economy principles. <\/div>\n <\/div>\n
Industrie 4.0 Management<\/strong> | Volume 39 | 2023 | Edition 6 | Pages 55-60 | DOI 10.30844\/I4SE.23.1.64<\/a><\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n
\n \n
\n \n \n \n \"Digital\n <\/picture>\n <\/div>\n
\n \t \n
\t\t

Digital Twinning in Product Development<\/h4>\n
Development and use of experimental digital twins<\/div>
Heiko Matheis<\/a> \n \"ORCID<\/a>, Guido Grau<\/a>, Florian Mews<\/a>, Lukas Sch\u00fcller<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
\n 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%. <\/div>\n <\/div>\n
Industrie 4.0 Management<\/strong> | Volume 39 | 2023 | Edition 5 | Pages 37-41 | DOI 10.30844\/IM_23-5_37-41<\/a><\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n
\n \n
\n \n \n \n \"Integration\n <\/picture>\n <\/div>\n
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Integration of Agile Product Development and Ecodesign at SME<\/h4>\n
L\u00f6sungsstrategien f\u00fcr umweltvertr\u00e4gliche Produkte und Produktionsprozesse im Kontext von Kleinunternehmen<\/div>
Manuel L\u00f6wer<\/a>, Tim Katzwinkel<\/a>, Dominik Limbach<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
\n 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. <\/div>\n <\/div>\n
Industrie 4.0 Management<\/strong> | Volume 39 | 2023 | Edition 2 | Pages 46-50 | DOI 10.30844\/IM_23-2_46-50<\/a><\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n<\/div>\n\n
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