Additive Manufacturing Value Chain

Development of an SME-specific value chain of additively manufactured final metal parts

Tim Niklas Mai, Martin Brylowski, Ayman Nagi, Wolfgang Kersten ORCID Icon
JournalIndustrie 4.0 Management
Issue Volume 38, 2022, Edition 3, Pages 25-30
Open Accesshttps://doi.org/10.30844/I40M_22-3_25-30
Bibliography Share Cite Download

Abstract

Additive manufacturing processes are becoming increasingly important in industry and enable the cost-e ective production of complex components in small quantities. Small and medium-sized enterprises (SMEs) in particular can bene t from the high customization potential enabling the development of new business models. However, the widespread use of additive processes faces high production costs and technological challenges. Meanwhile, scienti c research focuses on the optimization of individual process steps of additive manufacturing and does not o er su cient support for SMEs. Therefore, this paper deals with the development of a cross-process value chain of additive manufacturing for SMEs. Based on a systematic analysis of scienti c literature, relevant additive manufacturing processes were investigated, and a cross-process value chain was derived. The results were veri ed by expert interviews and central research and development requirements were extracted.

Keywords

,

Bibliography

[1] Almeida, H.; Vasco, J.: Expectations of Additive Manufacturing for the Decade 2020–2030. In: H. A. Almeida und J. C. Vasco (Hrsg): Progress in Digital and Physical Manufacturing. Cham 2003.
[2] Wildemann, H.: 3D-Metalldruck. Wertschöpfungskette – Teileauswahl – Industrialisierung – Wirtschaftlichkeit – Einführungsstrategien, 1. Auflage. München 2019.
[3] Gebhardt, A.: Additive Fertigungsverfahren. Additive Manufacturing und 3D-Drucken für Prototyping – Tooling – Produktion, 5 Auflage. München 2016.
[4] Grund, M.: Implementierung von schichtadditiven Fertigungsverfahren – mit Fallbeispielen aus der Luftfahrtindustrie und Medizintechnik. Berlin Heidelberg 2015.
[5] Witt, E.; Anton, C.: Additive Fertigung. Entwicklungen, Möglichkeiten und Herausforderungen. Halle (Saale) 2020.
[6] Auth, C.; Arndt, A.; Anderl, R.: Development of a Concept for a Holistic Knowledge-Based Additive Manufacturing over the Entire Lifecycle. In: P. Chiabert, A. Bouras, F. Noël und J. Ríos (Hrsg): Product Lifecycle Management to Support Industry 4.0. Cham 2018.
[7] Kunovjanek, M.; Reiner, G.: How will the Diffusion of Additive Manufacturing Impact the Raw Material Supply Chain Process? In: International Journal of Production Research 58 (2020) 5, S. 1540–1554.
[8] Polenz, S.; Oettel, M.; Lopez, E.; Leyens, C.: Hybride Prozesskette aus Gießen und additiver Fertigung. In: Leightweight Design 12 (2019) 1, S. 58–63.
[9] Durach, C. F.; Kurpjuweit, S.; Wagner, S. M.: The Impact of Additive Manufacturing on Supply Chains. In: International Journal of Physical Distribution & Logistics Management 47 (2017) 10, S. 954–971.
[10] Schmidt, M.; Merklein, M.; Bourell, D.; Dimitrov, D.; Hausotte, T.; Wegener, K.; Overmeyer, L.; Vollertsen, F.; Levy, G. N.: Laser Based Additive Manufacturing in Industry and Academia. In: CIRP Annals 66 (2017) 2, S. 561–583.
[11] Fink, Arlene (2014): Conducting research literature reviews – From the Internet to Paper, 4. Auflage. Los Angeles 2014.
[12] Tranfield, D.; Denyer, D.; Smart, P.: Towards a Methodology for Developing Evidence-Informed Management Knowledge by Means of Systematic Review. In: British Journal of Management 14 (2003) 3, S. 207–222.
[13] Gläser, J.; Laudel, G.: Experteninterviews und qualitative Inhaltsanalyse als Instrument rekonstruierender Untersuchungen, 4. Auflage. Wiesbaden 2010.
[14] Porter, M. E.: Wettbewerbsvorteile (Competitive advantage). Spitzenleistungen erreichen und behaupten, 1. Auflage. Frankfurt a. M (1989).
[15] Töpfer, A.: Betriebswirtschaftslehre. Anwedungs- und prozessorientierte Grundlagen, 2. Auflage. Berlin Heidelberg 2007.
[16] Oehlrich, M.: Betriebswirtschaftslehre. Eine Einführung am Businessplan-Prozess, 2. Auflage. München 2010.
[17] Porter, M. E.: Wettbewerbsstrategie (Competitive strategy). Methoden zur Analyse von Branchen und Konkurrenten, 10. Auflage. Frankfurt a. M. 1999.
[18] Thornton, A.; Saad, J.; Clayton, J.: Measuring the critical attributes of AM powders. In: Metal Powder Report 74 (2019) 6, S. 314–319.
[19] Dawes, J.; Bowerman, R.; Trepleton, R.: Introduction to the Additive Manufacturing Powder Metallurgy Supply Chain. In: Johnson Matthey Technology Review 59 (2015) 3, S. 243–256.
[20] Jimo, A.; Braziotis, C.; Rogers, H.; Pawar, K.: Traditional vs Additive Manufacturing Supply Chain Configurations: A Comparative Case Study. In: Procedia Manufacturing 39 (2019) 1, S. 765–774
[21] Alkahtani, M.; Abidi, M. H.: Supply Chain in The Era of Additive Manufacturing. In: Garza-Reyes, A. (Hrsg.): International Conference on Industrial Engineering and Operations management. Pilsen 2019.
[22] Verboeket, V.; Krikke, H.: The Disruptive Impact of Additive Manufacturing on Supply Chains: A Literature Study, Conceptual Framework and Research Agenda. In: Computers in Industry 111 (2019) 1, S. 91–107.
[23] Munsch, M.; Schmidt-Lehr, M.; Wycisk, E.: Additive Manufacturing – New Metal Technologies, 6. Auflage. Hamburg 2020.
[24] Yusuf, S. M.; Cutler, S.; Gao, N.: Review: The Impact of Metal Additive Manufacturing on the Aerospace Industry. In: Metals 9 (2019) 12, S. 1286.
[25] Denkena, B.; Dittrich, M.-A.; Henning, S.; Lindecke, P.: Investigations on a Standardized Process Chain and Support Structure related Rework Procedures of SLM Manufactured Components. In: 18th Machining Innovations Conference for Aerospace Industry (2018), S. 50–57.

Your downloads

You might also be interested in

Generative AI in Organizational Knowledge Management

Generative AI in Organizational Knowledge Management

AI literacy as a prerequisite for augmentation
Uta Wilkens ORCID Icon, Valentin Langholf ORCID Icon, Niklas Obermann ORCID Icon
Generative Artificial Intelligence (GenAI) offers new opportunities for organizational knowledge management, particularly when it comes to learning processes at the interface between explicit, firm-specific, and tacit knowledge. Its use is therefore of particular interest for application areas such as industrial maintenance. Based on a mechanical engineering case study, this article demonstrates that augmenting both processes and employees with GenAI requires AI literacy combined with professional skills.
Industry 4.0 Science | Volume 42 | 2026 | Edition 5 | Pages 118-126 | DOI 10.30844/I4SE.26.5.14
Foundation Models in Industrial Robotics

Foundation Models in Industrial Robotics

Requirements for AI-supported assistance in production and logistics
Bernd Kuhlenkötter ORCID Icon, Daniel Syniawa ORCID Icon
Foundation models are increasingly changing the way robots are used in industry. Instead of writing complex programs line by line, programmers will soon be able to collaborate more closely with AI systems, describe tasks, and review generated solutions. This shifts their role from purely generating code to conceptual, supervisory, and validation activities. This article highlights the new possibilities that large AI models create for robot programming and the changes they entail for work and required skills in industrial practice.
Industry 4.0 Science | Volume 42 | 2026 | Edition 5 | Pages 16-23 | DOI 10.30844/I4SE.26.5.2
AI-Driven Organization as a New Work Paradigm

AI-Driven Organization as a New Work Paradigm

Implications for individual and organizational change
Katharina Hölzle ORCID Icon, Leonie Krauch, Wolfgang Beinhauer ORCID Icon, Carsten Schmidt, Josephine Hofmann ORCID Icon
Is it sufficient to train employees in the use of AI tools, or does the AI organization require an entirely new set of competencies? This paper introduces a digital enablement model comprising three competency dimensions and demonstrates, through an upskilling program implemented at the Fraunhofer Institute for Industrial Engineering IAO, how organizations can sustainably bridge the AI adoption gap.
Industry 4.0 Science | Volume 42 | 2026 | Edition 5 | Pages 94-100 | DOI 10.30844/I4SE.26.5.11
Designing Effective AI Certification

Designing Effective AI Certification

Insights from established certification domains for the standardization of human-centered AI
Katharina Morsch ORCID Icon
Certification for human-centered AI is becoming increasingly important—as a source of competitive differentiation, a response to regulatory expectations, and a mechanism for fostering human-centered work environments. But how can organizations determine whether the underlying requirements are truly embedded in practice? Drawing on expert interviews from established certification domains, this paper shows that the decisive question is answered not during the audit itself, but in the period between audit cycles. Ultimately, it is not the certificate that matters, but the commitment of organizational leadership and the organization as a whole to engage seriously in the certification process and its continuous implementation.
Industry 4.0 Science | Volume 42 | 2026 | Edition 5 | Pages 86-92 | DOI 10.30844/I4SE.26.4.10
Data-Driven Supplier Selection in Industry 4.0

Data-Driven Supplier Selection in Industry 4.0

Towards an industrial platform for selection and configuration
Purushothaman Ganesh ORCID Icon, Baris E. Albayrak ORCID Icon, Jörg Franke ORCID Icon, Till Sindel ORCID Icon, Jens Fürst, Sebastian Lang ORCID Icon
Industrial supply chains must repeatedly reconfigure sourcing strategies in response to disruptions, yet supplier capability information remains heterogeneous and difficult to operationalize. Existing research addresses supplier selection, simulation, and interoperability standards separately, treating discovery, evaluation, and optimization as disconnected steps requiring manual data transformation. This paper presents a framework for a data-driven industrial platform integrating three components: Asset Administration Shell-based supplier profiles structured by an Actor-Service ontology for semantic discovery, automatic discrete-event simulation model generation from layout data for performance evaluation, and KPI-driven configuration using optimization algorithms. The main contributions are an end-to-end interoperable workflow, a two-tier supplier profile concept separating semantic descriptions from simulation parameters, and a standardized KPI interface maintaining consistency ...
Industry 4.0 Science | Volume 42 | 2026 | Edition 4 | Pages 50-61 | DOI 10.30844/I4SE.26.4.6
Open Source as Enabler for Smart Data Ecosystems

Open Source as Enabler for Smart Data Ecosystems

How collaboration shapes sovereignty and interoperability across industrial applications
Anna Maria Schleimer ORCID Icon, Julia Pampus ORCID Icon
From automotive supply chains to smart factories, data ecosystems promise seamless collaboration across company boundaries. But how can industries build the necessary infrastructure without creating new dependencies? This article explores how open-source software can serve as a foundation and impactful tool for sovereign, interoperable technologies and standards. Yet, open-source software is not a silver bullet; rather, it poses challenges for digital sovereignty in burgeoning data ecosystems.
Industry 4.0 Science | Volume 42 | 2026 | Edition 4 | Pages 6-13 | DOI 10.30844/I4SE.26.4.1
Christoph Brocks