Hoffmann, Felix ; Lang, Enno ; Metternich, Joachim (2022)
Development of a Framework for the Holistic Generation of ML-Based Business Models in Manufacturing.
In: Procedia CIRP, 107
doi: 10.1016/j.procir.2022.04.035
Artikel, Bibliographie
Kurzbeschreibung (Abstract)
Analyzing data with the help of Machine Learning (ML) promises to raise significant potentials in all relevant target dimensions and different application fields of industrial production. Through the increasing availability of data in the context of digitalization as well as continuously more powerful and cost-effective possibilities for data processing, the amount of economically viable scenarios for implementing ML-based business models (BMs) in production rises. Despite the emerging data-related possibilities, especially small and medium sized enterprises (SMEs) struggle with identifying reasonable use cases for ML in their own company. This can be ascribed to a lack of knowledge about the necessary elements for ML applications' sustainable implementation and operation. Therefore, this paper aims to develop a framework for the holistic generation of ML-based BMs in manufacturing. At first, characteristics as well as general and specific requirements for ML-based BMs in manufacturing are elaborated. Subsequently, a morphology for the systematic development of ML-based BMs is generated using the insights gained. In a concluding step, the application of the developed concept is validated based on a selected use case.
| Typ des Eintrags: | Artikel |
|---|---|
| Erschienen: | 2022 |
| Autor(en): | Hoffmann, Felix ; Lang, Enno ; Metternich, Joachim |
| Art des Eintrags: | Bibliographie |
| Titel: | Development of a Framework for the Holistic Generation of ML-Based Business Models in Manufacturing |
| Sprache: | Englisch |
| Publikationsjahr: | 2022 |
| Verlag: | Elsevier B.V. |
| Titel der Zeitschrift, Zeitung oder Schriftenreihe: | Procedia CIRP |
| Jahrgang/Volume einer Zeitschrift: | 107 |
| DOI: | 10.1016/j.procir.2022.04.035 |
| Kurzbeschreibung (Abstract): | Analyzing data with the help of Machine Learning (ML) promises to raise significant potentials in all relevant target dimensions and different application fields of industrial production. Through the increasing availability of data in the context of digitalization as well as continuously more powerful and cost-effective possibilities for data processing, the amount of economically viable scenarios for implementing ML-based business models (BMs) in production rises. Despite the emerging data-related possibilities, especially small and medium sized enterprises (SMEs) struggle with identifying reasonable use cases for ML in their own company. This can be ascribed to a lack of knowledge about the necessary elements for ML applications' sustainable implementation and operation. Therefore, this paper aims to develop a framework for the holistic generation of ML-based BMs in manufacturing. At first, characteristics as well as general and specific requirements for ML-based BMs in manufacturing are elaborated. Subsequently, a morphology for the systematic development of ML-based BMs is generated using the insights gained. In a concluding step, the application of the developed concept is validated based on a selected use case. |
| Freie Schlagworte: | Artificial Intelligence, business model, development model, Machine tools, manufacturing, production |
| Fachbereich(e)/-gebiet(e): | 16 Fachbereich Maschinenbau 16 Fachbereich Maschinenbau > Institut für Produktionsmanagement und Werkzeugmaschinen (PTW) 16 Fachbereich Maschinenbau > Institut für Produktionsmanagement und Werkzeugmaschinen (PTW) > Management industrieller Produktion |
| Hinterlegungsdatum: | 10 Nov 2022 13:44 |
| Letzte Änderung: | 14 Nov 2022 06:58 |
| PPN: | 501629033 |
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| Suche nach Titel in: | TUfind oder in Google |
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