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
Article, Bibliographie
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.
| Item Type: | Article |
|---|---|
| Erschienen: | 2022 |
| Creators: | Hoffmann, Felix ; Lang, Enno ; Metternich, Joachim |
| Type of entry: | Bibliographie |
| Title: | Development of a Framework for the Holistic Generation of ML-Based Business Models in Manufacturing |
| Language: | English |
| Date: | 2022 |
| Publisher: | Elsevier B.V. |
| Journal or Publication Title: | Procedia CIRP |
| Volume of the journal: | 107 |
| DOI: | 10.1016/j.procir.2022.04.035 |
| 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. |
| Uncontrolled Keywords: | Artificial Intelligence, business model, development model, Machine tools, manufacturing, production |
| Divisions: | 16 Department of Mechanical Engineering 16 Department of Mechanical Engineering > Institute of Production Technology and Machine Tools (PTW) 16 Department of Mechanical Engineering > Institute of Production Technology and Machine Tools (PTW) > Management of Industrial Production |
| Date Deposited: | 10 Nov 2022 13:44 |
| Last Modified: | 14 Nov 2022 06:58 |
| PPN: | 501629033 |
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