Hoffmann, Felix ; Lang, Enno ; Metternich, Joachim (2023)
Seizing the value of data: Selecting appropriate pricing strategies for data-based services in manufacturing.
In: Procedia CIRP, 120
doi: 10.1016/j.procir.2023.08.029
Artikel, Bibliographie
Kurzbeschreibung (Abstract)
Utilizing data in the industrial environment offers manufacturing companies numerous opportunities to optimize their own processes and develop new service offerings that go beyond existing business models. Many companies have already recognized this potential and have developed their own data-based solutions. But despite these efforts, many of the solutions developed, ultimately fail on the market. This is commonly caused by companies struggling to develop suitable business models for their technical solutions. A crucial factor in this regard is the lack of adequate economic evaluation during the development phase. On the revenue side, the selection of a suitable pricing strategy represents an important decision. It determines the acceptance on the market as well as the profitability of a data-based business model. Due to the growing share of data-based value creation, new challenges and opportunities arise in this regard. This paper presents an approach to select and develop a suitable pricing strategy for data-based services in the industrial environment. For this purpose, challenges in the selection of pricing strategies with a special focus on intangible goods such as data are examined first. Subsequently, existing pricing mechanisms are identified and discussed. Based on this, a process model for determining suitable pricing strategies is derived, which companies can use in the context of their business model development. The method is then validated with an industrial use case and the results are discussed.
| Typ des Eintrags: | Artikel |
|---|---|
| Erschienen: | 2023 |
| Autor(en): | Hoffmann, Felix ; Lang, Enno ; Metternich, Joachim |
| Art des Eintrags: | Bibliographie |
| Titel: | Seizing the value of data: Selecting appropriate pricing strategies for data-based services in manufacturing |
| Sprache: | Englisch |
| Publikationsjahr: | 2023 |
| Ort: | Amsterdam |
| Verlag: | Elsevier |
| Titel der Zeitschrift, Zeitung oder Schriftenreihe: | Procedia CIRP |
| Jahrgang/Volume einer Zeitschrift: | 120 |
| DOI: | 10.1016/j.procir.2023.08.029 |
| URL / URN: | https://www.sciencedirect.com/science/article/pii/S221282712... |
| Kurzbeschreibung (Abstract): | Utilizing data in the industrial environment offers manufacturing companies numerous opportunities to optimize their own processes and develop new service offerings that go beyond existing business models. Many companies have already recognized this potential and have developed their own data-based solutions. But despite these efforts, many of the solutions developed, ultimately fail on the market. This is commonly caused by companies struggling to develop suitable business models for their technical solutions. A crucial factor in this regard is the lack of adequate economic evaluation during the development phase. On the revenue side, the selection of a suitable pricing strategy represents an important decision. It determines the acceptance on the market as well as the profitability of a data-based business model. Due to the growing share of data-based value creation, new challenges and opportunities arise in this regard. This paper presents an approach to select and develop a suitable pricing strategy for data-based services in the industrial environment. For this purpose, challenges in the selection of pricing strategies with a special focus on intangible goods such as data are examined first. Subsequently, existing pricing mechanisms are identified and discussed. Based on this, a process model for determining suitable pricing strategies is derived, which companies can use in the context of their business model development. The method is then validated with an industrial use case and the results are discussed. |
| Freie Schlagworte: | data economy, servitization, business models |
| 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: | 07 Jun 2024 07:05 |
| Letzte Änderung: | 07 Jun 2024 07:05 |
| PPN: | 518975975 |
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| Suche nach Titel in: | TUfind oder in Google |
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