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https://doi.org/10.21256/zhaw-30353| Publikationstyp: | Konferenz: Paper |
| Art der Begutachtung: | Peer review (Abstract) |
| Titel: | Data driven value creation in industrial services including remanufacturing |
| Autor/-in: | Stucki, Melissa Meierhofer, Jürg Gal, Barna Gallina, Viola Eisl, Stefanie |
| et. al: | No |
| DOI: | 10.1016/j.procs.2024.02.043 10.21256/zhaw-30353 |
| Erschienen in: | Procedia Computer Science |
| Band(Heft): | 232 |
| Seite(n): | 2240 |
| Seiten bis: | 2248 |
| Angaben zur Konferenz: | 5th International Conference on Industry 4.0 and Smart Manufacturing (ISM), Lisbon, Portugal, 22-24 November 2023 |
| Erscheinungsdatum: | 2024 |
| Verlag / Hrsg. Institution: | Elsevier |
| ISSN: | 1877-0509 |
| Sprache: | Englisch |
| Schlagwörter: | Product-service system; Remanufacturing; Sustainable value creation; Data driven decision making |
| Fachgebiet (DDC): | 658.5: Produktionssteuerung |
| Zusammenfassung: | In the era of twin transition companies face complex challenges. Economical resource usage and efficiency are particularly important in the manufacturing sector. Product-service systems are seen a promising solution that can meet the expectations regarding efficiency in a sustainable way. However, there is a huge potential regarding the evaluation of sustainable value creation. The paper focuses on the value creation process in product-service systems including offers with remanufactured products. Against this background, the goal of this paper is to describe how data driven industrial services can create sustainable value in the meaning of the triple bottom line. Based on previous work, a quantitative model for the assessment and optimization of this value creation is extended to include additional remanufacturing strategies. The value optimization model integrates the different perspectives of provider, custumer and society. The numerical evaluation of this model for a specific application case shows that economic and ecological value creation can be jointly achieved and optimized, and which service arrangements lead to this optimization. |
| URI: | https://digitalcollection.zhaw.ch/handle/11475/30353 |
| Volltext Version: | Publizierte Version |
| Lizenz (gemäss Verlagsvertrag): | CC BY-NC-ND 4.0: Namensnennung - Nicht kommerziell - Keine Bearbeitungen 4.0 International |
| Departement: | School of Engineering |
| Organisationseinheit: | Institut für Datenanalyse und Prozessdesign (IDP) |
| Enthalten in den Sammlungen: | Publikationen School of Engineering |
Dateien zu dieser Ressource:
| Datei | Beschreibung | Größe | Format | |
|---|---|---|---|---|
| 2024_Stucki-etal_Data-driven-value-creation-industrial-services-remanufacturing.pdf | 733.95 kB | Adobe PDF |  Öffnen/Anzeigen |
Zur Langanzeige
Stucki, M., Meierhofer, J., Gal, B., Gallina, V., & Eisl, S. (2024). Data driven value creation in industrial services including remanufacturing [Conference paper]. Procedia Computer Science, 232, 2240–2248. https://doi.org/10.1016/j.procs.2024.02.043
Stucki, M. et al. (2024) ‘Data driven value creation in industrial services including remanufacturing’, in Procedia Computer Science. Elsevier, pp. 2240–2248. Available at: https://doi.org/10.1016/j.procs.2024.02.043.
M. Stucki, J. Meierhofer, B. Gal, V. Gallina, and S. Eisl, “Data driven value creation in industrial services including remanufacturing,” in Procedia Computer Science, 2024, vol. 232, pp. 2240–2248. doi: 10.1016/j.procs.2024.02.043.
STUCKI, Melissa, Jürg MEIERHOFER, Barna GAL, Viola GALLINA und Stefanie EISL, 2024. Data driven value creation in industrial services including remanufacturing. In: Procedia Computer Science. Conference paper. Elsevier. 2024. S. 2240–2248
Stucki, Melissa, Jürg Meierhofer, Barna Gal, Viola Gallina, and Stefanie Eisl. 2024. “Data Driven Value Creation in Industrial Services Including Remanufacturing.” Conference paper. In Procedia Computer Science, 232:2240–48. Elsevier. https://doi.org/10.1016/j.procs.2024.02.043.
Stucki, Melissa, et al. “Data Driven Value Creation in Industrial Services Including Remanufacturing.” Procedia Computer Science, vol. 232, Elsevier, 2024, pp. 2240–48, https://doi.org/10.1016/j.procs.2024.02.043.
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