| Publikationstyp: | Buchbeitrag |
| Art der Begutachtung: | Editorial review |
| Titel: | Unsupervised learning and simulation for complexity management in business operations |
| Autor/-in: | Hollenstein, Lukas Lichtensteiger, Lukas Stadelmann, Thilo Amirian, Mohammadreza Budde, Lukas Meierhofer, Jürg Füchslin, Rudolf Marcel Friedli, Thomas |
| DOI: | 10.1007/978-3-030-11821-1_17 |
| Erschienen in: | Applied data science : lessons learned for the data-driven business |
| Herausgeber/-in des übergeordneten Werkes: | Braschler, Martin Stadelmann, Thilo Stockinger, Kurt |
| Seite(n): | 313 |
| Seiten bis: | 331 |
| Erscheinungsdatum: | 2019 |
| Verlag / Hrsg. Institution: | Springer |
| Verlag / Hrsg. Institution: | Cham |
| ISBN: | 978-3-030-11820-4 978-3-030-11821-1 |
| Sprache: | Englisch |
| Schlagwörter: | Data science; Machine learning; Simulation |
| Fachgebiet (DDC): | 005: Computerprogrammierung, Programme und Daten 006: Spezielle Computerverfahren |
| Zusammenfassung: | A key resource in data analytics projects is the data to be analyzed. What can be done in the middle of a project if this data is not available as planned? This chapter explores a potential solution based on a use case from the manufacturing industry where the drivers of production complexity (and thus costs) were supposed to be determined by analyzing raw data from the shop floor, with the goal of subsequently recommending measures to simplify production processes and reduce complexity costs. The unavailability of the data—often a major threat to the anticipated outcome of a project—has been alleviated in this case study by means of simulation and unsupervised machine learning: a physical model of the shop floor produced the necessary lower-level records from high-level descriptions of the facility. Then, neural autoencoders learned a measure of complexity regardless of any human-contributed labels. In contrast to conventional complexity measures based on business analysis done by consultants, our data-driven methodology measures production complexity in a fully automated way while maintaining a high correlation to the human-devised measures. |
| URI: | https://digitalcollection.zhaw.ch/handle/11475/17305 |
| Volltext Version: | Publizierte Version |
| Lizenz (gemäss Verlagsvertrag): | Lizenz gemäss Verlagsvertrag |
| Departement: | Life Sciences und Facility Management School of Engineering |
| Organisationseinheit: | Institut für Informatik (InIT) Institut für Angewandte Mathematik und Physik (IAMP) Institut für Computational Life Sciences (ICLS) Institut für Datenanalyse und Prozessdesign (IDP) |
| Publiziert im Rahmen des ZHAW-Projekts: | Complexity 4.0 |
| Enthalten in den Sammlungen: | Publikationen Life Sciences und Facility Management |
Dateien zu dieser Ressource:
Es gibt keine Dateien zu dieser Ressource.
Zur Langanzeige
Hollenstein, L., Lichtensteiger, L., Stadelmann, T., Amirian, M., Budde, L., Meierhofer, J., Füchslin, R. M., & Friedli, T. (2019). Unsupervised learning and simulation for complexity management in business operations. In M. Braschler, T. Stadelmann, & K. Stockinger (Eds.), Applied data science : lessons learned for the data-driven business (pp. 313–331). Springer. https://doi.org/10.1007/978-3-030-11821-1_17
Hollenstein, L. et al. (2019) ‘Unsupervised learning and simulation for complexity management in business operations’, in M. Braschler, T. Stadelmann, and K. Stockinger (eds) Applied data science : lessons learned for the data-driven business. Cham: Springer, pp. 313–331. Available at: https://doi.org/10.1007/978-3-030-11821-1_17.
L. Hollenstein et al., “Unsupervised learning and simulation for complexity management in business operations,” in Applied data science : lessons learned for the data-driven business, M. Braschler, T. Stadelmann, and K. Stockinger, Eds. Cham: Springer, 2019, pp. 313–331. doi: 10.1007/978-3-030-11821-1_17.
HOLLENSTEIN, Lukas, Lukas LICHTENSTEIGER, Thilo STADELMANN, Mohammadreza AMIRIAN, Lukas BUDDE, Jürg MEIERHOFER, Rudolf Marcel FÜCHSLIN und Thomas FRIEDLI, 2019. Unsupervised learning and simulation for complexity management in business operations. In: Martin BRASCHLER, Thilo STADELMANN und Kurt STOCKINGER (Hrsg.), Applied data science : lessons learned for the data-driven business. Cham: Springer. S. 313–331. ISBN 978-3-030-11820-4
Hollenstein, Lukas, Lukas Lichtensteiger, Thilo Stadelmann, Mohammadreza Amirian, Lukas Budde, Jürg Meierhofer, Rudolf Marcel Füchslin, and Thomas Friedli. 2019. “Unsupervised Learning and Simulation for Complexity Management in Business Operations.” In Applied Data Science : Lessons Learned for the Data-Driven Business, edited by Martin Braschler, Thilo Stadelmann, and Kurt Stockinger, 313–31. Cham: Springer. https://doi.org/10.1007/978-3-030-11821-1_17.
Hollenstein, Lukas, et al. “Unsupervised Learning and Simulation for Complexity Management in Business Operations.” Applied Data Science : Lessons Learned for the Data-Driven Business, edited by Martin Braschler et al., Springer, 2019, pp. 313–31, https://doi.org/10.1007/978-3-030-11821-1_17.
Alle Ressourcen in diesem Repository sind urheberrechtlich geschützt, soweit nicht anderweitig angezeigt.