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  2. Life Sciences und Facility Management
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  4. Publikationen Life Sciences und Facility Management
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dc.contributor.authorHollenstein, Lukas-
dc.contributor.authorLichtensteiger, Lukas-
dc.contributor.authorStadelmann, Thilo-
dc.contributor.authorAmirian, Mohammadreza-
dc.contributor.authorBudde, Lukas-
dc.contributor.authorMeierhofer, Jürg-
dc.contributor.authorFüchslin, Rudolf Marcel-
dc.contributor.authorFriedli, Thomas-
dc.date.accessioned2019-06-19T12:05:48Z-
dc.date.available2019-06-19T12:05:48Z-
dc.date.issued2019-
dc.identifier.isbn978-3-030-11820-4de_CH
dc.identifier.isbn978-3-030-11821-1de_CH
dc.identifier.urihttps://digitalcollection.zhaw.ch/handle/11475/17305-
dc.description.abstractA 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.de_CH
dc.language.isoende_CH
dc.publisherSpringerde_CH
dc.relation.ispartofApplied data science : lessons learned for the data-driven businessde_CH
dc.rightsLicence according to publishing contractde_CH
dc.subjectData sciencede_CH
dc.subjectMachine learningde_CH
dc.subjectSimulationde_CH
dc.subject.ddc005: Computerprogrammierung, Programme und Datende_CH
dc.subject.ddc006: Spezielle Computerverfahrende_CH
dc.titleUnsupervised learning and simulation for complexity management in business operationsde_CH
dc.typeBuchbeitragde_CH
dcterms.typeTextde_CH
zhaw.departementLife Sciences und Facility Managementde_CH
zhaw.departementSchool of Engineeringde_CH
zhaw.organisationalunitInstitut für Informatik (InIT)de_CH
zhaw.organisationalunitInstitut für Angewandte Mathematik und Physik (IAMP)de_CH
zhaw.organisationalunitInstitut für Computational Life Sciences (ICLS)de_CH
zhaw.organisationalunitInstitut für Datenanalyse und Prozessdesign (IDP)de_CH
zhaw.publisher.placeChamde_CH
dc.identifier.doi10.1007/978-3-030-11821-1_17de_CH
zhaw.funding.euNode_CH
zhaw.originated.zhawYesde_CH
zhaw.pages.end331de_CH
zhaw.pages.start313de_CH
zhaw.parentwork.editorBraschler, Martin-
zhaw.parentwork.editorStadelmann, Thilo-
zhaw.parentwork.editorStockinger, Kurt-
zhaw.publication.statuspublishedVersionde_CH
zhaw.publication.reviewEditorial reviewde_CH
zhaw.webfeedDatalabde_CH
zhaw.webfeedSimulation and Optimizationde_CH
zhaw.webfeedZHAW digitalde_CH
zhaw.webfeedMachine Perception and Cognitionde_CH
zhaw.webfeedIndustrie 4.0de_CH
zhaw.funding.zhawComplexity 4.0de_CH
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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.


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