Publication type: | Book part |
Type of review: | Editorial review |
Title: | Unsupervised learning and simulation for complexity management in business operations |
Authors: | 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 |
Published in: | Applied data science : lessons learned for the data-driven business |
Editors of the parent work: | Braschler, Martin Stadelmann, Thilo Stockinger, Kurt |
Page(s): | 313 |
Pages to: | 331 |
Issue Date: | 2019 |
Publisher / Ed. Institution: | Springer |
Publisher / Ed. Institution: | Cham |
ISBN: | 978-3-030-11820-4 978-3-030-11821-1 |
Language: | English |
Subjects: | Data science; Machine learning; Simulation |
Subject (DDC): | 005: Computer programming, programs and data 006: Special computer methods |
Abstract: | 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 |
Fulltext version: | Published version |
License (according to publishing contract): | Licence according to publishing contract |
Departement: | Life Sciences and Facility Management School of Engineering |
Organisational Unit: | Institute of Computer Science (InIT) Institute of Applied Mathematics and Physics (IAMP) Institute of Computational Life Sciences (ICLS) Institute of Data Analysis and Process Design (IDP) |
Published as part of the ZHAW project: | Complexity 4.0 |
Appears in collections: | Publikationen Life Sciences und Facility Management |
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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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