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
Published in : Applied data science : lessons learned for the data-driven business
Pages : 313
Pages to: 331
Editors of the parent work: Braschler, Martin
Stadelmann, Thilo
Stockinger, Kurt
Publisher / Ed. Institution : Springer
Publisher / Ed. Institution: Cham
Issue Date: 2019
License (according to publishing contract) : Licence according to publishing contract
Type of review: Editorial review
Language : English
Subjects : Data science; Machine learning; Simulation
Subject (DDC) : 005: Computer programming, programs and data
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.
Departement: Life Sciences and Facility Management
School of Engineering
Organisational Unit: Institute of Applied Information Technology (InIT)
Institute of Applied Mathematics and Physics (IAMP)
Institute of Applied Simulation (IAS)
Institute of Data Analysis and Process Design (IDP)
Publication type: Book Part
DOI : 10.1007/978-3-030-11821-1_17
ISBN: 978-3-030-11820-4
978-3-030-11821-1
URI: https://digitalcollection.zhaw.ch/handle/11475/17305
Published as part of the ZHAW project : Complexity 4.0
Appears in Collections:Publikationen Life Sciences und Facility Management

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