Unsupervised Learning and Simulation for Complexity Management in Business Operations
ISBN
978-3-030-11821-1
Type
book section
Date Issued
2019-06-14
Author(s)
Hollenstein, Lukas
;
Lichtensteiger, Lukas
;
Stadelmann, Thilo
;
Mohammadreza, Amirian
;
;
Meierhofer, Jürg
;
Füchslin, Rudolf
;
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.
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.
Language
English
HSG Classification
contribution to scientific community
HSG Profile Area
SoM - Business Innovation
Book title
Applied Data Science
Publisher
Springer
Start page
313
End page
331
Division(s)
Eprints ID
259557