Goh_A variability taxonomy to support automation decision making for manufacturing processes.pdf (2.29 MB)
A variability taxonomy to support automation decision-making for manufacturing processes
journal contribution
posted on 2019-06-13, 08:58 authored by Yee GohYee Goh, Simon Micheler, Angel Sanchez-Salas, Keith Case, Daniel Bumblauskas, Radmehr MonfaredRadmehr MonfaredAlthough many manual operations have been replaced by automation in the manufacturing domain, in
various industries skilled operators still carry out critical manual tasks such as final assembly. The
business case for automation in these areas is difficult to justify due to increased complexity and costs
arising out of process variabilities associated with those tasks. The lack of understanding of process
variability in automation design means that industrial automation often does not realise the full benefits
at the first attempt, resulting in the need to spend additional resource and time, to fully realise the
potential. This article describes a taxonomy of variability when considering automation of
manufacturing processes. Three industrial case studies were analysed to develop the proposed
taxonomy. The results obtained from the taxonomy are discussed with a further case study to
demonstrate its value in supporting automation decision-making.
History
School
- Mechanical, Electrical and Manufacturing Engineering
Published in
Production Planning and ControlVolume
31Issue
5Pages
383-399Citation
GOH, Y.M. ... et al., 2020. A variability taxonomy to support automation decision-making for manufacturing processes. Production Planning and Control, 31 (5), pp.383-399.Publisher
Taylor & FrancisVersion
- VoR (Version of Record)
Rights holder
© the authorsPublisher statement
This is an Open Access Article. It is published by Taylor and Francis under the Creative Commons Attribution 4.0 Unported Licence (CC BY). Full details of this licence are available at: http://creativecommons.org/licenses/by/4.0/Acceptance date
2019-06-22Publication date
2019-07-31Copyright date
2019ISSN
0953-7287Publisher version
Language
- en