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

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journal contribution
posted on 13.06.2019, 08:58 by Yee Goh, Simon Micheler, Angel Sanchez-Salas, Keith Case, Daniel Bumblauskas, Radmehr Monfared
Although many manual operations have been replaced by automation in the manufacturing domain, in various industries skilled operators still carry out critical manual tasks such as final assembly.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 Control

Volume

31

Issue

5

Pages

383-399

Citation

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 & Francis

Version

VoR (Version of Record)

Rights holder

© the authors

Publisher 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

22/06/2019

Publication date

2019-07-31

Copyright date

2019

ISSN

0953-7287

Language

en

Licence

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