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Please use this identifier to cite or link to this item: https://dspace.lboro.ac.uk/2134/37924

Title: A variability taxonomy to support automation decision-making for manufacturing processes
Authors: Goh, Yee M.
Micheler, Simon
Sanchez-Salas, Angel
Case, Keith
Bumblauskas, Daniel
Monfared, Radmehr P.
Keywords: Variability
Manufacturing process
Decision support
Issue Date: 2019
Publisher: Taylor & Francis
Citation: GOH, Y.M. ... et al., 2019. A variability taxonomy to support automation decision-making for manufacturing processes. Production Planning and Control, In Press.
Abstract: Although many manual operations have been replaced by automation in the manufacturing domain, in various industries skilled operators still carry out critical manual tasks such as final assembly.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.
Description: This paper is in closed access until 12 mths after publication.
Version: Accepted for publication
URI: https://dspace.lboro.ac.uk/2134/37924
Publisher Link: https://www.tandfonline.com/
ISSN: 0953-7287
Appears in Collections:Closed Access (Mechanical, Electrical and Manufacturing Engineering)

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