Dr Manish Shukla manish.shukla@durham.ac.uk
Assistant Professor
Application of additive manufacturing for mass customisation: understanding the interaction of critical barriers
Shukla, Manish; Todorov, Ivan; Kapletia, Dharm
Authors
Ivan Todorov
Dharm Kapletia
Abstract
Additive Manufacturing (AM) technology presents a very optimistic case for its application for mass customisation. Even though theoretically suitable, practically several critical barriers inhibit its implementation. Thus, this paper attempts to identify those barriers and also understand the dynamic interaction among them. The barriers were identified by a detailed literature review and validated by expert opinions. Interpretative Structural Modelling (ISM) was applied to determine the mutual influences among the barriers. It was also able to ascertain the level of the barriers and categorise them based on their driving power and dependence. The results are highly useful for industry practitioners to determine the interventions required to overcome the most dominant barriers.
Citation
Shukla, M., Todorov, I., & Kapletia, D. (2018). Application of additive manufacturing for mass customisation: understanding the interaction of critical barriers. Production Planning and Control, 29(10), 814-825. https://doi.org/10.1080/09537287.2018.1474395
Journal Article Type | Article |
---|---|
Acceptance Date | May 3, 2018 |
Online Publication Date | May 21, 2018 |
Publication Date | Jul 27, 2018 |
Deposit Date | Jun 26, 2018 |
Publicly Available Date | May 21, 2019 |
Journal | Production Planning and Control |
Print ISSN | 0953-7287 |
Electronic ISSN | 1366-5871 |
Publisher | Taylor and Francis Group |
Peer Reviewed | Peer Reviewed |
Volume | 29 |
Issue | 10 |
Pages | 814-825 |
DOI | https://doi.org/10.1080/09537287.2018.1474395 |
Keywords | Additive manufacturing, mass customisation, supply chain, barriers, interpretative structural modelling. |
Public URL | https://durham-repository.worktribe.com/output/1323555 |
Files
Accepted Journal Article
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Copyright Statement
This is an Accepted Manuscript of an article published by Taylor & Francis in Production Planning & Control on 21 May 2018, available online: http://www.tandfonline.com/10.1080/09537287.2018.1474395.
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