Advanced automation and robotics for high volume labour-intensive manufacturing

High volume, labour-intensive manufacturing operations pose several challenges for successful operations. It is an even bigger challenge to move away from manual dependency and shift towards automation of production processes. There seems to be no general agreement among researchers on what are the...

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Main Authors: Gavin Lai, Nai Yeen, Jayasekara, Deepesh, Wong, Kok Hoong, Yu, Lih Jiun, Kang, Hooi Siang, Pawar, Kulwant, Zhu, Yingdan
Format: Book Section
Language:English
Published: IEEE 2020
Subjects:
Online Access:https://eprints.nottingham.ac.uk/63019/
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author Gavin Lai, Nai Yeen
Jayasekara, Deepesh
Wong, Kok Hoong
Yu, Lih Jiun
Kang, Hooi Siang
Pawar, Kulwant
Zhu, Yingdan
author_facet Gavin Lai, Nai Yeen
Jayasekara, Deepesh
Wong, Kok Hoong
Yu, Lih Jiun
Kang, Hooi Siang
Pawar, Kulwant
Zhu, Yingdan
author_sort Gavin Lai, Nai Yeen
building Nottingham Research Data Repository
collection Online Access
description High volume, labour-intensive manufacturing operations pose several challenges for successful operations. It is an even bigger challenge to move away from manual dependency and shift towards automation of production processes. There seems to be no general agreement among researchers on what are the critical success factors (CSFs) on the implementation of advanced automation and robotics processes in the manufacturing environment. Success factors vary across the manufacturing industry and also organizational units. This paper attempts to elicit the opinion and perception of academic professionals with links to high volume labour-intensive manufacturing industry. A systematic literature search was conducted on the topic and semi-structured interviews were conducted with experts to gather their views on CSFs. Results from the literature search and interviews revealed that factors related to Organization Strategy, Culture, and Structure; Top management support; Past experience, staffing, and learning; Project management, planning, and control; Finance, and resource availability; Technology maturity and vendor support; to be critical for the success of the production process automation project initiatives. Findings from this study can be used as guidelines to ascertain CSFs for organizations and strategy planning. Manufacturing organizations can also use this study to evaluate their organizational readiness and enhance the level of automation further.
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spelling nottingham-630192020-09-14T01:33:28Z https://eprints.nottingham.ac.uk/63019/ Advanced automation and robotics for high volume labour-intensive manufacturing Gavin Lai, Nai Yeen Jayasekara, Deepesh Wong, Kok Hoong Yu, Lih Jiun Kang, Hooi Siang Pawar, Kulwant Zhu, Yingdan High volume, labour-intensive manufacturing operations pose several challenges for successful operations. It is an even bigger challenge to move away from manual dependency and shift towards automation of production processes. There seems to be no general agreement among researchers on what are the critical success factors (CSFs) on the implementation of advanced automation and robotics processes in the manufacturing environment. Success factors vary across the manufacturing industry and also organizational units. This paper attempts to elicit the opinion and perception of academic professionals with links to high volume labour-intensive manufacturing industry. A systematic literature search was conducted on the topic and semi-structured interviews were conducted with experts to gather their views on CSFs. Results from the literature search and interviews revealed that factors related to Organization Strategy, Culture, and Structure; Top management support; Past experience, staffing, and learning; Project management, planning, and control; Finance, and resource availability; Technology maturity and vendor support; to be critical for the success of the production process automation project initiatives. Findings from this study can be used as guidelines to ascertain CSFs for organizations and strategy planning. Manufacturing organizations can also use this study to evaluate their organizational readiness and enhance the level of automation further. IEEE 2020-07-30 Book Section PeerReviewed application/pdf en cc_by https://eprints.nottingham.ac.uk/63019/1/091115475929MergePDF.pdf Gavin Lai, Nai Yeen, Jayasekara, Deepesh, Wong, Kok Hoong, Yu, Lih Jiun, Kang, Hooi Siang, Pawar, Kulwant and Zhu, Yingdan (2020) Advanced automation and robotics for high volume labour-intensive manufacturing. In: 2020 International Congress on Human-Computer Interaction, Optimization and Robotic Applications (HORA). IEEE, [New York], pp. 1-9. ISBN 9781728193526 Automation Manufacturing; Service robots; Interviews; Manufacturing industries; Critical success factors; Automation and robotics; Manufacturing; Industry 4.0 http://dx.doi.org/10.1109/HORA49412.2020.9152831 10.1109/HORA49412.2020.9152831 10.1109/HORA49412.2020.9152831 10.1109/HORA49412.2020.9152831
spellingShingle Automation Manufacturing; Service robots; Interviews; Manufacturing industries; Critical success factors; Automation and robotics; Manufacturing; Industry 4.0
Gavin Lai, Nai Yeen
Jayasekara, Deepesh
Wong, Kok Hoong
Yu, Lih Jiun
Kang, Hooi Siang
Pawar, Kulwant
Zhu, Yingdan
Advanced automation and robotics for high volume labour-intensive manufacturing
title Advanced automation and robotics for high volume labour-intensive manufacturing
title_full Advanced automation and robotics for high volume labour-intensive manufacturing
title_fullStr Advanced automation and robotics for high volume labour-intensive manufacturing
title_full_unstemmed Advanced automation and robotics for high volume labour-intensive manufacturing
title_short Advanced automation and robotics for high volume labour-intensive manufacturing
title_sort advanced automation and robotics for high volume labour-intensive manufacturing
topic Automation Manufacturing; Service robots; Interviews; Manufacturing industries; Critical success factors; Automation and robotics; Manufacturing; Industry 4.0
url https://eprints.nottingham.ac.uk/63019/
https://eprints.nottingham.ac.uk/63019/
https://eprints.nottingham.ac.uk/63019/