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 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

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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.

Item Type: Book Section
Keywords: Automation Manufacturing; Service robots; Interviews; Manufacturing industries; Critical success factors; Automation and robotics; Manufacturing; Industry 4.0
Schools/Departments: University of Nottingham Ningbo China > Faculty of Science and Engineering > Department of Mechanical, Materials and Manufacturing Engineering
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Depositing User: Yu, Tiffany
Date Deposited: 14 Sep 2020 01:33
Last Modified: 14 Sep 2020 01:33

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