Article Gold Open Access 2023

Education and training for industry 4.0: a case study of a manufacturing ecosystem

Education and Training
Journal · Vol. 65 · Issue 8-9 · pp. 1070-1084
Abstract

Purpose: The purpose of this paper is to explore the approaches to education and training adopted by manufacturing organisations to identify and develop a set of learning principles for the successful transition to Industry 4.0. Design/methodology/approach: A case study of a manufacturing ecosystem in Queensland, Australia was undertaken, that included semi-structured interviews with a total sample of 22 manufacturing industry representatives, an analysis of secondary data including organisational documents and government reports, and embedded cases of two manufacturing organisations. Findings: Manufacturers successfully transitioning to Industry 4.0 are distinguished by a culture which values learning, management development to understand and lead innovation, experimental learning on the job and strong links to education and training providers through internships and upskilling pathways. These four principles inform approaches to creating tailored training solutions that respond to the unique needs of diverse manufacturing organisations. Research limitations/implications: The two case studies describe exemplary high performing companies only and not companies at earlier stages of adopting Industry 4.0. Therefore, future research could include a broader spectrum of companies across the adoption spectrum. Nevertheless, considered as a study of a total manufacturing ecosystem, there is strong alignment of views of government, industry, union and education stakeholders regarding the key factors of transition to Industry 4.0. Practical implications: There is a strong need for leaders of manufacturing organisations to enable a broad strategy of capability development beyond simple acquisition of new technologies. Detailed consideration and resourcing of on-the-job training and experimentation, talent attraction through innovation workplace cultures and strong relationships with education providers are important. Social implications: Given that Industry 4.0 technologies such as robotics and AI are now rapidly diffusing into other industry sectors, the research has broader implications for education and training for the future of work. These technologies could produce stark differences between efficiency versus innovation-oriented adoption strategies. Whilst the former could displace workers, the latter can open pathways for upskilling, product and process innovation and cross sector employment. Originality/value: Through the ecosystem level case approach, multiple stakeholder perspectives provide triangulated insights into advanced manufacturer's education, skills and training strategies, uncovering four learning principles that underpin the approach of manufacturers successfully transitioning to Industry 4.0. The findings have practical implications for policy makers and industry bodies supporting the transition to advanced manufacturing and provide manufacturing managers with insights into successful education and skill strategies that can be adapted to specific organisational needs. © 2023, Emerald Publishing Limited.

Keywords

Author Keywords

Skills Training technology vocational education and training Industry 4.0 education Learning advanced manufacturing

Index Keywords

Author Affiliations
School of Design, Queensland University of Technology, Brisbane, QLD, Australia
School of Management, Queensland University of Technology, Brisbane, QLD, Australia
Queensland University of Technology, Brisbane, QLD, Australia
Funding & Acknowledgements
Australian Research Council, ARC
Grant: IC200100001
Funding: This research was funded by Jobs Queensland and supported by The Industrial Transformation Training Centre (ITTC) in Collaborative Robotics in Advanced Manufacturing (also known as the Australian Cobotics Centre) funded by the ARC's Industrial Transformation Research Program (Project ID: IC200100001).
Australian Research Council, ARC
Funding: This research was funded by Jobs Queensland and supported by The Industrial Transformation Training Centre (ITTC) in Collaborative Robotics in Advanced Manufacturing (also known as the Australian Cobotics Centre) funded by the ARC's Industrial Transformation Research Program (Project ID: IC200100001).
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