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The "future of employment" on the shop floor: Why production jobs are less susceptible to computerization than assumed

Sabine Pfeiffer

International Journal for Research in Vocational Education and Training (IJRVET), 2018, vol. 5, issue 3, 208-225

Abstract: Context: Germany is seen as one of the major players in developing what is known as 'Industry 4.0.' Especially in the manufacturing and the automotive sector, the vocational training is seen as a precondition and consequence alike for the global success of these sectors. Current research though characterizes production work, especially machine-related tasks, as dull routine work and therefore of high probability of computerization. Approach: Based on qualitative research perspectives and sociological results that reveal the importance of experience and implicit capabilities, this study quantifies what is mostly seen as 'non-routine' work. To measure these dimensions of living labouring capacity, an index is introduced that is developed from 18 items of one of the biggest German task-based, representative surveys. Findings: The contribution challenges the widespread prognosis that production workers face high susceptibility. Comparing data on non-routine share in production and of vocational trained workers with those of Frey and Osborne, the findings stress the mostly neglected importance of non-routine work, even in production and especially with vocational trained, machine-related occupations. Conclusion: The results draw on how much more employees on the shop floor are apt to handle change, complexity, and imponderabilities than often assumed. If their work will or will not be susceptible to novel approaches in robotics or algorithms, therefore, is not a question of routine.

Keywords: VET; Vocational Education and Training; Industry 4.0; Routine Task; Living Labouring Capacity; Machine Work; Experience (search for similar items in EconPapers)
Date: 2018
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International Journal for Research in Vocational Education and Training (IJRVET) is currently edited by Karen Evans, Michael Gessler, Johanna Lasonen, Margaret Malloch, and Martin Mulder

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