Article
Evaluation of Manual Skill Degradation Due to Automation in
Apparel Manufacturing
Zahid Rashid * and Matthias Rötting
Citation: Rashid, Z.; Rötting, M.
Evaluation of Manual Skill
Degradation Due to Automation in
Apparel Manufacturing. Appl. Sci.
2021, 11, 11098. https://doi.org/
10.3390/app112311098
Academic Editors: João Carlos de
Oliveira Matias and Radu Godina
Received: 19 October 2021
Accepted: 17 November 2021
Published: 23 November 2021
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4.0/).
Faculty of Mechanical Engineering and Transport Systems, Institute of Psychology and Ergonomics,
Sekr. MAR 3-1Marchstr. 23, D-10587 Berlin, Germany; roetting@mms.tu-berlin.de
* Correspondence: zahid.rashid@campus.tu-berlin.de
Abstract:
Manual skill degradation is a common problem that production managers face in assembly
lines due to frequent changes in batch styles. Since the advancement in automated machinery,
reliance on manual machines has been reduced. However, due to the high cost of fully automated
machinery, it is still not available on a large scale in apparel manufacturing setups as most of the
setups are in developing countries. Few related studies regarding the effects of automation on
manual skills have been conducted in aviation and other emerging technological advanced fields;
little focus was given on the effects of automation in apparel manufacturing. This exploratory study
examines automation-induced performance degradation in the apparel production line. Sixty-seven
sewing machine operators were initially trained on manual sewing machines to learn a complex
production operation. Then, participants were divided randomly into three groups to experience
varied amount of automation exposure. The manual machine group (MMG)kept working on the
manual machines after the initial training and skill development. In contrast, the automation group
(AG) shifted to automated pocket setting machines after skill development. Finally, the refresher
training group (RTG) rotated between manual and automated machines after the skill development.
The skill retrieval assessment was carried out after six weeks in the production line. The result of
an independent t-test showed no significant differences among performances of the three groups
after the initial training stage. A significant increase in the average single cycle time (ASCT) and
decrease in the right-first-time percent (RFT %) was found in the AG while the ASCT decreased and
the RFT% increased among the MMG after the retention interval. The RTG almost maintained its
production output and the ASCT due to refresher training drills. Relevance to industry: Production
managers usually maintain a skill set among the operators to run the production line smoothly.
Therefore, capacity development drills of sewing operators are essential to maintain an efficient
required skill set.
Keywords:
automation; manual skills; skill degradation; sewing machine operators; refresher trainings
1. Introduction
Automation of things in manufacturing setups has made worker’s manual tasks much
easier and faster. Automation is defined as everything that was previously done by humans
and now done by machines [
1
]. However, along with numerous benefits in manufacturing
setups, excessive automation is indirectly causing risks for individuals. Either people
have to adapt to changing working environments by learning new skills or look for other
career opportunities [
2
]. The labor-intensive apparel manufacturing industrial shift from
developed to developing countries has created a competitive race among developing
countries [
3
]. In order to remain competitive, these apparel manufacturing set-ups need to
adapt to new technologies, which they are doing, but not at the same pace as being done
by other industries. Also, due to different complex operations in apparel manufacturing,
the complete shift of automation of tasks is costly and difficult. Nevertheless, there are
some other strategies that these industries can adopt to increase their productivity.
Appl. Sci. 2021, 11, 11098. https://doi.org/10.3390/app112311098 https://www.mdpi.com/journal/applsci
