Manual assembly learning, disability, and instructions: an industrial experiment

Abstract

Cognitive assistance systems help people with learning disabilities to increase their skills and consequently their employment opportunities in the regular labour market. Research on advanced work instructions has encouraged training disabled workers in cognitively demanding production tasks, especially manual assembly. However, studies lack evidence on the effect of repetition or work cycle alongside the form of instruction and type of disability. This paper addresses this gap and reports on an experiment conducted at a sheltered workplace. Four forms of instruction (paper-based, animations, projection, adaptive projection) were tested to assist operators with three types of disability (illiterate, psychosocial, cognitive) with a manual assembly task. The results show that projection enhances the first assembly cycle. Challenging operators by filtering the content of the instruction with increased experience leads to greater independence and a better understanding of their tasks. However, adaptive instructions can form a barrier for those operators who are most dependent on mentor support. The form of instruction should thus be considered carefully for each operator as their adaptation to changes and cognitive assistance systems varies. The results are discussed in light of the Industry 5.0 human-centric and socially sustainable production agenda with managerial and research implications and future research priorities.

KEYWORDS:

• Disability
• instruction
• cognitive
• assistance systems
• learning
• Industry 5.0

Acknowledgements

We thank Ton Janssen, Liese van Oort, and Rob Kerkhofs, at Senzer, and Bart Lamberigts, at Arkite NV, for providing resources and support during the project. We also thank the anonymous reviewers for their constructive comments to improve this paper.

Data availability statement

The data that supports the findings of this study is available from the corresponding author upon reasonable request.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Notes

1 HIM installed at Senzer (sheltered workplace in NL). Retrieved 21 February 2023, from https://www.youtube.com/watch?v=YF9Fbvejp-s.

2 Arkite Platform. Retrieved 21 February 2023, from https://www.arkite.com/platform/.

Additional information

Funding

This work was supported by the Finnish Work Environment Fund [Työsuojelurahasto] under grant 200224.

Notes on contributors

Jaakko Peltokorpi

Jaakko Peltokorpi is a Post-Doc Researcher at Aalto University, Department of Mechanical Engineering, in the Materials to Products (M2P) research group. He received a Ph.D. degree with the title ‘Worker Coordination, Collaboration and Learning in Make-to-order Assembly Production’ at Aalto University in 2018. Since then, he has been investigating and developing analytical learning curve models which also consider the cognitive load individuals encounter during assembly work. He is currently working in the ‘Green Factory – towards carbon neutral production (GREEF)’ project in the Sustainable Manufacturing Finland programme, funded by Business Finland.

Steven Hoedt

Steven Hoedt is a Master of Science in automation engineering and a Ph.D. candidate at Ghent University. The focus of his Ph.D. studies in Industrial Engineering and Operations Research is mainly on the assessment of the learning and forgetting of manual assembly operators and how to tackle the forgetting effect via training or on-the-job support systems. In addition to his Ph.D. research, he coordinates various research projects that mainly focus on human-augmented technology in the manufacturing industry and is an active researcher within the FlandersMake@UGent-ISyE research group.

Thomas Colman

Thomas Colman is a Master of Science in electronics engineering and works at the data analytics team at Kion Mobile Automation. During and for this research project he has been developing an algorithm, commissioned by Arkite NV, that monitors the learning curve of a certain test person, determines a personal experience level, and adjusts the amount of information shown accordingly. By using his experience in practical research, he played a key role in setting up, conducting, and maintaining the physical field tests.

Kim Rutten

Kim Rutten has a Master’s degree in chemical engineering from the Technical University of Eindhoven in the Netherlands. After ten years of innovation engineering, he joined Arkite, a company specialising in Augmented Reality guidance systems for the manufacturing industry. Aside from proprietary 3D detection algorithms, he focused on mining operator performance data. He is currently working as a product manager for Machine Learning at Software AG. Leveraging machine learning capabilities for leading platforms for IIoT – Cumulocity IoT, for self-service analytics – TrendMiner, for streaming analytics – Apama and the Thin Edge revolution.

El-Houssaine Aghezzaf

El-Houssaine Aghezzaf is a professor of industrial systems engineering and operations research at the Faculty of Engineering and Architecture of Ghent University. He holds a Master of Science degree and a Ph.D. in applied mathematics and operation. He currently heads the department of Industrial Systems Engineering and Product Design and has an active role in Flanders Make, a strategic research centre in Flanders. His main research interests are in integrated optimisation and simulation approaches to the design, planning, and control problems arising in manufacturing systems and in logistical and utility networks.

Johannes Cottyn

Johannes Cottyn is an assistant professor in industrial automation at Ghent University in the department of Industrial Systems Engineering (ISyE) and Product Design. He leads the FlandersMake@UGent-ISyE research group within the Flexible Assembly of Flanders Make research cluster. His initial research interests lie in the combination of industrial control and software systems (e.g. MES) and manufacturing excellence best practices (e.g. Lean). His current research activities are focused on human-augmented and digital-twin-enabled manufacturing.