Effect of Industry 4.0 technologies adoption on the learning process of workers in a quality inspection operation

Abstract

This study examines the effect of Industry 4.0 (I4.0) technologies on the learning process of operators. We collected data from the training of new operators in a quality inspection workstation. Two distinct scenarios were considered: before and after the adoption of I4.0 technologies. Data from 10 operators were collected in each scenario; the quality inspection cycle was repeated by each operator 30 consecutive times. A 2-parameter hyperbolic learning curve model was used to assess the learning process in the two groups. Results indicated that operators supported by I4.0 technologies had a significantly higher learning rate than those performing the same tasks without I4.0 support. No significant difference was found in the final performance level between groups. Our study bridges a theoretical gap in the relationship between I4.0 and learning by directly comparing the effect of digital support on the training of new employees in a manufacturing environment. We also offer arguments to support managerial decisions with regards to I4.0 adopti-on at an operational level. That allows organisations to prioritise their digitalisation efforts so that the training of operators in workstations can be expedited.

KEYWORDS:

• Industry 4.0
• learning curve
• learning rate
• quality control
• training
• cockpit inspection

Data availability statement

The data that support the findings of this study are available from the corresponding author, GT, upon reasonable request.

Disclosure statement

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

Additional information

Notes on contributors

Guilherme Luz Tortorella

Guilherme Luz Tortorella is Associate Professor of the Department of Systems and Production Engineering of the Universidade Federal de Santa Catarina, Brazil. He is the Head of Research of the Productivity and Continous Improvement Lab and the Editor-in-Chief of Journal of Lean Systems. He is one of the founders of the Brazilian Conference on Lean Systems and has more than 18 years with practical and academic experience with manufacturing and operations management.

Michel J. Anzanello

Michel J. Anzanello holds a PhD in Industrial and Systems Engineering from Rutgers – The State University of New Jersey – USA (2009), a Master's in Production Engineering from the Federal University of Rio Grande do Sul (2004) and a degree in Chemical Engineering from the Federal University of Rio Grande. of the South (2001). He is Associate Professor II at the Department of Production and Transport Engineering at the Federal University of Rio Grande do Sul and professor at the Graduate Program in Production Engineering at UFRGS, of which he is vice-coordinator. He is an ad-hoc advisor to CNPq and CAPES. He has been a member of the Advisory Committee for Engineering III at CAPES since 2017. He received the best track paper award at the International Conference on Industrial Engineering and Operations Management (IEOM) 2017. He has experience in the area of Production Engineering, with an emphasis on data mining, planning, design and control of production systems, multivariate process control and learning curve analysis.

Flavio S. Fogliatto

Flavio S. Fogliatto holds a Full Professor position in the IE Dept of the Federal University of Rio Grande do Sul, Brazil. He received his PhD in Industrial & Systems Engineering from Rutgers University, USA. Prof. Fogliatto specialises in the research areas of Quality Engineering, Operations Research, and Healthcare Analytics. His work has been published in Chemometrics, PP&C, Computers & Industrial Engineering, International Journal of Production Research and International Journal of Production Economics, among others.

Jiju Antony

Jiju Antony is recognised worldwide as a leader in Lean Six Sigma (LSS) methodology for achieving and sustaining operational excellence. He is a Professor of Industrial and Systems Engineering and triple certified LSS Master Black Belt (ASQ, USA; ILSSi, UK and ISSP, UK) in the department of Industrial and Systems Engineering at Khalifa University, Abu Dhabi. He has a proven track record for conducting internationally leading research in the field of Quality Management, Continuous Improvement and Operational Excellence. Professor Antony has authored over 550 journal, conference and white papers and 14 textbooks. He has published over 300 papers on Six Sigma and Lean Six Sigma topics and is considered to be one of the highest in the world for the number of Six Sigma publications. He has an h-index of 90 according to Google Scholar with a total of over 30,000 citations on quality management and operational excellence topics, the highest in the world.

Daniel Nascimento

Daniel Nascimento is a researcher in the Operations Management and Industrial Engineering at the University of Jaen, Spain. Dr. Nascimento is an expert in Industry 4.0 and sustainability, also having a large experience as a practitioner in the oil and gas industry sector. His research expertise has been in the adoption of novel digital technologies to support the improvement of key organisational processes.