Learning curve applications in Industry 4.0: a scoping review

Abstract

This study aimed at identifying applications of learning curve (LC) modelling at individual, group, and organisational levels in Industry 4.0 (I4.0) environments. For that, a scoping review on four databases was conducted using the Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) guidelines. Our results indicated that LCs are more prominently adopted in I4.0 to model learning at the individual level using technologies oriented to sensing and communication (e.g. big data, IoT, wireless sensors, cloud computing, remote control, or monitoring). However, the effect of a few processing and actuation technologies, such as augmented/virtual reality, collaborative robots, and machine learning/AI, on learning seems promising. Further, despite the number of studies investigated, few explicitly described the LC model used to represent the impact of I4.0 technologies on learning. Our findings allowed the proposition of five research directions. Literature on both LC and I4.0 is still fragmented, poorly addressing their relationship. As I4.0 is an innovative approach that allows more extensive information exchange and processing, new ways of using I4.0 technologies to expedite data collection, which has always constrained LC practical applications, should be devised to close the gap between I4.0 and learning.

Keywords:

• Learning curve
• learning models
• Industry 4.0
• information and communication technologies
• scoping review

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.

Flavio Sanson Fogliatto

Flavio S. Fogliatto holds a Full Professor position in the IE Department 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.

Michel J. Anzanello

Michel 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. His research has been published in the journals Chemometrics and Intelligent Laboratory Systems, International Journal of Production Economics, International Journal of Production Research, Forensic Science International and Journal of Pharmaceutical and Biomedical Analysis, among others. It has h-index = 18 (Scopus Base, 09/2021).

Roberto Vassolo

Roberto Vassolo is Full Professor of the IAE Business School at the Universidad Austral, Argentina, and Visiting Profess oat the Department of Industrial Engineering and Systemes at the Pontificia Universidad Católica de Chile. His main research field has been Strategic Management in High Uncertainty contexts, Strategy under the Business Cycle, Competitive Dynamics in Natural Resource Industries, and Adaptation of Organisational Routines.

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.

Kevin Otto

Kevin Otto is a Professor in the Manufacturing and Industrial Engineering Group in the Department of Mechanical Engineering at the University of Melbourne. Professor Otto is an expert in design and manufacturing quality improvement and flexible modular design. His research expertise has been in uncertainty quantification and optimisation to reduce production variability using both on-line and off-line modelling and experimentation. This includes robust design optimisation to ensure confidence levels against probabilistic risk of performance compliance. He has particular interest in model calibration, uncertainty quantification from multiple sources including test and fabrication, and the study of variability reduction across modular product families.

Mike Kagioglou

Mike Kagioglou is Dean of Engineering, Design and Built Environment at Western Sydney University in Australia, and Pro-Vice Chancellor for Global Development (Europe and the UK). Mike was based in the UK for the past 25 years and was Dean of Art, Design and Architecture at the University of Huddersfield and prior to that at the University of Salford where he was the Head of the School of Built Environment. Mike’s career spans Engineering, Manufacturing, Creativity, Design, Architecture and the Built Environment, engaged in inter and multi-disciplinary research at a global level. Mike has taught in product design and development, manufacturing and production systems, lean construction/manufacturing/healthcare, requirements management, among other subject areas. He has been involved in more than £25m of research across many funding agencies in the UK and Europe. He was an Academic Director for the £11M EPSRC funded interdisciplinary IMRC in Health and Care Infrastructures Research and Innovation Centre (HaCIRIC) and was previously the Director of the £8M EPSRC (Engineering and Physical Sciences Research Council) Salford Centre for Research and Innovation (SCRI) in the built and human environment. Mike has published more than 230 academic referred papers, industrial reports and two books, one of which in Healthcare infrastructure. His current research is around Healthcare infrastructure and better decision making in complex settings, following an outcomes/benefits based philosophy—benefits realisation. He is also working in diverse areas, such as living labs, location based and project planning, decision making, BIM and automated regulation capture.