Learning curve applications in Industry 4.0: a scoping review

Guilherme Luz Tortorella, Flavio Sanson Fogliatto, Michel J. Anzanello, Roberto Vassolo, Jiju Antony, Kevin Otto, Mike Kagioglou

    Research output: Contribution to journalArticlepeer-review

    2 Scopus citations


    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.

    Original languageBritish English
    JournalProduction Planning and Control
    StateAccepted/In press - 2022


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


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