Smart operators: How augmented and virtual technologies are affecting the worker's performance in manufacturing contexts
Abstract
Purpose: The correct interaction between the workforce and augmented, virtual, and mixed reality technologies represents a crucial aspect of the success of the smart factory. This interaction is, indeed, affected by the variability of human behavior and its reliability, which can strongly influence the quality, safety, and productivity standards. For this reason, this paper aims to provide a clear and complete analysis of the impacts of these technologies on the performance of operators.
Design/methodology/approach: A Systematic Literature Review (SLR) was conducted to identify peer-reviewed papers that focused on the implementation of augmented and virtual technologies in manufacturing systems and their effects on human performance.
Findings: In total, 61 papers were selected and thoroughly analyzed. The findings of this study reveal that Augmented, Virtual and Mixed Reality can be applied for several applications in manufacturing systems with different types of devices, that involve various advantages and disadvantages. The worker’s performance that are influencing by the use of these technologies are above all time to complete a task, error rate and mental and physical workload.
Originality/value: Over the years Augmented, Virtual and Mixed Reality technologies in manufacturing systems have been investigated by researchers. Several studies mostly focused on technological issues, have been conducted. The role of the operator, whose tasks may be influenced positively or negatively by the use of new devices, has been hardly ever analyzed and a deep analysis of human performance affected by these technologies is missing. This study represents a preliminary analysis to fill this gap. The results obtained from the SLR allowed us to develop a conceptual framework that investigates the current state-of-the-art knowledge about the topic and highlights gaps in the current research.
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Full Text:
PDFDOI: https://doi.org/10.3926/jiem.3607
This work is licensed under a Creative Commons Attribution 4.0 International License
Journal of Industrial Engineering and Management, 2008-2024
Online ISSN: 2013-0953; Print ISSN: 2013-8423; Online DL: B-28744-2008
Publisher: OmniaScience