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  • 1.
    Etemady Qeshmy, Danial
    et al.
    KTH, School of Industrial Engineering and Management (ITM), Industrial Economics and Management (Dept.).
    Makdisi, Jacob
    KTH, School of Industrial Engineering and Management (ITM), Industrial Economics and Management (Dept.).
    Ribeiro da Silva, Elias
    KTH, School of Industrial Engineering and Management (ITM), Industrial Economics and Management (Dept.). Pontifical Catholic Univeristy of Parana, Imaculada Conceição, 1155, 80215-901, Curitiba, Brazil.
    Angelis, Jannis
    KTH, School of Industrial Engineering and Management (ITM), Industrial Economics and Management (Dept.), Industrial Management. Research Institute of Industrial Economics, Grevgatan 34, SE-10215, Stockholm, Sweden.
    Managing Human Errors: Augmented Reality systems as a tool in the quality journey2018In: Procedia Manufacturing, E-ISSN 2351-9789, Vol. 28, p. 24-30Article in journal (Refereed)
    Abstract [en]

    The manufacturing industry is shifting, entering a new era with smart and connected devices. The fourth industrial revolution is promising increased growth and productivity by the Smart Factory and within the enabling technologies is Augmented Reality (AR). At the same time as the technology is introduced, errors in manufacturing are a problem which is affecting the productivity and the quality. This research aims to find the main causes of human errors in assembly lines and thereafter explores whether augmented reality is an appropriate tool to be used in order to address those issues. Based on a literature review that identified and characterized a preliminary set of root causes for human errors in assembly lines, these causes were empirically studied in an exercise that covered an in-depth case study in a multinational automotive company. Data in form of interviews and deviation- reports have been used to identify the causing factors and the result showed that the main causes of human errors are the amount of thinking, deciding and searching for information which affected the cognitive load of the operator and in result their performance. Several interviews with experts in augmented reality allowed to verify if AR technology would be feasible to solve or mitigate the found causes. Besides that, in repetitive manual assembly operations, AR is better used showing the process in order to train new operators. At the same time for experienced operators, AR should show information only when an error occurs and when there is a need of taking an active choice. Nevertheless, while theoretically able to managing human error when fully developed, the desired application makes the augmentation of visual objects redundant and increasingly complex for solving the identified causes of errors which questions the appropriateness of using AR systems. Furthermore, the empirical findings showed that for managing human errors, the main bottleneck of an AR system is the systems artificial intelligence capabilities.

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