Industry 4.0: The use of simulation for human reliability assessment

被引:25
作者
Angelopoulou, Anastasia [1 ]
Mykoniatis, Konstantinos [2 ]
Boyapati, Nithisha Reddy [1 ]
机构
[1] Columbus State Univ, Columbus, GA 31907 USA
[2] Auburn Univ, Auburn, AL 36849 USA
来源
INTERNATIONAL CONFERENCE ON INDUSTRY 4.0 AND SMART MANUFACTURING (ISM 2019) | 2020年 / 42卷
关键词
Industry; 4.0; Human reliability assessment; Simulation; System dynamics; SYSTEMS; FUTURE;
D O I
10.1016/j.promfg.2020.02.094
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
Nowadays, many manufacturers and organizations are successfully transitioning to Industry 4.0 by realizing the advantages of digitized manufacturing and adapting highly data-driven and automated processes, which enable them to deliver improved services and products to customers. Yet, the human factor seems not to be considered adequately in the Industry 4.0 processes. Neglecting the humans in a complex system such as the Industry 4.0 will have impacts on the system performance and the system's ability to function safely. This work explores the impact of human error on Industry 4.0 by considering the deployment of an anthropocentric approach, where human operators will make decisions, supported by cyber physical systems, rather than human work being dominated and determined by technology. More specifically, this work proposes a human reliability assessment simulation model that takes into consideration performance shaping factors that affect human work in a complex Industry 4.0 system. The simulation model allows quantifying human error in a given scenario. The model's functionality and the implementation of the human reliability assessment formulas were verified. Finally, we discuss future plans and steps to validate the model using a detailed case study in the Industry 4.0 context in order to draw conclusions about the impact of each performance shaping factor and their effect on the human error. (C) 2020 The Authors. Published by Elsevier B.V.
引用
收藏
页码:296 / 301
页数:6
相关论文
共 25 条
  • [1] Angelopoulou A, 2019, 2019 EUROPEAN MODELI
  • [2] Angelopoulou A, 2017, IN2017 IEEE C COGNIT, P27
  • [3] Angelopoulou A, 2015, THESIS U CENTRAL FLO
  • [4] UTASiMo: a simulation-based tool for task analysis
    Angelopoulou, Anastasia
    Mykoniatis, Konstantinos
    [J]. SIMULATION-TRANSACTIONS OF THE SOCIETY FOR MODELING AND SIMULATION INTERNATIONAL, 2018, 94 (01): : 43 - 54
  • [5] Blackman Harold S., 2008, Proceedings of the Human Factors and Ergonomics Society. 52nd Annual Meeting, P1733, DOI 10.1518/107118108X348774
  • [6] The origins of the SPAR-H method's performance shaping factor multipliers
    Boring, Ronald L.
    Blackman, Harold S.
    [J]. 2007 IEEE 8TH HUMAN FACTORS AND POWER PLANTS AND HPRCT 13TH ANNUAL MEETING, 2007, : 177 - 184
  • [7] De Felice F., 2012, IRACST-International Journal of Research in Management Technology (IJRMT), V2
  • [8] Tangible Industry 4.0: a scenario-based approach to learning for the future of production
    Erol, Selim
    Jaeger, Andreas
    Hold, Philipp
    Ott, Karl
    Sihn, Wilfried
    [J]. 6TH CIRP CONFERENCE ON LEARNING FACTORIES, 2016, 54 : 13 - 18
  • [9] Placing the operator at the centre of Industry 4.0 design: Modelling and assessing human activities within cyber-physical systems
    Fantini, Paola
    Pinzone, Marta
    Taisch, Marco
    [J]. COMPUTERS & INDUSTRIAL ENGINEERING, 2020, 139
  • [10] The future of employment: How susceptible are jobs to computerisation?
    Frey, Carl Benedikt
    Osborne, Michael A.
    [J]. TECHNOLOGICAL FORECASTING AND SOCIAL CHANGE, 2017, 114 : 254 - 280