Acceptance Modelling in Product Development _ Case Study: Connected Systems for Industry 4.0 Solutions

2019 4th International Conference on Mechanical, Manufacturing, Modeling and Mechatronics;2019 4th International Conference on Design Engineering and Science

工业技术（总论）;机械制造

Zimmermann, V.^1 ; Heimicke, J.^1 ; Albers, A.^1 ; Reiß, N.^1

IPEK - Institute of Product Engineering, Karlsruhe Institute of Technology (KIT), Kaiserstraße 10, Karlsruhe

76131, Germany^1

关键词: Acceptance modelling;    Acceptance models;    Downstream process;    Laboratory and field studies;    Production process;    Production workers;    Research environment;    Working performance;   

Others  :  https://iopscience.iop.org/article/10.1088/1757-899X/520/1/012011/pdf
DOI  :  10.1088/1757-899X/520/1/012011

学科分类：工业工程学

来源: IOP

【 摘 要 】

Within Industry 4.0 many connected systems are entering production to optimize the processes. The integration of the manual workstations into a Smart Factory, as a fully connected production, is required as well. Therefore, systems with which the production workers interact as users are introduced. However, as a side-effect, the production employees and their working performance are more transparent as well. This often leads to a lack of user acceptance, even though these systems have positive effects on complete production processes. If, for unforeseen reasons, there is a lack of user acceptance, the introduced system will not be as successful as planned, irrespective of how technically valuable the future product is. Therefore, the potential user acceptance towards the System-in-Development should be understood. This contribution introduces an acceptance model that has been developed and validated within a Live-Lab with industrial participation, as a research environment between laboratory and field studies. For this purpose, influencing factors for user acceptance of connected products were identified. Based on various scientific surveys, the factors were weighted and their interactions analysed in order to enable prognosis regarding user acceptance and to derive recommendations concerning actions for the downstream processes.