TY - GEN
T1 - Making implicit knowledge explicit – Acquisition of plant staff’s mental models as a basis for developing a decision support system
AU - Pantförder, Dorothea
AU - Schaupp, Julia
AU - Vogel-Heuser, Birgit
N1 - Publisher Copyright:
© Springer International Publishing AG 2017.
PY - 2017
Y1 - 2017
N2 - Monitoring of industrial production plants is a complex task, which requires a hight level of knowledge about the interrelations in the production process in many cases. This knowledge on the one hand, is available as handbooks, process models or process data. On the other hand, the plant’s staff has implicit knowledge in the form of mental models. Experienced process engineers and operators have improved these mental models over years of working with the process. In this paper, a procedure is described, of how implicit knowledge can be made explicit by the acquisition of plant’s staff mental models. The aim is to build a cause-effect model for different quality parameters, which can be integrated into a decision support system (DSS), which helps the operator in decision-making.
AB - Monitoring of industrial production plants is a complex task, which requires a hight level of knowledge about the interrelations in the production process in many cases. This knowledge on the one hand, is available as handbooks, process models or process data. On the other hand, the plant’s staff has implicit knowledge in the form of mental models. Experienced process engineers and operators have improved these mental models over years of working with the process. In this paper, a procedure is described, of how implicit knowledge can be made explicit by the acquisition of plant’s staff mental models. The aim is to build a cause-effect model for different quality parameters, which can be integrated into a decision support system (DSS), which helps the operator in decision-making.
KW - Decision making
KW - Knowledge acquisition
KW - Mental model
KW - Plant manufacturing
UR - http://www.scopus.com/inward/record.url?scp=85025122124&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-58750-9_50
DO - 10.1007/978-3-319-58750-9_50
M3 - Conference contribution
AN - SCOPUS:85025122124
SN - 9783319587493
T3 - Communications in Computer and Information Science
SP - 358
EP - 365
BT - HCI International 2017 - Posters Extended Abstracts - 19th International Conference, HCI International 2017, Proceedings
A2 - Stephanidis, Constantine
PB - Springer Verlag
T2 - 19th International Conference on Human-Computer Interaction, HCI International 2017
Y2 - 9 July 2017 through 14 July 2017
ER -