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A Managerial Early Warning System at a Smart Factory: An Intuitive Decision‐making Perspective
Author(s) -
Bertoncel Tine,
Erenda Ivan,
Bach Mirjana Pejić,
Roblek Vasja,
Meško Maja
Publication year - 2018
Publication title -
systems research and behavioral science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.371
H-Index - 45
eISSN - 1099-1743
pISSN - 1092-7026
DOI - 10.1002/sres.2542
Subject(s) - intuition , automotive industry , warning system , early warning system , factory (object oriented programming) , perspective (graphical) , computer science , process management , context (archaeology) , risk analysis (engineering) , business , knowledge management , order (exchange) , operations management , engineering , psychology , artificial intelligence , cognitive science , telecommunications , paleontology , finance , biology , programming language , aerospace engineering
Early warning systems are becoming increasingly important in high‐risk industries, because of their potential to detect all kinds of subtle threats and opportunities, that is, weak signals, in order to avoid strategic surprises. However, it is an under‐researched area within the context of smart factories. For the purpose of the study, semi‐structured group interviews were used to investigate how managers at a smart factory, a highly innovative global supplier in the automotive industry sense weak signals, perceive the role of intuition, smart systems and business model adjustments. The results of the study show that managers perceive early warning systems as highly important for timely response to development changes and that both smart systems and intuition play an essential role in detecting and responding to weak signals. Based on this study, we propose a managerial early warning system model with four stages, namely, identifying, screening, appraising and responding to weak signs, within the context of a smart factory. © 2018 John Wiley & Sons, Ltd.