Best Practices. Managerial Early Warning System as Best Practice for Project Selection at a Smart Factory

Tine Bertoncel, Ivan Erenda and Maja Meško
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Tine Bertoncel: University of Primorska, Slovenia
Ivan Erenda: Group TPV, Slovenia
Maja Meško: University of Primorska, Slovenia

The AMFITEATRU ECONOMIC journal, 2018, vol. 20, issue 49, 805

Abstract: The purpose of the paper is to contribute to the development of best practices at emerging factories of the future, i.e. smart factories of Industry 4.0. Smart factories need to develop effective managerial early warning systems to identify and respond to subtle threats or opportunities, i.e. weak signals, in order to adapt to an ever-changing environment in a timely manner and thus gain or maintain a competitive advantage on the market. These factories need to develop and implement a several-stage early warning system that is specific to their industry. The aim of our study is, with the help of semi-structured group interviews, to examine which stages of a managerial early warning system are present in the case of a global innovative supplier in the automotive industry. As such, a four-stage managerial early warning system model for a knowledge-based automotive smart factory is proposed, in which aggregate activities and management decision-making strategies are defined for each stage, with the importance of intuition being taken into consideration. We found that managers rely on intuition and extensive analysis for satisficing strategies and teamwork for optimizing strategies, when using their managerial early warning system.

Keywords: managerial early warning system; best practice; Industry 4.0; smart factories; satisficing and optimizing; Slovenia (search for similar items in EconPapers)
JEL-codes: D81 D91 L60 O33 (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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