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


  • Tine Bertoncel

    (University of Primorska, Slovenia)

  • Ivan Erenda

    (Group TPV, Slovenia)

  • Maja Meško

    (University of Primorska, Slovenia)


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.

Suggested Citation

  • Tine Bertoncel & Ivan Erenda & Maja Meško, 2018. "Best Practices. Managerial Early Warning System as Best Practice for Project Selection at a Smart Factory," The AMFITEATRU ECONOMIC journal, Academy of Economic Studies - Bucharest, Romania, vol. 20(49), pages 805-805, August.
  • Handle: RePEc:aes:amfeco:v:20:y:2018:i:49:p:805

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    References listed on IDEAS

    1. Bearden, J. Neil & Connolly, Terry, 2007. "Multi-attribute sequential search," Organizational Behavior and Human Decision Processes, Elsevier, vol. 103(1), pages 147-158, May.
    2. YU, Jie & Subramanian, Nachiappan & Ning, Kun & Edwards, David, 2015. "Product delivery service provider selection and customer satisfaction in the era of internet of things: A Chinese e-retailers’ perspective," International Journal of Production Economics, Elsevier, vol. 159(C), pages 104-116.
    3. repec:eee:eejocm:v:27:y:2018:i:c:p:74-87 is not listed on IDEAS
    4. Geiger, Niels, 2014. "The rise of behavioural economics: A quantitative assessment," Violette Reihe: Schriftenreihe des Promotionsschwerpunkts "Globalisierung und Beschäftigung" 44/2015, University of Hohenheim, Carl von Ossietzky University Oldenburg, Evangelisches Studienwerk.
    5. Wolfgang Pesendorfer, 2006. "Behavioral Economics Comes of Age: A Review Essay on Advances in Behavioral Economics," Journal of Economic Literature, American Economic Association, vol. 44(3), pages 712-721, September.
    6. Agor, Weston H., 1986. "How top executives use their intuition to make important decisions," Business Horizons, Elsevier, vol. 29(1), pages 49-53.
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    More about this item


    managerial early warning system; best practice; Industry 4.0; smart factories; satisficing and optimizing; Slovenia;

    JEL classification:

    • D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty
    • D91 - Microeconomics - - Micro-Based Behavioral Economics - - - Role and Effects of Psychological, Emotional, Social, and Cognitive Factors on Decision Making
    • O33 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Technological Change: Choices and Consequences; Diffusion Processes
    • L60 - Industrial Organization - - Industry Studies: Manufacturing - - - General


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