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

Author

Listed:
  • Tine Bertoncel

    (University of Primorska, Slovenia)

  • Ivan Erenda

    (Group TPV, Slovenia)

  • Maja Meško

    (University of Primorska, Slovenia)

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.

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

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    Citations

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    Cited by:

    1. Judit Oláh & Nemer Aburumman & József Popp & Muhammad Asif Khan & Hossam Haddad & Nicodemus Kitukutha, 2020. "Impact of Industry 4.0 on Environmental Sustainability," Sustainability, MDPI, vol. 12(11), pages 1-21, June.
    2. Dusan Gosnik, 2019. "Core Business Process Management and Company Performance," Management, University of Primorska, Faculty of Management Koper, vol. 14(1), pages 59-86.
    3. Vasja Roblek & Mirjana Pejic Bach & Maja Mesko & Tine Bertoncel, 2020. "Best Practices of the Social Innovations in the Framework of the E-Government Evolution," The AMFITEATRU ECONOMIC journal, Academy of Economic Studies - Bucharest, Romania, vol. 22(53), pages 275-275, February.

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    More about this item

    Keywords

    managerial early warning system; best practice; Industry 4.0; smart factories; satisficing and optimizing; Slovenia;
    All these keywords.

    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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