IDEAS home Printed from https://ideas.repec.org/a/aes/amfeco/v20y2018i49p805.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: http://www.amfiteatrueconomic.ro/temp/Article_2758.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. González-Valdés, Felipe & Ortúzar, Juan de Dios, 2018. "The Stochastic Satisficing model: A bounded rationality discrete choice model," Journal of choice modelling, Elsevier, vol. 27(C), pages 74-87.
    3. 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.
    4. 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.
    5. Bearden, J. Neil & Connolly, Terry, 2007. "Multi-attribute sequential search," Organizational Behavior and Human Decision Processes, Elsevier, vol. 103(1), pages 147-158, May.
    6. J. Neil Bearden & Terry Connolly, 2008. "On Optimal Satisficing: How Simple Policies Can Achieve Excellent Results," Springer Optimization and Its Applications, in: Tamar Kugler & J. Cole Smith & Terry Connolly & Young-Jun Son (ed.), Decision Modeling and Behavior in Complex and Uncertain Environments, pages 79-97, Springer.
    7. Agor, Weston H., 1986. "How top executives use their intuition to make important decisions," Business Horizons, Elsevier, vol. 29(1), pages 49-53.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Dusan Gosnik, 2019. "Core Business Process Management and Company Performance," Management, University of Primorska, Faculty of Management Koper, vol. 14(1), pages 59-86.
    2. 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.
    3. 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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. repec:aud:audfin:v:20:y:2018:i:49:p:805 is not listed on IDEAS
    2. Dorian Jullien, 2018. "Under Risk, Over Time, Regarding Other People: Language and Rationality within Three Dimensions," Research in the History of Economic Thought and Methodology, in: Including a Symposium on Latin American Monetary Thought: Two Centuries in Search of Originality, volume 36, pages 119-155, Emerald Group Publishing Limited.
    3. Dorian Jullien, 2016. "Under Uncertainty, Over Time and Regarding Other People: Rationality in 3D," GREDEG Working Papers 2016-20, Groupe de REcherche en Droit, Economie, Gestion (GREDEG CNRS), Université Côte d'Azur, France.
    4. Chun, Young H., 2015. "Multi-attribute sequential decision problem with optimizing and satisficing attributes," European Journal of Operational Research, Elsevier, vol. 243(1), pages 224-232.
    5. Gowdy, John M., 2007. "Toward an experimental foundation for benefit-cost analysis," Ecological Economics, Elsevier, vol. 63(4), pages 649-655, September.
    6. David Laibson, 1997. "Golden Eggs and Hyperbolic Discounting," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 112(2), pages 443-478.
    7. Shastitko, Andrey & Golovanova, Svetlana, 2016. "Meeting blindly… Is Austrian economics useful for dynamic capabilities theory?," Russian Journal of Economics, Elsevier, vol. 2(1), pages 86-110.
    8. Huang, Yanliu & Hutchinson, J. Wesley, 2013. "The roles of planning, learning, and mental models in repeated dynamic decision making," Organizational Behavior and Human Decision Processes, Elsevier, vol. 122(2), pages 163-176.
    9. Vadim Kufenko & Niels Geiger, 2016. "Business cycles in the economy and in economics: an econometric analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 107(1), pages 43-69, April.
    10. Nicolas Sirven & Thomas Barnay, 2017. "Expectations, loss aversion and retirement decisions in the context of the 2009 crisis in Europe," International Journal of Manpower, Emerald Group Publishing Limited, vol. 38(1), pages 25-44, April.
    11. David Dillenberger & Andrew Postlewaite & Kareen Rozen, 2013. "Optimism and Pessimism with Expected Utility, Fifth Version," PIER Working Paper Archive 15-009, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania, revised 23 Feb 2015.
    12. Stephen Martin, 2018. "Behavioral antitrust," Chapters, in: Victor J. Tremblay & Elizabeth Schroeder & Carol Horton Tremblay (ed.), Handbook of Behavioral Industrial Organization, chapter 15, pages 404-454, Edward Elgar Publishing.
    13. Li, Ying & Dai, Jing & Cui, Li, 2020. "The impact of digital technologies on economic and environmental performance in the context of industry 4.0: A moderated mediation model," International Journal of Production Economics, Elsevier, vol. 229(C).
    14. Claudio Campanale & Rui Castro & Gian Luca Clementi, 2010. "Asset Pricing in a Production Economy with Chew-Dekel Preferences," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 13(2), pages 379-402, April.
    15. Park, Hyeon, 2019. "Inter-temporal choices with temporal reference dependence," Research in Economics, Elsevier, vol. 73(1), pages 107-122.
    16. David Dillenberger & Andrew Postlewaite & Kareen Rozen, 2017. "Optimism and Pessimism with Expected Utility," Journal of the European Economic Association, European Economic Association, vol. 15(5), pages 1158-1175.
    17. Nicolas Brisset & Dorian Jullien, 2019. "Models as Speech Acts: A Restatement and a new Case Study," GREDEG Working Papers 2019-09, Groupe de REcherche en Droit, Economie, Gestion (GREDEG CNRS), Université Côte d'Azur, France.
    18. Vasja Roblek & Maja Meško & Alojz Krapež, 2016. "A Complex View of Industry 4.0," SAGE Open, , vol. 6(2), pages 21582440166, June.
    19. Beltagui, Ahmad & Kunz, Nathan & Gold, Stefan, 2020. "The role of 3D printing and open design on adoption of socially sustainable supply chain innovation," International Journal of Production Economics, Elsevier, vol. 221(C).
    20. Hugh Kelley & Tom Evans, 2010. "Measuring the Impact of Behavioral Traders in the Market for Closed-end Country Funds from 2002 to 2009," Chapters, in: Brian Bruce (ed.), Handbook of Behavioral Finance, chapter 16, Edward Elgar Publishing.
    21. Sanjeev Yadav & Sunil Luthra & Dixit Garg, 2022. "Internet of things (IoT) based coordination system in Agri-food supply chain: development of an efficient framework using DEMATEL-ISM," Operations Management Research, Springer, vol. 15(1), pages 1-27, June.

    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

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:aes:amfeco:v:20:y:2018:i:49:p:805. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Valentin Dumitru (email available below). General contact details of provider: https://edirc.repec.org/data/aseeero.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.