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The Contribution of Digital Technology to the Forecasting of Supply Chain Development, in IT Products, Modeling and Simulation of the Problem

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  • Dimitrios K. Nasiopoulos

    (BICTEVAC Business Information and Communication Technologies in Value Chains Laboratory, Department of Agribusiness and Supply Chain Management, School of Applied Economics and Social Sciences, Agricultural University of Athens, 118 55 Athens, Greece)

  • Dimitrios M. Mastrakoulis

    (BICTEVAC Business Information and Communication Technologies in Value Chains Laboratory, Department of Agribusiness and Supply Chain Management, School of Applied Economics and Social Sciences, Agricultural University of Athens, 118 55 Athens, Greece)

  • Dimitrios A. Arvanitidis

    (BICTEVAC Business Information and Communication Technologies in Value Chains Laboratory, Department of Agribusiness and Supply Chain Management, School of Applied Economics and Social Sciences, Agricultural University of Athens, 118 55 Athens, Greece)

Abstract

Aiming for the forecasting and predictability of their future development, corporations have developed appropriate strategies as a result of the necessity to optimize the distribution networks of new IT products over time. The necessity of diversifying manufacturing was brought on by the fierce competition between businesses and the sophisticated consumer demand trends for personalized items. For businesses looking to create more effective distribution networks for their products, mass adaptability may be advantageous. Fuzzy cognitive mapping (FCM), associations developed from web analytics data, and simulation results based on dynamic and agent-based simulation models work together to practically aid digital marketing experts, decision-makers and analysts in offering answers to their corresponding problems. In order to apply the measures in agent-based modeling, the current work is based on the gathering of web analysis data over a predetermined time period, as well as on identifying the influence correlations between measurements.

Suggested Citation

  • Dimitrios K. Nasiopoulos & Dimitrios M. Mastrakoulis & Dimitrios A. Arvanitidis, 2022. "The Contribution of Digital Technology to the Forecasting of Supply Chain Development, in IT Products, Modeling and Simulation of the Problem," Forecasting, MDPI, vol. 4(4), pages 1-19, November.
  • Handle: RePEc:gam:jforec:v:4:y:2022:i:4:p:55-1037:d:988084
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    References listed on IDEAS

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    1. Deighton, John & Kornfeld, Leora, 2009. "Interactivity's Unanticipated Consequences for Marketers and Marketing," Journal of Interactive Marketing, Elsevier, vol. 23(1), pages 4-10.
    2. Sigitas Davidavičius & Tadas Limba, 2022. "Recognition of Digital Content Needs for Inbound Marketing Solutions," Social Sciences, MDPI, vol. 11(8), pages 1-11, August.
    3. Jorge M. Silva-Risso & Randolph E. Bucklin & Donald G. Morrison, 1999. "A Decision Support System for Planning Manufacturers' Sales Promotion Calendars," Marketing Science, INFORMS, vol. 18(3), pages 274-300.
    4. Martin Natter & Andreas Mild & Udo Wagner & Alfred Taudes, 2008. "—Planning New Tariffs at tele.ring: The Application and Impact of an Integrated Segmentation, Targeting, and Positioning Tool," Marketing Science, INFORMS, vol. 27(4), pages 600-609, 07-08.
    5. Damianos P. Sakas & Dimitrios K. Nasiopoulos & Panagiotis Reklitis, 2019. "Modeling and Simulation of the Strategic Use of Marketing in Search Engines for the Business Success of High Technology Companies," Springer Proceedings in Business and Economics, in: Damianos P. Sakas & Dimitrios K. Nasiopoulos (ed.), Strategic Innovative Marketing, pages 217-226, Springer.
    6. An, Li, 2012. "Modeling human decisions in coupled human and natural systems: Review of agent-based models," Ecological Modelling, Elsevier, vol. 229(C), pages 25-36.
    7. Germann, Frank & Lilien, Gary L. & Rangaswamy, Arvind, 2013. "Performance implications of deploying marketing analytics," International Journal of Research in Marketing, Elsevier, vol. 30(2), pages 114-128.
    8. Damianos P. Sakas & Dimitrios K. Nasiopoulos & Panagiotis Reklitis, 2019. "Modeling and Simulation of the Strategic Use of the Internet Forum Aiming at Business Success of High-Technology Companies," Springer Proceedings in Business and Economics, in: Damianos P. Sakas & Dimitrios K. Nasiopoulos (ed.), Strategic Innovative Marketing, pages 173-183, Springer.
    9. Dimitrios Κ. Nasiopoulos & Damianos P. Sakas & Panagiotis Trivellas, 2021. "The Role of Digital Marketing in the Development of a Distribution and Logistics Network of Information Technology Companies," Springer Proceedings in Business and Economics, in: Damianos P. Sakas & Dimitrios K. Nasiopoulos & Yulia Taratuhina (ed.), Business Intelligence and Modelling, pages 267-276, Springer.
    10. P. K. Kannan & Barbara Kline Pope & Sanjay Jain, 2009. "—Pricing Digital Content Product Lines: A Model and Application for the National Academies Press," Marketing Science, INFORMS, vol. 28(4), pages 620-636, 07-08.
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