IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v17y2025i10p4429-d1654866.html
   My bibliography  Save this article

Forecasting Demand for Eco-Friendly Vehicles Using Machine Learning Technologies in the Era of Management 5.0

Author

Listed:
  • Serhii Kozlovskyi

    (Department of Entrepreneurship, Corporate and Spatial Economics, Vasyl Stus Donetsk National University, 21600 Vinnytsia, Ukraine)

  • Tetiana Kulinich

    (Department of Management of Organizations, Lviv Polytechnic National University, 79000 Lviv, Ukraine)

  • Marcin Duszyński

    (School of Business, National-Louis University, 33300 Nowy Sącz, Poland)

  • Taras Popovskyi

    (Department of Management and Behavioral Economics, Vasyl Stus Donetsk National University, 21600 Vinnytsia, Ukraine)

  • Tetiana Dluhopolska

    (Bohdan Havrylyshyn Education and Research Institute of International Relations, West Ukrainian National University, 46020 Ternopil, Ukraine)

  • Artur Kornatka

    (School of Business, National-Louis University, 33300 Nowy Sącz, Poland)

  • Yurii Popovskyi

    (Department of Marketing and Business Analytics, Vasyl Stus Donetsk National University, 21600 Vinnytsia, Ukraine)

Abstract

Management 5.0 represents a new paradigm in business strategy and leadership that integrates sustainability, advanced digital technologies, and human-centered decision-making. The article explores the application of machine learning technologies for forecasting demand for eco-friendly vehicles as a key tool for enhancing manufacturers’ competitiveness. This research supports key UN Sustainable Development Goals (SDGs), including SDG 7 (Clean Energy), SDG 9 (Innovation and Infrastructure), SDG 11 (Sustainable Cities), and SDG 12 (Responsible Consumption). Based on an analysis of the European market from 2019 to 2023 and forecasting through 2027, a comprehensive approach was developed using ARIMA, Prophet, and Random Forest models. Empirical findings indicate that implementing predictive analytics can reduce inventory costs by 18–25% and optimize working capital by 15–20%. Model performance varied by market type: Random Forest excelled in smaller markets, while Prophet delivered strong results in trend-stable environments. The results confirm that accurate demand forecasting, supported by machine learning technologies, creates significant competitive advantages in the era of management 5.0 through production process optimization and improved market positioning.

Suggested Citation

  • Serhii Kozlovskyi & Tetiana Kulinich & Marcin Duszyński & Taras Popovskyi & Tetiana Dluhopolska & Artur Kornatka & Yurii Popovskyi, 2025. "Forecasting Demand for Eco-Friendly Vehicles Using Machine Learning Technologies in the Era of Management 5.0," Sustainability, MDPI, vol. 17(10), pages 1-27, May.
    • Handle: RePEc:gam:jsusta:v:17:y:2025:i:10:p:4429-:d:1654866
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/17/10/4429/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/17/10/4429/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Grewal, Dhruv & Roggeveen, Anne L. & Nordfält, Jens, 2017. "The Future of Retailing," Journal of Retailing, Elsevier, vol. 93(1), pages 1-6.
    2. Rogge, Karoline S. & Reichardt, Kristin, 2016. "Policy mixes for sustainability transitions: An extended concept and framework for analysis," Research Policy, Elsevier, vol. 45(8), pages 1620-1635.
    3. Bronwyn H. Hall & Nathan Rosenberg (ed.), 2010. "Handbook of the Economics of Innovation," Handbook of the Economics of Innovation, Elsevier, edition 1, volume 1, number 1.
    4. Benjamin K. Sovacool & Mari Martiskainen & Andrew Hook & Lucy Baker, 2019. "Decarbonization and its discontents: a critical energy justice perspective on four low-carbon transitions," Climatic Change, Springer, vol. 155(4), pages 581-619, August.
    5. Alma Delia Torres-Rivera & Angel de Jesus Mc Namara Valdes & Rodrigo Florencio Da Silva, 2023. "The Resilience of the Renewable Energy Electromobility Supply Chain: Review and Trends," Sustainability, MDPI, vol. 15(14), pages 1-21, July.
    6. Babai, Zied & Boylan, John E. & Kolassa, Stephan & Nikolopoulos, Konstantinos, 2016. "Supply chain forecasting: Theory, practice, their gap and the futureAuthor-Name: Syntetos, Aris A," European Journal of Operational Research, Elsevier, vol. 252(1), pages 1-26.
    Full references (including those not matched with items on IDEAS)

    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. Graf, Holger & Kalthaus, Martin, 2018. "International research networks: Determinants of country embeddedness," Research Policy, Elsevier, vol. 47(7), pages 1198-1214.
    2. Martin Kalthaus, 2020. "Knowledge recombination along the technology life cycle," Journal of Evolutionary Economics, Springer, vol. 30(3), pages 643-704, July.
    3. Josie Coburn & Frederique Bone & Andy C. Stirling & Michael M. Hopkins & Jorge Mestre-Ferrandiz & Stathis Arapostathis & Martin J. Llewelyn, 2021. "Appraising research policy instrument mixes: a multicriteria mapping study in six European countries of diagnostic innovation to manage antimicrobial resistance," SPRU Working Paper Series 2021-03, SPRU - Science Policy Research Unit, University of Sussex Business School.
    4. Kurz, Michael & Kleimeier, Stefanie, 2019. "Credit Supply: Are there negative spillovers from banks’ proprietary trading? (RM/19/005-revised-)," Research Memorandum 026, Maastricht University, Graduate School of Business and Economics (GSBE).
    5. Atal, Vidya & Bar, Talia & Gordon, Sidartha, 2016. "Project selection: Commitment and competition," Games and Economic Behavior, Elsevier, vol. 96(C), pages 30-48.
    6. Cowling, Marc & Ughetto, Elisa & Lee, Neil, 2018. "The innovation debt penalty: Cost of debt, loan default, and the effects of a public loan guarantee on high-tech firms," Technological Forecasting and Social Change, Elsevier, vol. 127(C), pages 166-176.
    7. Jan Fagerberg & Martin Srholec, 2017. "Global Dynamics, Capabilities and the Crisis," Economic Complexity and Evolution, in: Andreas Pyka & Uwe Cantner (ed.), Foundations of Economic Change, pages 83-106, Springer.
    8. Boncinelli, Fabio & Bartolini, Fabio & Casini, Leonardo, 2018. "Structural factors of labour allocation for farm diversification activities," Land Use Policy, Elsevier, vol. 71(C), pages 204-212.
    9. Ufuk Akcigit & Douglas Hanley & Stefanie Stantcheva, 2022. "Optimal Taxation and R&D Policies," Econometrica, Econometric Society, vol. 90(2), pages 645-684, March.
    10. John M. de Figueiredo & Brian S. Silverman, 2017. "On the Genesis of Interfirm Relational Contracts," Strategy Science, INFORMS, vol. 2(4), pages 234-245, December.
    11. Christian Rammer & Gastón P Fernández & Dirk Czarnitzki, 2021. "Artificial Intelligence and Industrial Innovation: Evidence from Firm-Level Data," Working Papers of Department of Economics, Leuven 674605, KU Leuven, Faculty of Economics and Business (FEB), Department of Economics, Leuven.
    12. Banal-Estañol, Albert & Duso, Tomaso & Seldeslachts, Jo & Szücs, Florian, 2022. "R&D Spillovers through RJV Cooperation," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 51(4), pages 1-10.
    13. Anne Marie Knott, 2016. "Outsourced R&D and GDP Growth," Working Papers 16-19, Center for Economic Studies, U.S. Census Bureau.
    14. Yang Li & Yuanzhu Wang & Rajah Rasiah, 2023. "Research on the Influence of Tax Incentives and Financing Constraints on NEEQ Enterprises’ Innovation," Sustainability, MDPI, vol. 15(3), pages 1-19, February.
    15. Ngui Min Fui Tom, 2020. "Crashed! Why Drone Delivery Is Another Tech Idea not Ready to Take Off," International Business Research, Canadian Center of Science and Education, vol. 13(7), pages 251-251, July.
    16. Matteo Aquilina & Giulio Cornelli & Marina Sanchez del Villar, 2024. "Regulation, information asymmetries and the funding of new ventures," BIS Working Papers 1162, Bank for International Settlements.
    17. Mark Knell & Simone Vannuccini, 2022. "Tools and concepts for understanding disruptive technological change after Schumpeter," Jena Economics Research Papers 2022-005, Friedrich-Schiller-University Jena.
    18. Pietro Moncada-Paternò-Castello, 2022. "Top R&D investors, structural change and the R&D growth performance of young and old firms," Eurasian Business Review, Springer;Eurasia Business and Economics Society, vol. 12(1), pages 1-33, March.
    19. Caloffi, Annalisa & Freo, Marzia & Ghinoi, Stefano & Mariani, Marco & Rossi, Federica, 2022. "Assessing the effects of a deliberate policy mix: The case of technology and innovation advisory services and innovation vouchers," Research Policy, Elsevier, vol. 51(6).
    20. Antonelli, Cristiano, 2017. "Digital knowledge generation and the appropriability trade-off," Telecommunications Policy, Elsevier, vol. 41(10), pages 991-1002.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;
    ;

    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:gam:jsusta:v:17:y:2025:i:10:p:4429-:d:1654866. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.