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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
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    References listed on IDEAS

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