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Sustainability
Volume 17
Issue 10
10.3390/su17104429
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This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Article

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

by
Serhii Kozlovskyi
1,
Tetiana Kulinich
2,
Marcin Duszyński
3,
Taras Popovskyi
4,
Tetiana Dluhopolska
5,*,
Artur Kornatka
3 and
Yurii Popovskyi
6
1
Department of Entrepreneurship, Corporate and Spatial Economics, Vasyl’ Stus Donetsk National University, 21600 Vinnytsia, Ukraine
2
Department of Management of Organizations, Lviv Polytechnic National University, 79000 Lviv, Ukraine
3
School of Business, National-Louis University, 33300 Nowy Sącz, Poland
4
Department of Management and Behavioral Economics, Vasyl Stus Donetsk National University, 21600 Vinnytsia, Ukraine
5
Bohdan Havrylyshyn Education and Research Institute of International Relations, West Ukrainian National University, 46020 Ternopil, Ukraine
6
Department of Marketing and Business Analytics, Vasyl Stus Donetsk National University, 21600 Vinnytsia, Ukraine
*
Author to whom correspondence should be addressed.
Sustainability 2025, 17(10), 4429; https://doi.org/10.3390/su17104429
Submission received: 7 April 2025 / Revised: 3 May 2025 / Accepted: 6 May 2025 / Published: 13 May 2025
(This article belongs to the Special Issue Management for Sustainable Future: Challenges, Innovations and Organizational Performance)
Versions Notes

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.
Keywords: demand forecasting; eco-friendly transport; machine learning; hybrid model; supply chain sustainability; Management 5.0 demand forecasting; eco-friendly transport; machine learning; hybrid model; supply chain sustainability; Management 5.0

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MDPI and ACS Style

Kozlovskyi, S.; Kulinich, T.; Duszyński, M.; Popovskyi, T.; Dluhopolska, T.; Kornatka, A.; Popovskyi, Y. Forecasting Demand for Eco-Friendly Vehicles Using Machine Learning Technologies in the Era of Management 5.0. Sustainability 2025, 17, 4429. https://doi.org/10.3390/su17104429

AMA Style

Kozlovskyi S, Kulinich T, Duszyński M, Popovskyi T, Dluhopolska T, Kornatka A, Popovskyi Y. Forecasting Demand for Eco-Friendly Vehicles Using Machine Learning Technologies in the Era of Management 5.0. Sustainability. 2025; 17(10):4429. https://doi.org/10.3390/su17104429

Chicago/Turabian Style

Kozlovskyi, Serhii, Tetiana Kulinich, Marcin Duszyński, Taras Popovskyi, Tetiana Dluhopolska, Artur Kornatka, and Yurii Popovskyi. 2025. "Forecasting Demand for Eco-Friendly Vehicles Using Machine Learning Technologies in the Era of Management 5.0" Sustainability 17, no. 10: 4429. https://doi.org/10.3390/su17104429

APA Style

Kozlovskyi, S., Kulinich, T., Duszyński, M., Popovskyi, T., Dluhopolska, T., Kornatka, A., & Popovskyi, Y. (2025). Forecasting Demand for Eco-Friendly Vehicles Using Machine Learning Technologies in the Era of Management 5.0. Sustainability, 17(10), 4429. https://doi.org/10.3390/su17104429

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