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

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dc.contributor.authorSerhii Kozlovskyi
dc.contributor.authorTetiana Kulinich
dc.contributor.authorTaras Popovskyi
dc.contributor.authorTetiana Dluhopolska
dc.contributor.authorArtur Kornatka
dc.contributor.authorYurii Popovskyi
dc.date.accessioned2026-01-05T11:06:48Z
dc.date.available2026-01-05T11:06:48Z
dc.date.issued2025-05-13
dc.descriptionСтаття у Міжнародному рецензованому журналі з відкритим доступом Sustainability 2025, 17, що видається онлайн два рази на місяць MDPI
dc.description.abstractManagement 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.
dc.identifier.urihttps://doi.org/10.3390/su17104429
dc.identifier.urihttps://r2.donnu.edu.ua/handle/123456789/4098
dc.language.isoen
dc.publisherMDPI AG Switzerland
dc.relation.ispartofseriesSustainability; Том 3 (62), 2025, с. 154-159
dc.subjectdemand forecastingen
dc.subjecteco-friendly transporten
dc.subjectmachine learningen
dc.subjecthybrid modelen
dc.subjectsupply chain sustainabilityen
dc.subjectmanagement 5.0en
dc.titleForecasting Demand for Eco-Friendly Vehicles Using Machine Learning Technologies in the Era of Management 5.0
dc.typeArticle
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