Impacts of the COVID‑19 Pandemic and the Russia‑Ukraine Conflict on the Automotive Industry: A Data Analytics Perspective

This paper examines the combined impacts of the COVID‑19 pandemic and the Russia‑Ukraine conflict on the automotive industry using a data analytics perspective. We construct a synthetic dataset of automobile sales and vehicle attributes for 2018‑2024 and employ exploratory visuals and ordinary least squares modelling to assess how factors such as model year, manufacturer, entry price, seating capacity, engine size and emissions influence vehicle price. The analysis shows that newer model years, higher entry prices, larger engines and lower emissions significantly drive higher prices, while the pandemic and war periods show resilience but highlight supply chain disruptions and volatility. The paper situates the findings within current trends in AI‑enabled supply‑chain forecasting and discusses implications for manufacturers and policymakers. Charts and tables are provided to illustrate the time series trends, average price comparisons and regression results. The study concludes with recommendations for leveraging predictive analytics to enhance industry resilience.

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2025-08-10
DOI
https://doi.org/10.5281/zenodo.16788438
Primary Topic
Environmental and Biological Research in Conflict Zones
Type
article
Field-Weighted Citation Impact
0.00
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article

Impacts of the COVID‑19 Pandemic and the Russia‑Ukraine Conflict on the Automotive Industry: A Data Analytics Perspective

Zenodo (CERN European Organization for Nuclear Research)
Environmental and Biological Research in Conflict Zones
article

Impacts of the COVID‑19 Pandemic and the Russia‑Ukraine Conflict on the Automotive Industry: A Data Analytics Perspective

article en

Abstract

This paper examines the combined impacts of the COVID‑19 pandemic and the Russia‑Ukraine conflict on the automotive industry using a data analytics perspective. We construct a synthetic dataset of automobile sales and vehicle attributes for 2018‑2024 and employ exploratory visuals and ordinary least squares modelling to assess how factors such as model year, manufacturer, entry price, seating capacity, engine size and emissions influence vehicle price. The analysis shows that newer model years, higher entry prices, larger engines and lower emissions significantly drive higher prices, while the pandemic and war periods show resilience but highlight supply chain disruptions and volatility. The paper situates the findings within current trends in AI‑enabled supply‑chain forecasting and discusses implications for manufacturers and policymakers. Charts and tables are provided to illustrate the time series trends, average price comparisons and regression results. The study concludes with recommendations for leveraging predictive analytics to enhance industry resilience.

Zenodo (CERN European Organization for Nuclear Research)
Industry, innovation and infrastructure
Openalex Percentile: Top 44%
Environmental and Biological Research in Conflict Zones
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Impacts of the COVID‑19 Pandemic and the Russia‑Ukraine Conflict on the Automotive Industry: A Data Analytics Perspective · Zenodo (CERN European Organization for Nuclear Research) (2025) | TGRS Research Map | TGRS