2024
Fan, Xiaotong; Valilai, Omid Fatahi; Wicaksono, Hendro
Integrating Economic, Technological, and Consumer Factors for Enhanced Accuracy in Electric Vehicle Demand Forecasting: A Case Study in Germany Proceedings Article
In: Lecture Notes in Mechanical Engineering (LNME): Flexible Automation and Intelligent Manufacturing: Manufacturing Innovation and Preparedness for the Changing World Order , Springer, 2024.
Abstract | Links | BibTeX | Tags: automotive industry, demand forecasting, machine learning, supply chain management, sustainability, timeseries analysis
@inproceedings{nokey,
title = {Integrating Economic, Technological, and Consumer Factors for Enhanced Accuracy in Electric Vehicle Demand Forecasting: A Case Study in Germany},
author = {Xiaotong Fan and Omid Fatahi Valilai and Hendro Wicaksono
},
doi = {https://doi.org/10.1007/978-3-031-74485-3_50},
year = {2024},
date = {2024-12-13},
booktitle = {Lecture Notes in Mechanical Engineering (LNME): Flexible Automation and Intelligent Manufacturing: Manufacturing Innovation and Preparedness for the Changing World Order },
publisher = {Springer},
abstract = {The development of the electric vehicle industry has the potential to reduce CO
emissions significantly and overcome energy supply challenges. However, manufacturing electric cars is a complex process consisting of procurement, logistics, and assembly. Accurate demand forecasting plays an essential role in the industry’s long-term development because it effectively fulfills customer needs while mitigating the risks of overproduction. Forecasting electric vehicle demand presents a significant challenge due to limited data availability and multiple factors influencing it. Comprehensive research integrating economic, technological, and consumer dynamics into demand forecasts remains notably deficient. The main goal of this research is to develop demand forecasting of electric vehicles within Germany, considering those factors, including variables like gasoline prices, the count of installed charging stations, and the Google search index. The research involves experimentation with various models, including Prophet, SARIMA, and their variants, incorporating different combinations of exogenous variables. The results demonstrate that SARIMA and its variants outperform other models regarding predictive accuracy. This research equips electric vehicle manufacturing companies with invaluable insights into market trends and the potential impact of diverse influencing variables. With this knowledge, companies can adapt their production strategies to align with market dynamics, enhancing their competitiveness in the rapidly evolving electric vehicle landscape.},
keywords = {automotive industry, demand forecasting, machine learning, supply chain management, sustainability, timeseries analysis},
pubstate = {published},
tppubtype = {inproceedings}
}
The development of the electric vehicle industry has the potential to reduce CO
emissions significantly and overcome energy supply challenges. However, manufacturing electric cars is a complex process consisting of procurement, logistics, and assembly. Accurate demand forecasting plays an essential role in the industry’s long-term development because it effectively fulfills customer needs while mitigating the risks of overproduction. Forecasting electric vehicle demand presents a significant challenge due to limited data availability and multiple factors influencing it. Comprehensive research integrating economic, technological, and consumer dynamics into demand forecasts remains notably deficient. The main goal of this research is to develop demand forecasting of electric vehicles within Germany, considering those factors, including variables like gasoline prices, the count of installed charging stations, and the Google search index. The research involves experimentation with various models, including Prophet, SARIMA, and their variants, incorporating different combinations of exogenous variables. The results demonstrate that SARIMA and its variants outperform other models regarding predictive accuracy. This research equips electric vehicle manufacturing companies with invaluable insights into market trends and the potential impact of diverse influencing variables. With this knowledge, companies can adapt their production strategies to align with market dynamics, enhancing their competitiveness in the rapidly evolving electric vehicle landscape.
emissions significantly and overcome energy supply challenges. However, manufacturing electric cars is a complex process consisting of procurement, logistics, and assembly. Accurate demand forecasting plays an essential role in the industry’s long-term development because it effectively fulfills customer needs while mitigating the risks of overproduction. Forecasting electric vehicle demand presents a significant challenge due to limited data availability and multiple factors influencing it. Comprehensive research integrating economic, technological, and consumer dynamics into demand forecasts remains notably deficient. The main goal of this research is to develop demand forecasting of electric vehicles within Germany, considering those factors, including variables like gasoline prices, the count of installed charging stations, and the Google search index. The research involves experimentation with various models, including Prophet, SARIMA, and their variants, incorporating different combinations of exogenous variables. The results demonstrate that SARIMA and its variants outperform other models regarding predictive accuracy. This research equips electric vehicle manufacturing companies with invaluable insights into market trends and the potential impact of diverse influencing variables. With this knowledge, companies can adapt their production strategies to align with market dynamics, enhancing their competitiveness in the rapidly evolving electric vehicle landscape.
2023
Beibit, Rauan; Valilai, Omid Fatahi; Wicaksono, Hendro
Estimating the COVID-19 Impact on the Semiconductor Shortage in the European Automotive Industry using Supervised Machine Learning Proceedings Article
In: Proceedings of the 2023 10th International Conference on Industrial Engineering and Applications, pp. 302–308, 2023.
Abstract | Links | BibTeX | Tags: machine learning, supply chain management, timeseries analysis
@inproceedings{beibit2023estimating,
title = {Estimating the COVID-19 Impact on the Semiconductor Shortage in the European Automotive Industry using Supervised Machine Learning},
author = {Rauan Beibit and Omid Fatahi Valilai and Hendro Wicaksono},
doi = {https://doi.org/10.1145/3587889.3588215},
year = {2023},
date = {2023-06-09},
urldate = {2023-01-01},
booktitle = {Proceedings of the 2023 10th International Conference on Industrial Engineering and Applications},
pages = {302–308},
abstract = {The COVID-19 pandemic impacted different industrial sectors. It causes semiconductor shortage and, subsequently, on the industries downstream, such as the automotive industry. It is because of factory shutdown, increasing consumer electronic demands due to working from home, shifted focus of companies to consumer electronics, and limited logistic capacity. This research aims to analyze the influencing factors and estimate the extent of the impact of COVID-19 on the semiconductor and automotive industry in Europe using machine learning. We developed five regression models to predict the semiconductor sales and number of new passenger car registrations that reflect the development of sales in the automotive industry. Our research revealed that random forest regression is the best machine learning model for analyzing the relationship between COVID-19, semiconductor sales, and passenger car registrations. However, overall, our research found that the COVID-19 pandemic is not the only factor that impacts the semiconductor shortage in the automotive industry. The geopolitical landscape and the world’s reliance on Chinese exports are also essential influencing factors in many supply chains, including in the semiconductor and automotive sectors.
},
keywords = {machine learning, supply chain management, timeseries analysis},
pubstate = {published},
tppubtype = {inproceedings}
}
The COVID-19 pandemic impacted different industrial sectors. It causes semiconductor shortage and, subsequently, on the industries downstream, such as the automotive industry. It is because of factory shutdown, increasing consumer electronic demands due to working from home, shifted focus of companies to consumer electronics, and limited logistic capacity. This research aims to analyze the influencing factors and estimate the extent of the impact of COVID-19 on the semiconductor and automotive industry in Europe using machine learning. We developed five regression models to predict the semiconductor sales and number of new passenger car registrations that reflect the development of sales in the automotive industry. Our research revealed that random forest regression is the best machine learning model for analyzing the relationship between COVID-19, semiconductor sales, and passenger car registrations. However, overall, our research found that the COVID-19 pandemic is not the only factor that impacts the semiconductor shortage in the automotive industry. The geopolitical landscape and the world’s reliance on Chinese exports are also essential influencing factors in many supply chains, including in the semiconductor and automotive sectors.