1. Introduction to Industry 4.0 and its impact on the automotive sector.
2. Explanation of the challenges faced by traditional supply chain management in the automotive industry.
3. Importance of supply chain visibility and risk mitigation for BMW USA in the context of car sales forecasting.
4. Objectives of the project: to integrate Industry 4.0 technologies with time series forecasting to improve supply chain visibility and mitigate risks in BMW USA car sales.
5. Significance of accurate sales forecasting for BMW USA to optimize operations and increase profitability.
6. Discussion on the potential risks affecting BMW’s supply chain, including market movements, geopolitical factors, and uncertainties.
7. Overview of traditional forecasting methodologies: Naïve, Moving Average, Exponential Smoothing, Regression.
8. Introduction to modern machine learning models for forecasting: Prophet and Auto-Regressive Integrated Moving Average (ARIMA).
9. Explanation of the methodology: application of traditional and modern forecasting techniques to BMW USA car sales data from 2017 to 2023.
10. Importance of comparative analysis between traditional and modern forecasting methods.
11. Description of data sources and collection methods for BMW USA car sales data.
12. Explanation of how the selected forecasting techniques will be applied to the dataset.
13. Discussion on the expected results: forecasted BMW USA car sales for the first quarter of 2024.
14. Analysis of the performance of traditional forecasting methods compared to modern machine learning models.
15. Conclusion and recommendations for BMW USA to enhance supply chain visibility and mitigate risks in car sales forecasting through the integration of Industry 4.0 technologies and advanced forecasting techniques.
https://www.goodcarbadcar.net/bmw-us-sales-figures/ car sales data take quarterly
plagiarism should be less than 12%
write in below format for Time Series Forecasting for bmw usa and add Industry 4.0 and Supply Chain Visibility & Risk Mitigation
Abstract
Introduction
Review of Concepts
- Naive Method
- Moving Average
- Exponential Smoothing
- Regression
- Prophet
- Auto-Regressive Integrated Moving Average (ARIMA)
Methodology
- Data collection
- Methods & Observation
- Naive Method
- Moving Average
- Exponential Smoothing
- Regression
- Prophet
- Auto-Regressive Integrated Moving Average (ARIMA)
Analysis of Results
Conclusion
References
Appendix