Demand Forecasting Using Different Methods in Python

This project explores the application of advanced methods for demand forecasting in supply chain management. With the increasing complexity and volatility of global markets, accurate demand forecasting is crucial for optimizing inventory management, production planning, and overall supply chain efficiency. Traditional forecasting techniques often struggle to capture the nonlinear and dynamic nature of demand patterns, especially in highly unpredictable environments. In response, this project investigates the effectiveness of various deep learning algorithms, including SARIMA, Prophet, and LSTM, in predicting future demand with high accuracy and reliability. Through detailed experimentation and analysis, the project aims to identify the most suitable deep learning approach for different scenarios and industries. The outcomes of this research have the potential to revolutionize supply chain dynamics by providing decision-makers with actionable insights for proactive inventory management and improved operational efficiency. Additionally, the project successfully reduces forecasting error rates from around 30% to 18.64%, showcasing the significant impact of SARIMA techniques on enhancing forecasting accuracy.