Think Different

  Photo by RDNE Stock project on pexel Demand forecasting is a critical component of supply chain management. This solution uses historical data and machine learning algorithms to predict future demand. Data Requirements Historical sales data (3-5 years) Seasonal data (e.g., holidays, promotions) Product information (e.g., categories, subcategories) External data (e.g., weather, economic indicators) Data Preprocessing Data cleaning: Handle missing values, outliers, and data inconsistencies. Data transformation: Convert data into suitable formats for analysis. Feature engineering: Extract relevant features from data, such as: Time-based features (e.g., day of week, month) Seasonal features (e.g., holiday indicators) Product-based features (e.g., category, subcategory) Model Selection Choose a suitable algorithm based on data characteristics and performance metrics: Traditional methods: ARIMA (AutoRegressive Integrated Moving Average) Exponential Smoothing (ES) Naive Methods (e.g., m...