Demand Forecasting

Demand forecasting refers to techniques and technologies that use past sales data, market trends, and seasonal fluctuations to predict the future demand for products or services. Leveraging machine learning, statistical analysis, and AI enables higher-accuracy forecasting, which is used to optimize inventory management, production planning, and sales strategy. Demand forecasting is used across a wide range of industries—retail, manufacturing, logistics, e-commerce, and energy management—contributing to reduced risk of excess inventory or stockouts, cost savings, and improved customer satisfaction. Representative forecasting models and approaches include: • Time series analysis (ARIMA, seasonal adjustment) • Regression analysis • Machine learning models (random forest, XGBoost, etc.) • Deep learning models (LSTM, Transformer-based models) • Dynamic forecasting using causal inference and reinforcement learning Demand forecasting is a critical technology for efficient resource allocation and competitive advantage in business decision-making and supply chain management.