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Demand Forecasting is a book category focused on predicting future customer demand to support decisions in supply chain, inventory, production, staffing, finance, and pricing. These books span both qualitative approaches (judgment, Delphi, market research) and quantitative methods (time-series models like exponential smoothing and ARIMA, causal/econometric models, hierarchical and intermittent-demand techniques, and modern machine learning). Topics commonly include data preparation, handling seasonality and trend, modeling promotions and price effects, new product forecasting, feature engineering, accuracy metrics and bias reduction, and scenario planning. Many titles tie methods to real-world applications in retail, manufacturing, e-commerce, energy, and services, showing how forecasts drive S&OP, replenishment, and capacity planning. Readers also learn about forecasting process design, governance, and collaboration between planners and data scientists, with practical guidance using tools from spreadsheets to Python/R and enterprise platforms. This category serves students, analysts, and managers seeking both rigorous foundations and actionable playbooks.

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