This document discusses demand forecasting in supply chains. It covers the role of forecasting, characteristics of forecasts, components of forecasts, and time-series forecasting methods. The key methods covered are static, adaptive, moving average, simple exponential smoothing, Holt's exponential smoothing (for trend), and Winter's method (for trend and seasonality). Examples are provided to illustrate how to apply these forecasting techniques to real demand data. The overall goal of the techniques is to decompose historical demand data into systematic and random components to generate accurate forecasts.