Demand Forecasting using Excel Template

Explore demand forecasting basics with an excel template to understand independent versus dependent demand, bill of materials, and forecasting principles plus pattern types like trend and seasonality.

Explore qualitative and quantitative demand forecasting, including time series, causal models, moving average, exponential smoothing, seasonal indexing, and the Excel template.

Evaluate forecast performance by measuring accuracy with metrics such as mad, mse, and mape, and distinguish bias from random deviation while exploring cpfr for collaborative planning.

Explore simple linear regression and broader regression models to forecast demand using independent and dependent variables, with time series, cross sectional, ARIMA, and logit/probit approaches.

Apply regression modeling to demand forecasting, using GDP as a key predictor in a simple linear model, built and evaluated in Excel with R square and p values.

Apply a two-variable multiple regression in Excel using GDP and PCE to forecast demand, validating with adjusted r-squared and p-values, and ensuring no multicollinearity.

Use the excel template to model seasonality and trend with simple, weighted, and exponential smoothing; deseasonalize, forecast, reseasonalize, and evaluate with absolute, squared, and mean absolute percentage error.

Learn to build an Excel template that creates seasonal indices and deseasonalized data, and forecast with moving average, weighted moving average, and exponential methods, then reseasonalize and evaluate results.

Evaluate forecast accuracy with absolute deviation, algebraic error, and squared error; compare mean absolute deviation, mean squared error, root mean squared error, and absolute percentage error to select best method.

Develop demand forecasts with Excel by regression modeling, using GDP and PCE as predictors, analyze correlation, and build simple and multiple regression equations for quarterly forecasts.