Forecasting Methods & Metrics

S&OP vs SIOP vs IBP side by side: horizon, owner, units and output, one decision run through all three, and the forecast every version depends on.

Nine ways to improve demand forecast accuracy, each with the symptom that says it applies: bias, stockouts, promotions, overrides and when to switch.

How forecasting in retail works at SKU, store and DC level, why store forecasts break, and how to handle promotions, markdowns and stockouts.

Moving average, Holt-Winters, ARIMA and machine learning run on the same 104 weeks of demand. See which wins, where each breaks, and download the dataset.

How to run an ABC analysis step by step, add XYZ for demand variability, and turn the 9-cell matrix into service levels and forecast rules. 20-SKU example inside.

How to calculate forecast bias, read a tracking signal, and find where the lean comes from. Worked 12-week example plus a free calculator.

What the bullwhip effect is, how a 10% demand change turns into a factory-sized swing, real examples, and the forecasting fix that stops it at the source.

Most demand models track twenty variables. A handful do the work. What demand drivers actually are, how to find yours, and why adding more data stops helping.

What demand planning is, how the cycle runs month to month, and how teams reach a touchless forecast. The process, the four KPIs, and the accuracy bar to clear first.