Forecast bias is the sum of actuals minus the sum of forecast, divided by the sum of forecast. The formula, the four sign conventions that disagree with each other, a worked example, and the bias ratio that tells bias from noise.
Forecast Value Add scores every stage of your forecast against the stage that fed it, and against a naive baseline. A worked stairstep, how to run it in a spreadsheet, and how to read a negative result without starting a war with sales.
Most forecast bias is an incentive problem wearing a modeling costume. A practitioner method: find it by product and customer, map it to who benefits, and separate the forecast from the target before you touch the model.
Forecast accuracy is one number with four common formulas behind it. Here is how MAD, MAPE, WAPE and RMSE differ, when each one stops telling the truth, and how to pick the one your decision actually survives.
The Supply Chain GuysHonest supply chain judgment from practitioners All companies big or small do some sort of business forecasting, they estimate how their future will look like and use this information to make decisions that will shape the future in the way they want. However the quality of this forecast, its usability, detail and…