What’s your forecast accuracy telling you? Stop and ask a few questions
Forecast accuracy is an important performance metric in any effective S&OP process, but it can be measured in various ways. Comparing your company’s accuracy to an industry standard will be difficult to impossible if you don’t know the details behind the measurement. More importantly, the metric needs to resonate within your organization as a meaningful indicator of forecast relevance. So then…
What details should one consider for forecast accuracy measurement?

Here’s the Steelwedge Top Six:
1. Aggregation level: Are you measuring accuracy at a product SKU or family level? What about other hierarchy levels? Odds are your accuracy will appear to be better at an aggregated level such as family. This happens because variability of forecasts and actuals tend to cancel out one another as data is combined. The result is a smoothing of results and lowering of error calculations. Recommendation: Measure accuracy at the same level as the majority of forecasts are captured.
2. Error Calculation: In its most basic form, accuracy is a measure of the difference between a prediction and what actually happened. How far off were we? Error is equal to the difference between forecast and actual. Often, this is captured as a percentage value called percent error. Mean absolute percent error (MAPE) calculates the average of errors. Since we don’t want positives and negatives to cancel out each other, we use the absolute values of each error. There are other methods, but MAPE is fairly common. Weighted MAPE is a method used to give greater importance (weight) to items with greater activity. Amount of activity may be defined as the proportion a particular item is of the total. Recommendation: Keep it simple. Make sure people understand the measurement and how…