In December the Institute of Business Forecasting published the first of a new blog series on Forecast Value Added. Each month I will be interviewing an industry forecasting practitioner (or consultant/vendor) about their use of FVA analysis.
Utilize metrics weighted by profit. With limited time and resources, this focuses your attention on actions that impact earnings.
You may not always have reliable data on margin / profit. But if you can trust the numbers, this is a great way to direct your improvement efforts to those products that make the most difference. (Extremely low volume / low revenue / low margin items may not be worth spending any effort on.)
Don’t overlook measuring forecast bias.
Company forecasts are often overly optimistic. But Jonathon points out the situation where chronic supply shortages have led Sales forecasters to chronically under-forecast (not wanting their targets tied to numbers they don’t trust can be built). This can potentially perpetuate the shortages.
Compare performance to a naïve model.
The traditional random walk may be considered too simplistic, so Jonathon suggests using a seasonal random walk, simple exponential smoothing, or a moving average. While I suggest always utilizing the random walk as the ultimate point of comparison, I agree that other extremely simple models are appropriate to use for comparison (and they often forecast reasonably well). Early in my career, in a very stable low-growth business, we compared our forecasts to a 52-week moving average.
The FVA metric resonates with management.
FVA is easy to understand, and can be a key metric for root cause analysis and corrective action.
Jonathon uses a deseasonalized CV to reduce the risk of false positives. (While high CV generally implies lower forecastability, a highly seasonal item will have high CV but can be quite forecastable if its patterns are consistent and repeating.)
Discourage arbitrary performance goals (such as MAPE < 20%).