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EXECUTIVE SUMMARY | The purpose of this new column is to share new innovations in business forecasting with emphasis on people, process, analytics, and technology. It will focus on the benefits that innovation has delivered to improve the overall business forecasting discipline. The objective is to provide real insights into how innovation has improved not only forecast accuracy, but also the way forecasting is done. True innovation creates a paradigm shift in every aspect of the way we do forecasting, impacting process efficiency, reduced latency, improved performance, introducing new advanced analytics, and enabling technology.
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The two most discussed topics today in demand forecasting are forecastability and performance efficiency. The most common performance metric used across all industry verticals is MAPE (mean absolute percentage error), which pays little attention to the forecastability of demand and process efficiency. Furthermore, very few companies actually measure the touch points in their demand forecasting process before and after someone makes a manual adjustment (override) to determine if they have added value.
Measuring forecast performance is one of the most important elements of the demand forecasting process, and the least understood when put into practice. As you know, what gets measured gets fixed, and what gets fixed gets rewarded. You cannot improve your demand forecast accuracy until you measure and benchmark your current forecast performance. It is not unusual to encounter companies that have never truly measured the accuracy of their demand forecasts on an ongoing basis. Some measure forecast accuracy weekly, monthly, and quarterly at the most aggregate level in their product hierarchy with little focus on the lower levels-the stock keeping unit (SKU) detail or internal mix within the aggregates. It is not uncommon to find many companies that have virtually no idea that their lower-level product forecasts at the product group and the SKU detail have extremely high forecast error (or very low forecast accuracy). This is usually attributed to the way they calculate forecast accuracy (or error). They normally do not measure forecast error in terms of absolute values; thus, when they sum those error values to the aggregate levels, the plus and minus signs wash each other out, making the accuracy look much better than the lowerlevel detail. In...