Working Together: Bottom Up and Top Down Forecasting

An effective S&OP program depends on solid, accurate demand forecasts. Best practice companies do three things well: statistical, top-down and bottom-up forecasting. Many companies are doing one or two of these, but few are doing all three well. Of course, some companies do none. Let’s just say these companies have a huge upside improvement potential.

A statistically generated forecast should use a “best fit” approach to select the mathematical algorithm that minimizes error (using mean absolute percentage error (MAPE)). The statistical engine should select the best algorithm for each time-phased data series or set of regression data. The resulting forecast should serve as a starting point for bottom-up and top-down forecasts.

Bottom-up forecasts are accumulated from many contributors. A distributed sales force may have hundreds or thousands of contributors. Each contributor has a specific area of expertise such as a specific customer, product or geographic area. The contributor enters her forecasts for her specific area of responsibility. Forecasts from all contributors are summed to capture an overall bottom-up forecast.

Conversely, a top-down approach applies a more centralized view. A small number of forecasters will look at various inputs and generate forecasts. Influencing factors may include market data, economic indicators, and general product and customer trends. Here, too, the statistically generated forecast is a good number from which to start.

The beauty of top-down and bottom-up forecasts is their ability to look at the world from differing vantage points. The folks in the “ivory tower” know important information, but they don’t know everything. The folks in the field have keen insights into their unique areas, but they only see their small piece. The challenge is to capture the small pieces without tainting the field forecaster’s view. In other words, don’t tell the field forecasters the top-down targets. When field forecasters are told what their forecasts are expected to be, they tend to send back values right in line with the top-down values. Such tainted bottom-up forecasts miss the point of gathering field intelligence.

An effective marriage will capture top-down and bottom-up forecasts separately. A management by exception S&OP tool will make comparisons quickly to enable users to analyze critical differences and refine the ultimate consensus driven forecast.

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