Statistical methods of demand forecasting

This article provides a managerial overview rather than a complete treatment of statistical theory. Its purpose is to show where a few established techniques can make demand analysis more disciplined.

When to use it

Use statistical methods when the HOOF approach would benefit from stronger evidence, particularly when estimating historical growth or testing the relationship between demand and an external driver.

Origins

The techniques come from different statistical and economic traditions rather than one unified model. Least-squares regression developed through the work of Adrien-Marie Legendre and Carl Friedrich Gauss; trend extrapolation grew with time-series analysis; and the National Bureau of Economic Research developed indicator-based, or barometric, methods through business-cycle research led by Wesley Clair Mitchell, Arthur Burns and others.

What it is

The familiar warning about “lies, damned lies and statistics,” often associated with Mark Twain, is a reminder to use evidence critically rather than a reason to avoid quantitative analysis. Three comparatively accessible methods can support market-demand work:

• Trend projection
• Regression analysis
• Barometric method (NBER)

How to use it

Trend projection

A trend line summarises the direction of observations plotted over time:

1. Set out annual market-demand data.
2. Plot time on the x-axis and demand on the y-axis, using logarithmic graph paper when a constant percentage growth rate is the relevant pattern.
3. Place a reasoned line of best fit through the observations.
4. Measure its gradient to estimate the average annual growth rate.

This provides an alternative to the moving-average approach in Appendix A. Both methods should produce a broadly comparable estimate of historical market growth when the assumptions fit.

Treat extrapolation with much greater caution. Extending the line across future periods produces numbers, but not necessarily credible forecasts. The projection assumes that the forces shaping past demand will continue in the same way. That is often unrealistic. Use the line primarily to understand history, then apply the HOOF approach to demand forecasting to examine how demand drivers may change.