"Companies routinely rank demand planning immaturity as a major obstacle in meeting their supply chain goals," suggests a new white paper called Elght Methods that Improve Forecasting Accuracy. But accurate forecasts are the foundation for profitable business growth. Optimal demand planning and forecasting requires comprehensive modeling capabilities plus the flexibility and ease$-$of$-$use to shift methods as life cycles progress and market conditions change.

Attribute$-$based methods that use demand profiles are often suited to new product introduction and end of product life cycles, at times when reliable historical demand data is lacking or the available data is less relevant.

At the more mature stages of the product life cycle, $5$ different time$-$series statistical models come into play, including modified Holt, Holt$-$Winters, moving average, and intermittent or low demand. These models are used to create retrospective forecasts that cover prior periods $($typically $3$ years$)$ of documented demand. The forecasts are then matched to actual demand history to determine which one best fits the real$-$world data. The best$-$fit winner is used to create an objective base forecast.

Causal methods are used throughout the life cycle to adjust forecasts in anticipation of promotional events. Causal methods allow planners to predict how discounting and other promotional factors will affect volume, and layer the impact of these events on top of the underlying base forecast.

Finally, derived models can be used to create a Parent$-$Child relationship in which forecasts for closely related products are driven as a percentage of the forecast for a 'leader' product. This ensures that when the forecast is modified for the 'parent', all the 'child' forecasts would be updated accordingly.

To prevail in a business economy shaped by uncertain demand and rapid market changes, all of these forecasting methods must be harnessed. Forecasting software can automate much of the selection and switching of methods as a product moves through its life cycle. A best$-$in$-$class forecasting system is one that provides flexibility for users to weight elements and override key parameters in the forecast calculation based on their intuitive knowledge and market expertise.

Critical Thinking Questions

Why are accurate forecasts so important?

A$.$ Forecasting is an exact science.

B$.$ They are always quantitative in nature.

C$.$ Humans cannot make business forecasts.

D$.$ They are the foundation for business growth.

The best type of forecasting for introducing a new product is

A$.$ using historical data.

B$.$ a time series model.

C$.$ an attribute$-$based method.