Making Sense of Unpredictable Demand: The Associative Forecasting Method
Often times, when you're making your demand forecast, unforeseen changes happen quickly in real-time – forcing you to rush a new demand forecast. The University of Central Florida describes two kinds of demand forecasts: “Forecasting methods that react very strongly (or quickly) to demand changes are said to be responsive. Forecasting methods that do not react quickly to demand changes are said to be stable.” They advise that when choosing an appropriate demand forecast method, you should balance out each possible method's stability and responsiveness. You should use responsive-sided methods if your historical data shows frequent rapid changes, and vice versa, you should use stable-sided methods when your historical data shows a sort of continuity or gradually-changing demand without any huge peaks in demand changes.
One method that they discuss which allows you to stabilize demand data that may seem erratic and unpredictable, is the Associative Forecasting Method. This method assumes that one variable is dependent on other independent variables. They give the example of a drywall construction company. The historical demand data for the construction company's drywall demand looks erratic on the surface, and time-series methods of analysis might not yield results that are usable. However, if we take into account the city's new home construction permits and use that as the independent variable – the resulting forecast analysis gives better predictability and results that are understandable. Thus, on the surface, the drywall demand seemed random, but in conjunction with the city permit historical data, the demand pattern becomes clear.
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