Which method is used in forecasting seasonal demand?

Forecasting seasonal demand is essential for businesses to effectively manage inventory and meet customer needs during peak and off-peak seasons. The creation of a seasonal index is a method that quantifies seasonal fluctuations in demand by adjusting historical data to account for seasonal variations.

This process typically involves determining an average demand per time period and then comparing actual demand to that average to construct an index that reflects seasonal patterns. For instance, if a particular month typically sees increased sales compared to the average, the seasonal index for that month would be greater than one, indicating higher demand relative to the norm.

By using a seasonal index, businesses can precisely adjust their forecasts to account for predictable changes in demand that occur in cycles, such as increased sales during holidays or back-to-school periods. This tailored approach helps organizations optimize inventory levels, reduce stockouts, and improve customer satisfaction.

The other methods mentioned do not specifically account for seasonal variations in the same manner. Estimating through exponential smoothing might provide a general forecast based on past trends but does not factor in specific seasonal patterns. Averaging past demand data may capture some trends but might overlook the fluctuations in demand caused by seasonal factors. Regression analysis can be useful in understanding relationships between demand and other variables but would typically require additional steps