You have just taken over as the new forecasting analyst for Fritos sales to Intermountain convenience stores. Despite having years of sales data showing flat demand with moderate variation, your predecessor used a 4-week moving average to project sales. Which adjustment would you make to the forecasting method in order to improve accuracy? Create a trend forecasting model. Implement an exponential smoothing model. Simplify the approach with a naive model. Increase the number of weeks used in the moving average model.
Question
You have just taken over as the new forecasting analyst for Fritos sales to Intermountain convenience stores. Despite having years of sales data showing flat demand with moderate variation, your predecessor used a 4-week moving average to project sales. Which adjustment would you make to the forecasting method in order to improve accuracy? Create a trend forecasting model. Implement an exponential smoothing model. Simplify the approach with a naive model. Increase the number of weeks used in the moving average model.
Solution
Given that the sales data shows flat demand with moderate variation, the best approach would be to implement an exponential smoothing model. This model is particularly useful when dealing with data that has no clear trend or seasonal pattern, as it assigns exponentially decreasing weights over time. This means that more recent observations are given more importance, which could help in capturing any sudden changes in the sales pattern.
A trend forecasting model would not be suitable in this case as the data shows flat demand. Similarly, a naive model, which simply forecasts the next period’s demand to be equal to the current period’s demand, may not capture the moderate variation in the data.
Increasing the number of weeks used in the moving average model could potentially smooth out the data too much, making it less responsive to changes in the sales pattern. Therefore, an exponential smoothing model would be the most appropriate adjustment to make in this case.
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