CPIM Practice Exam

Which method can be used to understand the impact of seasonality on demand forecasts?

1. Moving average

2. Deseasonalization

3. Qualitative analysis

4. Exponential smoothing

The correct answer is: Deseasonalization

The choice of deseasonalization as the method to understand the impact of seasonality on demand forecasts is appropriate because this technique specifically aims to remove the seasonal component from historical data. Seasonality refers to periodic fluctuations in demand that occur at regular intervals due to seasonal factors, and these can significantly distort forecasts if not accounted for. By applying deseasonalization, analysts can derive a clearer picture of the underlying demand patterns without the noise introduced by seasonal effects. This is essential for creating more accurate demand forecasts, particularly in industries where seasonality is a significant factor, like retail or agricultural markets. Once the seasonal effects have been removed, it becomes easier to assess trends and changes in demand that are not tied to seasonal fluctuations. This leads to a more accurate understanding of demand behavior, which can help businesses make informed decisions about production, inventory management, and staffing. In contrast, the other options may provide insights into data trends, but they do not specifically focus on adjusting for seasonal effects in the same direct manner. Moving averages smooth out data over time, focusing more on overall trends rather than seasonality. Qualitative analysis involves expert opinions and subjective data that do not explicitly address seasonal patterns. Exponential smoothing gives more weight to recent observations, which can also miss