Which forecasting technique is easy to calculate and responds quickly to changes in demand?

The selection of exponential smoothing as the correct answer is grounded in its characteristics that make it particularly effective for forecasting in a dynamic environment. Exponential smoothing is a technique that assigns exponentially decreasing weights to past observations. This means that more recent data points are given greater importance compared to older ones, which allows the forecast to be more responsive to changes in demand patterns.

The ease of calculation lies in its simplicity; it requires only the most recent forecast and the latest actual demand to produce the next forecast, making it efficient for various time frames. Since the method accounts for trends and seasonality if tailored properly, it effectively captures the changes in demand without the complexity that other methods might involve.

In contrast, moving averages might lag behind actual demand changes due to the equal weighting of all past observations, potentially delaying the response to shifts in trends. Causal models require establishing relationships between variables and can be more complex and slower to adjust when the underlying relationships change. Qualitative forecasting relies on subjective judgment, which may not respond as quickly to quantifiable shifts in demand as exponential smoothing does. Thus, exponential smoothing stands out for its quick adaptability and straightforward application in financial management contexts.