Certified Supply Chain Professional (CSCP) Practice Exam

How does a weighted moving average differ from a simple moving average?

1. It includes only the most recent observation

2. It gives more weight to recent data points

3. It ignores past data completely

4. It uses a fixed set of data points

The correct answer is: It gives more weight to recent data points

A weighted moving average is distinct from a simple moving average primarily because it assigns different weights to various observations, placing greater emphasis on more recent data points. This method acknowledges that more current data might provide a better reflection of trends and patterns than older data, which may be less relevant in a fast-paced context. In contrast, a simple moving average treats all observations equally, averaging the same way across the entire data set, regardless of how recent or old the data points are. By giving more importance to recent data in a weighted moving average, it becomes more responsive to changes, thus enabling better forecasting and trend analysis. The weighted moving average does not ignore past data entirely; it simply adjusts the significance of each observation based on how recent it is. Also, it does not operate within a fixed set of data points in the same manner as a simple moving average, which typically calculates the mean over a consistent number of past values, treating all included points uniformly.