Which forecasting technique calculates the average demand over a specified number of past periods?

Which forecasting technique calculates the average demand over a specified number of past periods?

• A. Trend time average
• B. Moving average
• C. Demand smoothing
• D. Quantitative analysis

Answer: (B)

The moving average forecasting technique calculates the average demand over a specified number of past periods. It is commonly used to smooth out fluctuations in data and identify trends over time. By averaging the demand from previous periods, it provides a more stable estimate that can help in forecasting future demand. This method is particularly effective in industries where demand patterns may fluctuate due to seasonal effects or random variations. By using a defined number of past periods, such as the last three or five, it reduces the impact of extreme values, making it easier to predict future needs based on more reliable data. The moving average is user-friendly, requiring minimal data processing and easy to implement in various forecasting software. It is designated as a fundamental technique within quantitative forecasting, focusing on historical data to inform future demand rather than relying on subjective judgment or insight.

The moving average forecasting technique calculates the average demand over a specified number of past periods. It is commonly used to smooth out fluctuations in data and identify trends over time. By averaging the demand from previous periods, it provides a more stable estimate that can help in forecasting future demand.

This method is particularly effective in industries where demand patterns may fluctuate due to seasonal effects or random variations. By using a defined number of past periods, such as the last three or five, it reduces the impact of extreme values, making it easier to predict future needs based on more reliable data.

The moving average is user-friendly, requiring minimal data processing and easy to implement in various forecasting software. It is designated as a fundamental technique within quantitative forecasting, focusing on historical data to inform future demand rather than relying on subjective judgment or insight.