Outline a simple weekly forecast method using a moving average.

• A. Forecast is based on previous period's actuals only.
• B. The forecast uses the average of the last N weeks and is used for the next period.
• C. Seasonality should never be considered.
• D. Forecasting with a moving average requires no historical data.

Answer: (B)

A simple weekly moving-average forecast estimates the next week's demand by averaging recent weeks’ actual values. The idea is to take the average of the last N weeks and use that as the forecast for the upcoming week. This approach smooths out random bumps in weekly data and keeps the forecast tied to recent experience, so as new data come in, the forecast automatically updates.

Why this is the best fit: it uses actual historical data and provides a straightforward, repeatable method that reduces noise from week-to-week fluctuations. You can adjust N to control responsiveness: smaller N reacts faster to recent changes, while larger N yields a smoother, slower-to-change forecast.

Why the other ideas don’t fit: using only the previous week’s actuals lacks the smoothing benefit and ignores longer-term patterns; saying seasonality should never be considered is misleading because seasonality can be incorporated (for example, with seasonal indices or by adjusting the moving-average window); and claiming no historical data is needed contradicts the very nature of a moving average, which relies on past observations to compute the average.