Which forecasting method uses a smoothing constant to weight the most recent observations more heavily?

Which forecasting method uses a smoothing constant to weight the most recent observations more heavily?

• A. Moving average
• B. Time-series
• C. Exponential smoothing
• D. Delphi method

Answer: (C)

Exponential smoothing uses a smoothing constant to weight the most recent observations more heavily. It blends the latest actual demand with the previous forecast, using a factor alpha (between 0 and 1) that determines how much emphasis to place on the newest data. The basic idea is f_{t+1} = alpha * x_t + (1 - alpha) * f_t, so a higher alpha makes the forecast react quickly to recent changes, while a lower alpha smooths out short-term fluctuations and keeps the forecast steadier. This is different from a simple moving average, which gives equal weight to observations within a fixed window, and from other methods like Delphi, which rely on qualitative judgment rather than numerical weighting. Time-series is a broad category that includes many approaches, but the one that explicitly uses a smoothing constant to prioritize recent observations is exponential smoothing, making it well-suited for responsive yet smoothed forecasts in contexts like foodservice demand planning.

Exponential smoothing uses a smoothing constant to weight the most recent observations more heavily. It blends the latest actual demand with the previous forecast, using a factor alpha (between 0 and 1) that determines how much emphasis to place on the newest data. The basic idea is f_{t+1} = alpha * x_t + (1 - alpha) * f_t, so a higher alpha makes the forecast react quickly to recent changes, while a lower alpha smooths out short-term fluctuations and keeps the forecast steadier. This is different from a simple moving average, which gives equal weight to observations within a fixed window, and from other methods like Delphi, which rely on qualitative judgment rather than numerical weighting. Time-series is a broad category that includes many approaches, but the one that explicitly uses a smoothing constant to prioritize recent observations is exponential smoothing, making it well-suited for responsive yet smoothed forecasts in contexts like foodservice demand planning.