Exponential smoothing is a forecasting method that assigns weights to past observations, with more recent observations having higher weights. The alpha value is the smoothing constant that determines how much weight is given to the most recent observation. A low alpha value means that the forecast is based more on the historical average, while a high alpha value means that the forecast is more responsive to the latest changes in demand. A company with stable demand would typically use a low alpha value to smooth out random fluctuations and obtain a more accurate forecast. A beta value is another smoothing constant that is used for trend-adjusted exponential smoothing, which accounts for the presence of a linear trend in the data. A low beta value means that the trend component is based more on the historical average, while a high beta value means that the trend component is more responsive to the latest changes in demand. A company with stable demand would not need to use trend-adjusted exponential smoothing, since there is no significant trend in the data. References := CPIM Part 2 Exam Content Manual, Domain 3: Plan and Manage Demand, Section C: Forecast Demand, Subsection 2: Select appropriate forecasting technique(s) (p. 16)
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