Weighted Moving Average To adjust MA method to reflect more closely recent fluctuation Baca sendiri
Exponential Smoothing Weights most recent data more strongly than distant past data. Usefull if changes in data are result of an actual change (such as seoasons) rather than just random change Rumus: F = forecast D = actual demand = smoothing constant What happens if =0 or =1…?
Case F 2 = D 1 + (1- )F 1 = 0,3.37 + 0,7.37 = 37 F 3 = D 2 + (1- )F 2 = + 0,7.37 = 37
Adjusted Exponential Smoothing Exponential smoothing generally lies below the actual demand (especially in upward trends) Adjusted exponential smoothing adds a certain value to adjust the forecast so it reflects the actual demand more precisely Rumus: T = trend factor = smoothing constant for trend
Your consent to our cookies if you continue to use this website.