第六章供应链中的需求预测.ppt

Adaptive Forecasting where Lt = estimate of level at the end of Period t Tt = estimate of trend at the end of Period t St = estimate of seasonal factor for Period t Ft = forecast of demand for Period t (made Period t – 1 or earlier) Dt = actual demand observed in Period t Et = Ft – Dt = forecast error in Period t Steps in Adaptive Forecasting Initialize Compute initial estimates of level (L0), trend (T0), and seasonal factors (S1,…,Sp) Forecast Forecast demand for period t + 1 Estimate error Compute error Et+1 = Ft+1 – Dt+1 Modify estimates Modify the estimates of level (Lt+1), trend (Tt+1), and seasonal factor (St+p+1), given the error Et+1 Moving Average Used when demand has no observable trend or seasonality Systematic component of demand = level The level in period t is the average demand over the last N periods Lt = (Dt + Dt-1 + … + Dt–N+1) / N Ft+1 = Lt and Ft+n = Lt After observing the demand for period t + 1, revise the estimates Lt+1 = (Dt+1 + Dt + … + Dt-N+2) / N, Ft+2 = Lt+1 Moving Average Example A supermarket has experienced weekly demand of milk of D1 = 120, D2 = 127, D3 = 114, and D4 = 122 gallons over the past four weeks Forecast demand for Period 5 using a four-period moving average What is the forecast error if demand in Period 5 turns out to be 125 gallons? Moving Average Example L4 = (D4 + D3 + D2 + D1)/4 = (122 + 114 + 127 + 120)/4 = 120.75 Forecast demand for Period 5 F5 = L4 = 120.75 gallons Error if demand in Period 5 = 125 gallons E5 = F5 – D5 = 125 – 120.75 = 4.25 Revised demand L5 = (D5 + D4 + D3 + D2)/4 = (125 + 122 + 114 + 127)/4 = 122 Simple Exponential Smoothing Used when demand has no observable trend or seasonality Systematic component of demand = level Initial estimate of level, L0, assumed to be the average of all historical data Simple Exponential Smoothing Revised forecast using smoothing constant 0 a 1 Given data for Periods 1 to n Current forecast Thus Simple Exponential Smoothing Supermarket d