FORECAST 2 Exponential smoothing. 3a. Exponential Smoothing Assumes the most recent observations have the highest predictive value – gives more weight.

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Presentation transcript:

FORECAST 2 Exponential smoothing

3a. Exponential Smoothing Assumes the most recent observations have the highest predictive value – gives more weight to recent time periods F t+1 = F t +  (A t - F t ) etet F t+1 = Forecast value for time t+1 A t = Actual value at time t  = Smoothing constant Need initial forecast F t to start. Need initial forecast F t to start.

3a. Exponential Smoothing – Example 1 Given the weekly demand data what are the exponential smoothing forecasts for periods 2-10 using  =0.10? Assume F 1 =D 1 Given the weekly demand data what are the exponential smoothing forecasts for periods 2-10 using  =0.10? Assume F 1 =D 1 F t+1 = F t +  (A t - F t ) iAi

F t+1 = F t +  (A t - F t ) 3a. Exponential Smoothing – Example 1  = = F 2 = F 1 +  (A 1 –F 1 ) =820+  (820–820) =820 iAiFi

F t+1 = F t +  (A t - F t ) 3a. Exponential Smoothing – Example 1  = = F 3 = F 2 +  (A 2 –F 2 ) =820+  (775–820) =815.5 iAiFi

F t+1 = F t +  (A t - F t ) This process continues through week 10 3a. Exponential Smoothing – Example 1  = = iAiFi

F t+1 = F t +  (A t - F t ) What if the  constant equals 0.6 3a. Exponential Smoothing – Example 1  = =  = = iAiFi

F t+1 = F t +  (A t - F t ) What if the  constant equals 0.6 3a. Exponential Smoothing – Example 2  = =  = = iAiFi

Company A, a personal computer producer purchases generic parts and assembles them to final product. Even though most of the orders require customization, they have many common components. Thus, managers of Company A need a good forecast of demand so that they can purchase computer parts accordingly to minimize inventory cost while meeting acceptable service level. Demand data for its computers for the past 5 months is given in the following table. 3a. Exponential Smoothing – Example 3

F t+1 = F t +  (A t - F t ) What if the  constant equals 0.5 3a. Exponential Smoothing – Example 3  = =  = = iAiFi

α How to choose α – depends on the emphasis you want to place on the most recent data α Increasing α makes forecast more sensitive to recent data 3a. Exponential Smoothing

3-12 Picking a Smoothing Constant .1 .4 Actual