# Computer Science Centre University of Indonesia Forecasting Chapter 15 Management Science, 7th edition Bernard W Taylor III (2002)

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Computer Science Centre University of Indonesia Forecasting Chapter 15 Management Science, 7th edition Bernard W Taylor III (2002)

Agenda Intro to Forecasting Forecasting method Time Series Regression & Multiple Regression Other statistical forecasting method Tugas untuk 31 Oktober 2003 (presentasi)

Intro Forecasting is a prediction of what will occur in the future Although impossible to predict future exactly, forecast can provide reliable guidelines for decision making

Forecast Movement Forms (a)Trend (b) Cycle (economic) (c) Seasonal(d)Trend & Seasonal

Forecasting Methods Time series Regression Qualitative methods (must read yourself!)

Time Series Statistical techniques that make use of historical data Assumption: what happen in the past will happen in the future

Moving Average Tends to smooth the random increase and decrease Computed for specific period

Cont’d

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

Cont’d

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

Linear Trend Line Use least square regression Baca sendiri…!

Seasonal Adjustment We need to adjust seasonality by multiplying the normal forecast by a seasonal factor

Example: Turkey Demand Use linear trend to get forecast for year 5 = 58.17

Errors Baca sendiri

Multiple Regression Relationship between a dependent variable and two or more independent variable Formula y = ax 1 +bx 2 + … + c

Example of Multiple Regression Dependant variable: attendance Independent variable: wins & promotion We can predict attendance if we have \$60.000 for promotion and an expeted wins of seven games

Analisa Statistik Lainnya Chi-Square

Computer Science Centre University of Indonesia Tugas

Untuk 2 minggu lagi Dari buku Management Science, 7th edition, oleh Bernard W Taylor III (2002) Bab 15, nomor 39 (Taco Bell) dan nomor 43 (Bayville Police Dept)

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