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Forecasting

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.

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Forecasting

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  1. Forecasting Chapter 15 Management Science, 7th edition    Bernard W Taylor III (2002)

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

  3. 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

  4. Forecast Movement Forms • Trend (b) Cycle (economic) (c) Seasonal (d)Trend & Seasonal

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

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

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

  8. Cont’d

  9. Weighted Moving Average • To adjust MA method to reflect more closely recent fluctuation • Baca sendiri

  10. 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…?

  11. F2 = D1 + (1- )F1 = 0,3.37+ 0,7.37 = 37 F3 = D2 + (1- )F2 = + 0,7.37 = 37 Case

  12. Cont’d

  13. 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

  14. Linear Trend Line • Use least square regression • Baca sendiri…!

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

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

  17. Errors • Baca sendiri

  18. Multiple Regression • Relationship between a dependent variable and two or more independent variable • Formula y = ax1+bx2 + … + c

  19. 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

  20. Analisa Statistik Lainnya • Chi-Square

  21. Tugas

  22. 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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