Download presentation

Presentation is loading. Please wait.

Published byHaleigh Bagby Modified over 2 years ago

1
Computer Science Centre University of Indonesia 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 (a)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
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

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

15
Linear Trend Line Use least square regression Baca sendiri…!

16
Seasonal Adjustment We need to adjust seasonality by multiplying the normal forecast by a seasonal factor

17
Example: Turkey Demand Use linear trend to get forecast for year 5 = 58.17

18
Errors Baca sendiri

19
Multiple Regression Relationship between a dependent variable and two or more independent variable Formula y = ax 1 +bx 2 + … + c

20
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

21
Analisa Statistik Lainnya Chi-Square

22
Computer Science Centre University of Indonesia Tugas

23
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)

Similar presentations

OK

Forecasting 5 June 2001. Introduction What: Forecasting Techniques Where: Determine Trends Why: Make better decisions.

Forecasting 5 June 2001. Introduction What: Forecasting Techniques Where: Determine Trends Why: Make better decisions.

© 2017 SlidePlayer.com Inc.

All rights reserved.

Ads by Google

Ppt on pop art Ppt on dynamic web pages Ppt online downloader youtube Ppt on council of ministers of education Download ppt on pedal powered washing machine Ppt on switching devices in ece Ppt on vertically opposite angles are equal Ppt on heterotrophic mode of nutrition in plants Ppt on wireless sensor network security Esi ms ppt online