Computer Science CentreUniversity of Indonesia

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Forecasting

Chapter 15Management Science, 7th edition

    Bernard W Taylor III (2002)

In fo rm ationM anagem entR E S E A R C H G R O U P

Agenda

• Intro to Forecasting• Forecasting method• Time Series• Regression & Multiple Regression• Other statistical forecasting method• Tugas untuk 31 Oktober 2003

(presentasi)

In fo rm ationM anagem entR E S E A R C H G R O U P

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

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Forecast Movement Forms(a)Trend (b) Cycle

(economic)(c) Seasonal (d)Trend & Seasonal

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Forecasting Methods

• Time series• Regression• Qualitative methods (must read

yourself!)

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Time Series

• Statistical techniques that make use of historical data

• Assumption: what happen in the past will happen in the future

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Moving Average

• Tends to smooth the random increase and decrease

• Computed for specific period

915

507513011090

5

5

11

5

i

DMA

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Cont’d

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Weighted Moving Average

• To adjust MA method to reflect more closely recent fluctuation

• Baca sendiri

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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 = forecastD = actual demand = smoothing constant

• What happens if =0 or =1…?

ttt FDF )1(1

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Case

F2 = D1 + (1- )F1

= 0,3.37 + 0,7.37 = 37

F3 = D2 + (1- )F2

= + 0,7.37 = 37

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Cont’d

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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

111 ttt TFAF

tttt TFFT )1()( 11

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In fo rm ationM anagem entR E S E A R C H G R O U P

Linear Trend Line

• Use least square regression• Baca sendiri…!

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Seasonal Adjustment

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

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Example: Turkey Demand

Use linear trend to get forecast for year 5 = 58.17

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Errors

• Baca sendiri

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Multiple Regression

• Relationship between a dependent variable and two or more independent variable

• Formulay = ax1+bx2 + … + c

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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

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Analisa Statistik Lainnya

• Chi-Square

Computer Science CentreUniversity of Indonesia

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Tugas

In fo rm ationM anagem entR E S E A R C H G R O U P

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)