PERAMALAN PERMINTAAN PRODUK DAN JASAmukhyi.staff.gunadarma.ac.id/Downloads/files/12169/PERAMALAN...

3/4/2009

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PERAMALAN PERMINTAAN PRODUK DAN JASA

Dr. Mohammad Abdul Mukhyi, SE., MM

Dr. Mohammad Abdul Mukhyi, SE., MM. 2

Forecasting

• Forecasting is the art and science of predicting future events

• Institute of business forecasting (www.ibforecast.com)

• Forecasting as a part of strategic business planning

• Types of demand – what to forecast?

• Components of demand

• Overview of forecasting techniques

• Evaluating accuracy of forecasts

• Forecast ranging – prediction intervals

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Why Forecast?

• Lead times memerlukan keputusan dibuatmendahului peristiwa ketidak-pastian.

• Peramalan adalah penting bagi semuakeputusan strategis dan perencanaan dalamrantai pasokan.

• Peramalan permintaaan produk, material, tenaga kerja, pembiayaan adalah suatumasukan penting untuk penjadualan, memperoleh sumber daya, dan menentukankebutuhan sumber daya.

3Dr. Mohammad Abdul Mukhyi, SE., MM.

Demand Management

• Demand management is the interface between manufacturing planning and control and the market place. Activities include:

– Forecasting.

– Order Processing.

– Making delivery promises.


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Market Demand

Forecast

Aggregate PlansStarting Inventory

Market Demand

Forecast

Aggregate PlansStarting Inventory

People-machine Assignment

Material procurement

OvertimeHiring and

layoffs

Subcontracting

Ending inventory

Actual Demand

OvertimeHiring and

layoffs

Ending inventory

The Planning Process


Demand Management

Marketplace Demand Mgt.

Production Planning

MasterProductionPlanning

ResourcePlanning


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Forecasting Horizons.

• Short Term (0 to 3 months): for inventory management and scheduling.

• Medium Term (3 months to 2 years): for production planning, purchasing, and distribution.

• Long Term (2 years and more): for capacity planning, facility location, and strategic planning.


Principles of Forecasting

• Forecasts are almost always wrong.

• Every forecast should include an estimate of the forecast error.

• The greater the degree of aggregation, the more accurate the forecast.

• Long-term forecasts are usually less accurate than short-term forecasts.


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Forecasting Methods

• Metoda kualitatif adalah hubungan secaraalami karena mereka bersandar padapendapat dan pertimbangan manusia.

• Metode kuantitatif menggunakanmatematika atau model simulasi berdasarpada hubungan atau permintaan historisantar variabel.


Qualitative Approaches

• Usually based on judgments about causal factors that underlie the demand of particular products or services

• Approaches vary in sophistication from scientifically conducted surveys to intuitive hunches about future events

• The approach/method that is appropriate depends on a product’s life cycle stage


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• Educated guess

• Executive committee consensus

• Delphi method

• Survey of sales force

• Survey of customers

• Historical analogy

• Market research scientifically conducted surveys

Qualitative Approaches

intuitive hunches


• Berdasarkan asumsi bahwa "kekuatan" yang dihasilkan selama permintaankebutuhan masa depan, secara historisakan cenderung untuk mengulang

• Analisis pola permintaan terakhirmemberikan dasar yang baik untukmeramalkan permintaan di masa depan

• Mayoritas pendekatan kuantitatif masukke dalam kategori analisis time series

Quantitative Approaches


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Proses Peramalan1. Penentuan tujuan :

a. Variabel apa yang diestimasi.

b. Siapa yang akan menggunakan hasil peramalan.

c. Untuk tujuan apa hasil peramalan digunakan.

d. Estimasi jangka panjang atau jangka pendek.

e. Derajat ketepatan estimasi yang diharapkan.

f. Kapan estimasi dibutuhkan

g. Bagian-bagian peramalan yang diinginkan (pembeli, produk, daerah, dan lain-lain)

2. Pengembangan model.

3. Pengujian model.

4. Penerapan model.

5. Revisi dan evaluasi.

Dr. Mohammad Abdul Mukhyi, SE., MM. 13

ForecastingForecastingForecastingForecasting1. Time Series Analysis

Simple moving averageWeighted moving averageExponential SmoothingLinear Regression

2. Causal Relationship ForecastingCan be super-imposed on a time series analysis

3. Qualitative MethodsMarket researchInterview customers / sales people


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Time Series Analysis –Simple Moving Average

• Menghilangkan fluktuasi acak

• Peramalan mencerminkan masa lalu nyata

• Digunakan terbaik manakalapermintaan produksi adalah tidaktumbuh atau merosot dengan cepat

• Short time horizon


Time Series Analysis – Weighted Moving Average

• Similar to simple moving average, but weight given to individual values

• Used for averaging out seasonal data or known fluctuations

• Short time horizon• Better than simple moving average

but more expensive and time consuming to calculate


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Time Series Analysis –

Exponential Smoothing

• More accurate than moving averages• Relatively easy to calculate• Used when importance of the data

diminishes with age• Forecast lags the trend and can

overreact to changes• Compensate for this with a smoothing

constant delta


Time Series Analysis –

Linear Regression

• Given one set of data, we can predict the corresponding set

• Relationship between the two sets of variables is linear i.e. they follow the equation:

Y = a + bX• Very useful for long term forecasting• Individual forecasts can be calculated

using least squares analysis


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Regression Models

• Simple regression – One independent variable

• Multiple regression– More than one independent variables

• Linear regression– All variables are of power of 1, e.g. X

• Nonlinear regression– At least one independent variable is of power

different than 1 or interaction terms are present in the model, e.g. X2, X1X2


Causal Relationship Forecasting

• The occurrence of one variable causes the occurrence of another e.g. increase in rain causes increase in sale of umbrellas and raincoats

• The causing variable may not be directly related to the caused variable e.g. what effect does a .25% interest rate increase have?– Decrease in property sales– Decrease in real estate agents’ revenue– Decrease in sales of Volvos, BMWs, etc.– Decrease in overseas trips by new car sales

staff– Etc.


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Causal Methods: Simple

Linear Regression

Independent variable

De

pe

nd

en

t va

ria

ble

X

Y

Actualvalueof Y

Estimate ofY fromregressionequation

Value of X usedto estimate Y

Deviation,or error

{

Regressionequation:Y = a + bX


Simple Linear Regression Example

� Using the data below estimate point sales (in 000 of units) when budgeted advertising expenditure is $2,300Examples.xls

Sales AdvertisingMonth (000 units) (000 $)

1 264 2.52 116 1.33 165 1.44 101 1.05 209 2.0


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Sales vs. Advertising

0.000

50.000

100.000

150.000

200.000

250.000

300.000

0.000 0.500 1.000 1.500 2.000 2.500 3.000

Sales (000 units)


SUMMARY OUTPUT

Regression Statistics

Multiple R0.97956476

6

R Square0.95954713

1

Adjusted R Square

0.946062842

Standard Error15.6027357

4

Observations 5

ANOVA

df SS MS FSignificanc

e F

Regression 1 17323.6639117323.6639

171.160377

50.00349596

9

Residual 3 730.3360882243.445362

7

Total 4 18054

Coefficients

Standard Error t Stat P-value Lower 95%

Upper 95%

Lower 95.0%

Upper 95.0%

Intercept

-8.134986226 22.3524729

-0.363941219

0.74003927

-79.270597

7563.000625

3

-79.27059775

63.0006253

Advertising (000 $)

109.2286501 12.94843989

8.435661059

0.003495969 68.0208968

150.4364035 68.0208968

150.4364035


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� The regression line equation is:F = -8.135 + 109.229 X

– Regression is significant overall as p-value = .003

– Advertising is a significant predictor of sales as p-value = .003

– 94.61 percent of variability in sales is explained by advertising (good model!)

� If X = 2,300 the expected sales is:F(23) = -8.135 + 109.229 (2.3) = $243,091


Time-Series Regression Example

� Using the data below estimate point sales (in 000 of units) in month 6Examples.xls

SalesMonth (000 units)

1 1502 1633 1824 1915 209


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Time Series Regression Example

Sales vs. Time

0.000

50.000

100.000

150.000

200.000

250.000

0 1 2 3 4 5 6

Sales (000 units)


Time Series Regression

SUMMARY OUTPUT

Regression Statistics

Multiple R 0.995711736

R Square 0.99144186

Adjusted R Square 0.988589147

Standard Error 2.476556749

Observations 5

ANOVA

df SS MS F Significance F

Regression 1 2131.6 2131.6 347.5434783 0.000336881

Residual 3 18.4 6.133333333

Total 4 2150

Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0%

Intercept 135.2 2.597434632 52.05135804 1.5617E-05 126.933796 143.466204 126.933796 143.466204

Month 14.6 0.783156008 18.64251802 0.000336881 12.10764572 17.09235428 12.10764572 17.09235428


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Time Series Regression Example

� The regression line equation is:F = 135.2 + 14.6 t

– Regression is significant overall as p-value = .0003

– Time is a significant predictor of sales as p-value = .0003

– 98.86 percent of variability in sales is explained by time (good model!)

� If t = 6 the expected sales is:F(6) = 135.2 + 14.6 (6) = $222.800


Simple Moving Average Example

� Calculate a 3-week simple moving average forecast for part demand in weeks 4 and 5 Examples.xls

Week Demand

1 4002 3803 4114 4155 ?


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Week

450 —

430 —

410 —

390 —

370 —

| | | | | |0 5 10 15 20 25 30

Actual shipmentarrivals

Sh

ipm

en

t a

rriv

als



Week

Shipment

Week Arrivals

1 4002 3803 4114 415

F4=(400+380+411)/3 = 397F5=(380+411+415)/3 = 402


450 —

430 —

410 —

390 —

370 —

| | | | | |0 5 10 15 20 25 30

Sh

ipm

en

t a

rriv

als


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� F4 = 397

� F5 = 402

� Observe that as forecasts are made further into the future the forecast error increases


Weighted Moving Average Example

� Calculate a 3-week weighted moving average forecast for part demand in weeks 4 and 5. The weights are: w1=.70, w2=.20, w3=.10. Examples.xls

Week Demand

1 4002 3803 4114 4155 ?


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Week

450 —

430 —

410 —

390 —

370 —

| | | | | |0 5 10 15 20 25 30


3-week MAforecast

6-week MAforecast

Sh

ipm

en

t a

rriv

als

Weighted Moving Average assigned weights

Time Period Weightt .70t-1 .20t-2 .10



Week

450 —

430 —

410 —

390 —

370 —

| | | | | |0 5 10 15 20 25 30


3-week MAforecast

6-week MAforecast

Sh

ipm

en

t a

rriv

als

Weighted Moving Average

Time Period Weightt .70t-1 .20t-2 .10

F4 = 0.70(411) + 0.20(380) + 0.10(400) = 403.70F5 = 0.70(415) + 0.20(411) + 0.10(380) = 410.70


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� Calculate a single exponential smoothing forecast for period 5 assuming α = 0.1 and F1 = A1Examples.xls

Single Exponential Smoothing Example

Week Demand

1 4002 3803 4114 4155 ?


Single Exponential Smoothing Example

450 —

430 —

410 —

390 —

370 —

Week

| | | | | |0 5 10 15 20 25 30

Sh

ipm

en

t a

rriv

als

Exponential Smoothing, αααα = 0.10

F t +1 = Ft + αααα(At – Ft )

F1 = 400.00F2 = 400+0.10(400-400) = 400.00F3 = 400+0.10(380-400) = 398.00F4 = 398+0.10(415-398) = 399.30F5 = 399.30+.10(415-399.3) = 400.87


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Model Comparisons

450 —

430 —

410 —

390 —

370 —Sh

ipm

en

t a

rriv

als

Week

| | | | | |0 5 10 15 20 25 30

3-week MAforecast

3-week weighted MAforecast

Exponential smoothingαααα = 0.10


Model Comparisons• Model ini dapat diperluas untuk

kasus yang lebih rumit– Model yang terbaik untuk meramalkan?

• Pilihan pertimbangan, AP faktor dankonstanta smoothing α gunamenentukan penyimpangan yang menyebabkan kemampuan model peramalan


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Time Series Methods: Seasonal Influences

Consider the following data. Determine the quarterly seasonally adjusted forecast for year 5 if expected demand is 2,600 units

Examples.xls

Quarter Year 1 Year 2 Year 3 Year 4

1 45 70 100 1002 335 370 585 7253 520 590 830 11604 100 170 285 215

Total 1000 1200 1800 2200Average 250 300 450 550


Seasonal Influences Example

Quarterly Sales

0

200

400

600

800

1,000

1,200

1,400

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16

time

sa

les


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Seasonal Influences Example Quarter Year 1 Year 2 Year 3 Year 4

1 45 70 100 1002 335 370 585 7253 520 590 830 11604 100 170 285 215

Total 1000 1200 1800 2200Average 250 300 450 550

Seasonal Index = Actual Demand

Average Demand


Seasonal Influences ExampleQuarter Year 1 Year 2 Year 3 Year 4

1 45/250 = 0.18 70 100 1002 335 370 585 7253 520 590 830 11604 100 170 285 215

Total 1000 1200 1800 2200Average 250 300 450 550

Seasonal Index = = 0.1845

250


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1 45/250 = 0.18 70/300 = 0.23 100/450 = 0.22 100/550 = 0.182 335/250 = 1.34 370/300 = 1.23 585/450 = 1.30 725/550 = 1.323 520/250 = 2.08 590/300 = 1.97 830/450 = 1.84 1160/550 = 2.114 100/250 = 0.40 170/300 = 0.57 285/450 = 0.63 215/550 = 0.39

Quarter Average Seasonal Index

1 (0.18 + 0.23 + 0.22 + 0.18)/4 = 0.20234


Seasonal Influences ExampleQuarter Year 1 Year 2 Year 3 Year 4

1 45/250 = 0.18 70/300 = 0.23 100/450 = 0.22 100/550 = 0.182 335/250 = 1.34 370/300 = 1.23 585/450 = 1.30 725/550 = 1.323 520/250 = 2.08 590/300 = 1.97 830/450 = 1.84 1160/550 = 2.114 100/250 = 0.40 170/300 = 0.57 285/450 = 0.63 215/550 = 0.39

Quarter Average Seasonal Index

1 (0.18 + 0.23 + 0.22 + 0.18)/4 = 0.202 (1.34 + 1.23 + 1.30 + 1.32)/4 = 1.303 (2.08 + 1.97 + 1.84 + 2.11)/4 = 2.004 (0.40 + 0.57 + 0.63 + 0.39)/4 = 0.50


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1 45/250 = 0.18 70/300 = 0.23 100/450 = 0.22 100/550 = 0.182 335/250 = 1.34 370/300 = 1.23 585/450 = 1.30 725/550 = 1.323 520/250 = 2.08 590/300 = 1.97 830/450 = 1.84 1160/550 = 2.114 100/250 = 0.40 170/300 = 0.57 285/450 = 0.63 215/550 = 0.39

Quarter Average Seasonal Index Forecast

1 (0.18 + 0.23 + 0.22 + 0.18)/4 = 0.20 650(0.20) = 1302 (1.34 + 1.23 + 1.30 + 1.32)/4 = 1.303 (2.08 + 1.97 + 1.84 + 2.11)/4 = 2.004 (0.40 + 0.57 + 0.63 + 0.39)/4 = 0.50

Projected Annual Demand = 2600Average Quarterly Demand = 2600/4 = 650




1 45/250 = 0.18 70/300 = 0.23 100/450 = 0.22 100/550 = 0.182 335/250 = 1.34 370/300 = 1.23 585/450 = 1.30 725/550 = 1.323 520/250 = 2.08 590/300 = 1.97 830/450 = 1.84 1160/550 = 2.114 100/250 = 0.40 170/300 = 0.57 285/450 = 0.63 215/550 = 0.39

Quarter Average Seasonal Index Forecast

1 (0.18 + 0.23 + 0.22 + 0.18)/4 = 0.20 650(0.20) = 1302 (1.34 + 1.23 + 1.30 + 1.32)/4 = 1.30 650(1.30) = 8453 (2.08 + 1.97 + 1.84 + 2.11)/4 = 2.00 650(2.00) = 13004 (0.40 + 0.57 + 0.63 + 0.39)/4 = 0.50 650(0.50) = 325


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Seasonal Patterns

Period

Dem

an

d(a) Multiplicative pattern

| | | | | | | | | | | | | | | |0 2 4 5 8 10 12 14 16


Seasonal Patterns

Period

| | | | | | | | | | | | | | | |0 2 4 5 8 10 12 14 16

Dem

an

d

(b) Additive pattern


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Literatur:

• Dr. Ir. Hj. Luluk Kholisoh, MM, OPERATION MANAGEMENT Presented by: Magister Management of Banking Program

• www.ibforecast.com

• N. Gaither and Frazier, Production and Operations Management, 8th Edition, Duxbury Press, NY, NY, 1999.

• (Road server) Handouts for most classes are available on the ROAD server. The handouts can be accessed at: _ HYPERLINK http://road.uww.edu __http://road.uww.edu_

• The handouts are under my name (HOUSEMAJ) and Course #: 250306.

Dr. Mohammad Abdul Mukhyi, SE., MM.51

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