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3/4/2009
1
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.
4Dr. Mohammad Abdul Mukhyi, SE., MM.
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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
5Dr. Mohammad Abdul Mukhyi, SE., MM.
Demand Management
Marketplace Demand Mgt.
Production Planning
MasterProductionPlanning
ResourcePlanning
6Dr. Mohammad Abdul Mukhyi, SE., MM.
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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.
7Dr. Mohammad Abdul Mukhyi, SE., MM.
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.
8Dr. Mohammad Abdul Mukhyi, SE., MM.
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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.
9Dr. Mohammad Abdul Mukhyi, SE., MM.
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
10Dr. Mohammad Abdul Mukhyi, SE., MM.
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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
11Dr. Mohammad Abdul Mukhyi, SE., MM.
• 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
12Dr. Mohammad Abdul Mukhyi, SE., MM.
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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
14Dr. Mohammad Abdul Mukhyi, SE., MM.
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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
15Dr. Mohammad Abdul Mukhyi, SE., MM.
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
16Dr. Mohammad Abdul Mukhyi, SE., MM.
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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
17Dr. Mohammad Abdul Mukhyi, SE., MM.
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
18Dr. Mohammad Abdul Mukhyi, SE., MM.
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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
19Dr. Mohammad Abdul Mukhyi, SE., MM.
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.
20Dr. Mohammad Abdul Mukhyi, SE., MM.
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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
21Dr. Mohammad Abdul Mukhyi, SE., MM.
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
22Dr. Mohammad Abdul Mukhyi, SE., MM.
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Simple Linear Regression Example
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)
23Dr. Mohammad Abdul Mukhyi, SE., MM.
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
24Dr. Mohammad Abdul Mukhyi, SE., MM.
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Simple Linear Regression Example
� 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
25Dr. Mohammad Abdul Mukhyi, SE., MM.
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
26Dr. Mohammad Abdul Mukhyi, SE., MM.
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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)
27Dr. Mohammad Abdul Mukhyi, SE., MM.
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
28Dr. Mohammad Abdul Mukhyi, SE., MM.
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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
29Dr. Mohammad Abdul Mukhyi, SE., MM.
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 ?
30Dr. Mohammad Abdul Mukhyi, SE., MM.
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Simple Moving Average Example
Week
450 —
430 —
410 —
390 —
370 —
| | | | | |0 5 10 15 20 25 30
Actual shipmentarrivals
Sh
ipm
en
t a
rriv
als
31Dr. Mohammad Abdul Mukhyi, SE., MM.
Simple Moving Average Example
Week
Shipment
Week Arrivals
1 4002 3803 4114 415
F4=(400+380+411)/3 = 397F5=(380+411+415)/3 = 402
Actual shipmentarrivals
450 —
430 —
410 —
390 —
370 —
| | | | | |0 5 10 15 20 25 30
Sh
ipm
en
t a
rriv
als
32Dr. Mohammad Abdul Mukhyi, SE., MM.
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Simple Moving Average Example
� F4 = 397
� F5 = 402
� Observe that as forecasts are made further into the future the forecast error increases
33Dr. Mohammad Abdul Mukhyi, SE., MM.
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 ?
34Dr. Mohammad Abdul Mukhyi, SE., MM.
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Weighted Moving Average Example
Week
450 —
430 —
410 —
390 —
370 —
| | | | | |0 5 10 15 20 25 30
Actual shipmentarrivals
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
35Dr. Mohammad Abdul Mukhyi, SE., MM.
Weighted Moving Average Example
Week
450 —
430 —
410 —
390 —
370 —
| | | | | |0 5 10 15 20 25 30
Actual shipmentarrivals
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
36Dr. Mohammad Abdul Mukhyi, SE., MM.
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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 ?
37Dr. Mohammad Abdul Mukhyi, SE., MM.
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
38Dr. Mohammad Abdul Mukhyi, SE., MM.
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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
39Dr. Mohammad Abdul Mukhyi, SE., MM.
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
40Dr. Mohammad Abdul Mukhyi, SE., MM.
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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
41Dr. Mohammad Abdul Mukhyi, SE., MM.
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
42Dr. Mohammad Abdul Mukhyi, SE., MM.
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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
43Dr. Mohammad Abdul Mukhyi, SE., MM.
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
44Dr. Mohammad Abdul Mukhyi, SE., MM.
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Seasonal Influences Example
Quarter 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 = 0.20234
45Dr. Mohammad Abdul Mukhyi, SE., MM.
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
46Dr. Mohammad Abdul Mukhyi, SE., MM.
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Seasonal Influences Example
Quarter 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 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
47Dr. Mohammad Abdul Mukhyi, SE., MM.
Seasonal Influences Example
Quarter 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 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
48Dr. Mohammad Abdul Mukhyi, SE., MM.
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25
Seasonal Patterns
Period
Dem
an
d(a) Multiplicative pattern
| | | | | | | | | | | | | | | |0 2 4 5 8 10 12 14 16
49Dr. Mohammad Abdul Mukhyi, SE., MM.
Seasonal Patterns
Period
| | | | | | | | | | | | | | | |0 2 4 5 8 10 12 14 16
Dem
an
d
(b) Additive pattern
50Dr. Mohammad Abdul Mukhyi, SE., MM.
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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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