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Forecasting - PPC
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Departemen Teknik Industri FTI-ITB
TI-3003 Perencanaan dan Pengendalian Produksi
FORECASTING (Peramalan)
Laboratorium Sistem Produksi
www.lspitb.org ©2013
Departemen Teknik Industri FTI-ITB
2
Hasil Pembelajaran
• Umum Mahasiswa mampu menerapkan model matematik,
heuristik dan teknik statistik untuk menganalisis dan merancang suatu sistem perencanaan dan pengendalian produksi
• Khusus Mampu menganalisis pola deman serta menerapkan
teknik-teknik peramalan
Departemen Teknik Industri FTI-ITB
3
Tahapan PPC
Peramalan
Perencanaan
Agregat
Jadwal Produksi Induk
Perencanaan
Material
Order
Pembelian
Jadwal
Produksi
Penjadwalan
Ulang
Pengendalian Aktivitas Produksi di
Lantai Pabrik
Out-
sourcing
Rough Cut
Capacity
Planning
(RCCP)
Capacity
Requirement
Planning
(CRP)
Capacity Planning
Str
ateg
ic
pla
nn
ing
Peramalan (Forecasting)
Departemen Teknik Industri FTI-ITB
Forecasting Horizons
• Long Term 5+ years into the future
R&D, plant location, product planning
Principally judgement-based
• Medium Term 1 season to 2 years
Aggregate planning, capacity planning, sales forecasts
Mixture of quantitative methods and judgement
• Short Term 1 day to 1 year, less than 1 season
Demand forecasting, staffing levels, purchasing, inventory levels
Quantitative methods
Departemen Teknik Industri FTI-ITB
Short Term Forecasting: Needs and Uses
• Scheduling existing resources How many employees do we need and when?
How much product should we make in anticipation of demand?
• Acquiring additional resources When are we going to run out of capacity?
How many more people will we need?
How large will our back-orders be?
• Determining what resources are needed What kind of machines will we require?
Which services are growing in demand? declining?
What kind of people should we be hiring?
Departemen Teknik Industri FTI-ITB
Types of Forecasting Models
• Types of Forecasts Qualitative --- based on experience, judgement,
knowledge;
Quantitative --- based on data, statistics;
• Methods of Forecasting Naive Methods --- eye-balling the numbers;
Formal Methods --- systematically reduce forecasting errors;
– time series models (e.g. exponential smoothing);
– causal models (e.g. regression).
Focus here on Time Series Models
• Assumptions of Time Series Models There is information about the past;
This information can be quantified in the form of data;
The pattern of the past will continue into the future.
Departemen Teknik Industri FTI-ITB
Forecasting Examples
• Examples from student projects: Demand for tellers in a bank;
Traffic on major communication switch;
Demand for liquor in bar;
Demand for frozen foods in local grocery warehouse.
• Example from Industry: American Hospital
Supply Corp.
70,000 items;
25 stocking locations;
Store 3 years of data (63 million data points);
Update forecasts monthly;
21 million forecast updates per year.
Departemen Teknik Industri FTI-ITB
Simple Moving Average
• Forecast Ft is average of n previous observations or
actuals Dt :
• Note that the n past observations are equally weighted.
• Issues with moving average forecasts:
All n past observations treated equally;
Observations older than n are not included at all;
Requires that n past observations be retained;
Problem when 1000's of items are being forecast.
t
nti
it
ntttt
Dn
F
DDDn
F
1
1
111
1
)(1
Departemen Teknik Industri FTI-ITB
Simple Moving Average
• Include n most recent observations
• Weight equally
• Ignore older observations
weight
today
1 2 3 ... n
1/n
Departemen Teknik Industri FTI-ITB
Moving Average Internet Unicycle Sales
0
50
100
150
200
250
300
350
400
450
Apr-01 Sep-02 Jan-04 May-05 Oct-06 Feb-08 Jul-09 Nov-10 Apr-12 Aug-13
Month
Un
its
n = 3
Departemen Teknik Industri FTI-ITB
Example:
Moving Average Forecasting
Departemen Teknik Industri FTI-ITB
Exponential Smoothing I
• Include all past observations
• Weight recent observations much more heavily than very old observations:
weight
today
Decreasing weight given to older observations
Departemen Teknik Industri FTI-ITB
Exponential Smoothing I
• Include all past observations
• Weight recent observations much more heavily than very old observations:
weight
today
Decreasing weight given to older observations
10
Departemen Teknik Industri FTI-ITB
Exponential Smoothing I
• Include all past observations
• Weight recent observations much more heavily than very old observations:
weight
today
Decreasing weight given to older observations
10
)1(
Departemen Teknik Industri FTI-ITB
Exponential Smoothing I
• Include all past observations
• Weight recent observations much more heavily than very old observations:
weight
today
Decreasing weight given to older observations
10
2)1(
)1(
Departemen Teknik Industri FTI-ITB
Exponential Smoothing: Concept
• Include all past observations
• Weight recent observations much more heavily than very old observations:
weight
today
Decreasing weight given to older observations
10
3
2
)1(
)1(
)1(
Departemen Teknik Industri FTI-ITB
Exponential Smoothing: Math
21
2
2
1
)1()1(
)1()1(
tttt
tttt
DaDDF
DDDF
Departemen Teknik Industri FTI-ITB
Exponential Smoothing: Math
1)1( ttt FaaDF
21
2
2
1
)1()1(
)1()1(
tttt
tttt
DaDDF
DDDF
Departemen Teknik Industri FTI-ITB
Exponential Smoothing: Math
• Thus, new forecast is weighted sum of old forecast and actual demand
• Notes:
Only 2 values (Dt and Ft-1 ) are required, compared with n for moving average
Parameter a determined empirically (whatever works best)
Rule of thumb: < 0.5
Typically, = 0.2 or = 0.3 work well
• Forecast for k periods into future is:
1
2
2
1
)1(
)1()1(
ttt
tttt
FaaDF
DaaDaaaDF
tkt FF
Departemen Teknik Industri FTI-ITB
20
DATA
SUMBER : Arsip perusahaan
Data pemerintah (laporan Biro Pusat Statistik, Departemen, dll)
• FAKTOR INTERNAL THD PENJUALAN Kualitas, harga, delivery time, promosi, discount, dll
• FAKTOR EKSTERNAL Indikator perekonomian : GNP, tingkat pertumbuhan
ekonomi, tingkat inflasi, nilai tukar valuta asing, dll
Departemen Teknik Industri FTI-ITB
Exponential Smoothing
= 0.2
0
50
100
150
200
250
300
350
400
450
Jän.03 Mai.04 Sep.05 Feb.07 Jun.08 Nov.09 Mär.11 Aug.12
Un
its
Month
Internet Unicycle Sales (1000's)
Departemen Teknik Industri FTI-ITB
Example:
Exponential Smoothing
Departemen Teknik Industri FTI-ITB
Complicating Factors
• Simple Exponential Smoothing works well with data that is “moving sideways” (stationary)
• Must be adapted for data series which exhibit a definite trend
• Must be further adapted for data series which exhibit seasonal patterns
Departemen Teknik Industri FTI-ITB
Holt’s Method: Double Exponential Smoothing
• What happens when there is a definite trend?
A trendy clothing boutique has had the following sales over the past 6 months:
1 2 3 4 5 6 510 512 528 530 542 552
480
490
500
510
520
530
540
550
560
1 2 3 4 5 6 7 8 9 10
Month
Demand
Actual
Forecast
Departemen Teknik Industri FTI-ITB
Holt’s Method: Double Exponential Smoothing
• Ideas behind smoothing with trend:
``De-trend'' time-series by separating base from trend effects
Smooth base in usual manner using Smooth trend forecasts in usual manner using
• Smooth the base forecast Bt
• Smooth the trend forecast Tt
• Forecast k periods into future Ft+k with base and trend
))(1( 11 tttt TBDB
11 )1()( tttt TBBT
ttkt kTBF
Departemen Teknik Industri FTI-ITB
ES with Trend = 0.2, = 0.4
0
50
100
150
200
250
300
350
400
450
Jän.03 Mai.04 Sep.05 Feb.07 Jun.08 Nov.09 Mär.11 Aug.12
Un
its
Month
Internet Unicycle Sales (1000's)
Departemen Teknik Industri FTI-ITB
Example:
Exponential Smoothing with Trend
Departemen Teknik Industri FTI-ITB
Winter’s Method: Exponential Smoothing
w/ Trend and Seasonality
• Ideas behind smoothing with trend and seasonality: “De-trend’: and “de-seasonalize”time-series by
separating base from trend and seasonality effects
Smooth base in usual manner using Smooth trend forecasts in usual manner using Smooth seasonality forecasts using g
• Assume m seasons in a cycle 12 months in a year
4 quarters in a month
3 months in a quarter
et cetera
Departemen Teknik Industri FTI-ITB
Winter’s Method: Exponential Smoothing
w/ Trend and Seasonality
• Smooth the base forecast Bt
• Smooth the trend forecast Tt
• Smooth the seasonality forecast St
))(1( 11
tt
mt
tt TB
S
DB
11 )1()( tttt TBBT
mt
t
tt S
B
DS )1( gg
Departemen Teknik Industri FTI-ITB
Winter’s Method: Exponential Smoothing
w/ Trend and Seasonality
• Forecast Ft with trend and seasonality
• Smooth the trend forecast Tt
• Smooth the seasonality forecast St
mktttkt SkTBF )( 11
11 )1()( tttt TBBT
mt
t
tt S
B
DS )1( gg
Departemen Teknik Industri FTI-ITB
ES with Trend and Seasonality
Internet Unicycle Sales (1000's)
0
50
100
150
200
250
300
350
400
450
500
Jan-03 May-04 Sep-05 Feb-07 Jun-08 Nov-09 Mar-11 Aug-12
Month
Un
its
= 0.2, = 0.4, g = 0.6
Departemen Teknik Industri FTI-ITB
Example:
Exponential Smoothing with
Trend and Seasonality
Departemen Teknik Industri FTI-ITB
Forecasting Performance
• Mean Forecast Error (MFE or Bias): Measures average deviation of forecast from actuals.
• Mean Absolute Deviation (MAD): Measures average absolute deviation of forecast from actuals.
• Mean Absolute Percentage Error (MAPE): Measures absolute error as a percentage of the forecast.
• Standard Squared Error (MSE): Measures variance of forecast error
How good is the forecast?
Departemen Teknik Industri FTI-ITB
Forecasting Performance Measures
)(1
1
t
n
t
t FDn
MFE
n
t
tt FDn
MAD1
1
n
t t
tt
D
FD
nMAPE
1
100
2
1
)(1
t
n
t
t FDn
MSE
Departemen Teknik Industri FTI-ITB
• Want MFE to be as close to zero as possible -- minimum bias
• A large positive (negative) MFE means that the forecast is undershooting (overshooting) the actual observations
• Note that zero MFE does not imply that forecasts are perfect (no error) -- only that mean is “on target”
• Also called forecast BIAS
Mean Forecast Error (MFE or Bias)
)(1
1
t
n
t
t FDn
MFE
Departemen Teknik Industri FTI-ITB
Mean Absolute Deviation (MAD)
• Measures absolute error
• Positive and negative errors thus do not cancel out (as with MFE)
• Want MAD to be as small as possible
• No way to know if MAD error is large or small in relation to the actual data
n
t
tt FDn
MAD1
1
Departemen Teknik Industri FTI-ITB
Mean Absolute Percentage Error (MAPE)
• Same as MAD, except ...
• Measures deviation as a percentage of actual data
n
t t
tt
D
FD
nMAPE
1
100
Departemen Teknik Industri FTI-ITB
Mean Squared Error (MSE)
• Measures squared forecast error -- error variance
• Recognizes that large errors are disproportionately more “expensive” than small errors
• But is not as easily interpreted as MAD, MAPE -- not as intuitive
2
1
)(1
t
n
t
t FDn
MSE
Departemen Teknik Industri FTI-ITB
Fortunately, there is software...
Departemen Teknik Industri FTI-ITB
Homework
• Text book
• Problem Chapter 2 Number 16, 17, 22, 30, 33
• Due date – next week 10 Sept 2013
40
Departemen Teknik Industri FTI-ITB
41
JENIS POLA DATA
• Proses tetap (constant process)
Penjualan produk P
5.000
6.000
7.000
8.000
9.000
10.000
11.000
0 2 4 6 8 10 12
Bulan
Ju
mla
n (
10
00
bo
Departemen Teknik Industri FTI-ITB
42
JENIS POLA DATA …. • Kecenderungan (Trend process)
Penjualan produk Q
8.000
9.000
10.000
11.000
12.000
13.000
14.000
1 2 3 4 5 6 7 8 9 10 11 12
Bulan
Un
it
Departemen Teknik Industri FTI-ITB
43
JENIS POLA DATA …. • Siklus (Seasonal Process)
Penjualan produk perkantoran
-
100.000
200.000
300.000
400.000
500.000
600.000
700.000
1 2 3 4 5 6 7 8 9 10 11 12
Triwulan
Ju
ta R
p
Departemen Teknik Industri FTI-ITB
44
Qualitative Forecasting
1. Market Survey
2. Expert Opinian and the Deplhi Technique
Please check the approproate boxes
I do not own a 35 mm camera
I own Single Lens Refelx (SLR 35 mm camera
I onw an autofocus 35 mm camera
I plan to purchase a new SLR 35 mm camera in the next two years
I plan to purchase a new autofocus 35 mm camera in the next two years
I do not plan to purchase a new 35 mm camera in the next two years
Departemen Teknik Industri FTI-ITB
45
CAUSAL FORECASTING
1. SIMPLE LINIEAR REGRESSION
2. MULTI LINEAR REGRESSION
ntbhad ttt ,...,2,1
1bulan padan dikeluarka yg (IMB)bangunan mendirikanijin jumlah
bulan pada terjualyangset kitchen jumlah where
t-h
td
t
t
. 3322110 ttttt xbxbxbbd
termnoise
bulan padarusak yang phone-cellularjumlah
bulan pada phone-cellular harga
bulan pada potensial pembelijumlah
bulan pada terjualyang phone-cellularjumlah where
3
2
1
t
t
t
t
t
tx
tx
tx
td
Departemen Teknik Industri FTI-ITB
46
TIME SERIES FORCASTING
1. CONSTANT PROCESS :
a) Simple methods :
b) Moving Average:
c) Simple Exponential smoothing:
2. TREND PROCESS:
• Double exponential smoothing
3. SEASONAL PROCESS:
tt ad
T
t
tTTkT dT
ddF
1
1 dimana
T
NTt
tTTkT dN
MMF
1
1 dimana
1)1( dimana TTTTkT SdSSF
tt btad
ttt acd
Departemen Teknik Industri FTI-ITB
47
FORECAST ERROR
• Forecast Error = nilai actual – hasil peramalan
• Jenis Ukuran Forecast Error :
a) Mean Absolut deviatation (MAD) :
b) Mean Square Error (MSE) :
c) Mean absolut percentage Error (MAPE)
ttt Fde
T
t
teT
MAD
1
1
T
tt
eT
MSE
1
21
T
t t
t
d
e
TMAD
1
100.1