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8/10/2019 4. Demand Forecasting 1_2
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DEMAND FORECASTINGPART 1
Minggu 4
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Before making an investment decision, must
answer these question:
What should be the size or amount capitalrequired?
How large should be the size of workforce?
What should be the size of the order and safetystock?
What should be the capacity of the plant?
NEED FORECAST
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DEFINITION FORECASTING
(American Marketing Association)
An estimate of sales in physical units for a specifiedfuture period under proposed marketing plan or program
and under the assumed set of economic and other
forces outside the organisation for which the forecast is
made
Forecasting is an estimateof future eventachieved by systematically combining and casting in
predetermined way data about the past.
Forecasting is based on the historical data and its
requires statistical and management science techniques.
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Need for Demand Forecasting
Majority of the activities is depend on the future sales
Projected demand for the future assists in decision making with
respect to investment in plant and machinery, market planning
and programs.
To schedule the production activity to ensure optimum utilisation
of plants capacity
To prepare material planning to take up replenishment action to
make the materials available at right quantity and right time
To provide an information about the relationship between
demand for different products
To provide a future trend which is very much essential for product
design and development
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Forecasting Approaches
Qualitative Methods
Incorporate such factors as the decision makers intuition, emotions,
personal experiences, and value system in reaching a forecast.
Quantitative Methods
It use a variety of mathematical models that rely in historical data or
associative variables to forecast demand.
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Individual Opinion:Opini peramalan berasal dari pribadi(Individu) pakar/expert dalam bidangnya
yaitu :
- Konsultan : Ilmiah / non Ilmiah
- Manajer pemasaran / produksi
- Individu yang banyak bergerak padamasalah tersebut.
Group Opinion :Opini peramalan diperoleh dari beberapa
orang dengan mencoba merata-ratakanhasil peramalan yang lebih obyektif
(rasional)
Qualitative Forecasting
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MACAM-MACAM GROUP OPINION:
Riset Pasar Berguna bila ada kekurangan data historik atau data tidak reliabel.
Tahapan dalam riset pasar:
Memastikan informasi yang dicari
Memastikan sumber-sumber informasi
Menetapkan cara pengadaan atau pengumpulan data
Mengembangkan uji pendahuluan peralatan pengukuran
Menformulasikan sampel
Mendapatkan informasi
Melakukan tabulasi dan analisa data
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Metode Delphi
Teknik yang digunakan untuk mendapatkan konsensus pendapat dari
kelompok ahli kemudian mengumpulkan dan menformulasikan daftar
pertanyaan baru dan dibagikan kepada kelompok.
Analogi historik
Peramalan dilakukan dengan menggunakan pengalaman historik
produk sejenis.
Konsensus Panel
Gagasan yang didiskusikan secara terbuka oleh kelompok untuk
menghasilkan ramalan yang lebih baik daripada dilakukan seseorang.Partisipan terdiri dari: eksekutif, orang penjualan, para ahli dan
langganan
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Quantitative Forecasting
Time Series Analysis
Identifies the historical pattern of demand for the product
or project and extrapolates this demand into the future.
Past data is arranged in a chronological order as a
dependent variable and time as an independent variable
Causal Methods
Identifies the factors which cause the variation of demand
and tries to establish a relationship between the demandand these factorsnot only depend on time variable.
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Faktor-faktor yang berpengaruh:
- harga produk
- saluran distribusi
- promosi
- pendapatan- jumlah penduduk, dll
dt = f (faktor penyebab demand)
Pada metode ini diperlukan : - identifikasi variabel yang relevan- mencari fungsi yang cocok
Kebaikan : - mempunyai ketepatan hasil yang tinggi
- dapat digunakan untuk peramalan jangka panjang
Kelemahan : - tidak praktis, membutuhkan banyak jenis data
- waktu lama
- mahal
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Forecasting :upaya memperkecil resiko yang mungkin
timbul akibat pengambilan keputusan dalam
suatu perencanaan produksiNamun, upaya memperkecil resiko dibatasi
oleh biaya
Biaya totalBiaya peramalan
resiko
Biaya
Upaya
peramalan
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Metode
peramalan
Model
kualitatif
Model
kuantitatif
Time
series
kausal
smoothing
regresi
ekonometri
Regresi
multivariate
Moving
average
Exponential
smoothing
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Faktor-faktor yang harus dipertimbangkan dalam pemilihan
metode peramalan :
- tujuan peramalan- jangkauan peramalan
- tingkat ketelitian
- ketersediaan data
- bentuk pola data
- biayaHal-hal yang harus dilakukan :
- definisikan tujuan peramalan
- buat diagram pencar
- pilih beberapa metode peramalan- hitung ramalan dan kesalahannya
- pilih metode dengan kesalahan terkecil
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JENIS POLA DATA :
- Konstan
- Trend (linier )
- Musiman (seasional)
- Cyclic (siklis)
Fungsi peramalan:
- Konstan : dt = a
- Trend (linier) : dt = a + bt
- Kwadratis : dt = a + bt + ct2
- Eksponential : dt = a.ebt
- Cyclic (siklis) : dt = a + b sin cos
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Kriteria Performansi peramalan :
1. Mean square error (MSE)
Xt = data aktual pada periode t
Ft= data ramalan pada periode t
n = banyaknya periode
2. Presentase kesalahan ( PEt )
3. Mean Absolute Percentage error (MAPE)
n
t
t
ndtXMSE
1
2
'
%100.
'
t
ttt
X
dtXPe
n
t
t
n
PEMAPE
1
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4. Standar Error Of Estimate (SEE)
f = derajat bebas
1 = untuk data konstan
2 = untuk data linier
3 = untuk data kwadratis
Contoh :
Dari12 bulan terakhir ini dicatat penjualan produk x sbb
Bagaimana ramalan permintaan produk x untuk 12 bula
mendatang ?
Bulan J F M A M J J A S O N D
Penjualan 30 20 45 35 30 60 40 50 45 65 50 35
n
t
t
fn
dtXSEE
1
2'
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dt = f(t)
Konstan :
a = 30 + 20 + . + 50 + 35 = 42
12
dt = 42
n
dt
a
andt
adt
n
t
n
t
n
t
n
t
1
1
1 1
.
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dt = y(t) Ramalan (dt) e = dt dt e2= (dt-dt)2
1. 302. 20
3. 45
4. 35
5. 30
6. 60
7. 40
8. 50
9. 45
10. 6511. 50
12. 35
4242
42
42
42
42
42
42
42
4242
42
-12-22
3
-7
-12
18
-2
8
3
238
- 7
144484
9
49
144
324
4
64
9
52964
49
Jumlah 1873
MENCARI SEE :
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n
t fn
dtdt
1
2)'(
SEE
05,1327,170
112
1873
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TRENDdt = a + bt
2
11
2
1 1 1
.
..
n
t
n
t
n
t
n
t
n
t
ttN
ttytytN
b
2
1 1
2
1 1 1
.
..
n
t
n
t
n
t
n
t
n
t
ttN
tdtdttN
b
atau
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N
t
bN
dt
a
n
t
n
t
11
btdta
N
t
b
N
dt
a
n
t
n
t
11
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T dt = y(t) t.dt t2 dt dt-dt (dt-dt)2
1.
2.
3.4.
5.
6.
7.8.
9.
10.
11.
12.
30
20
4535
30
60
4050
45
65
50
35
30
40
135140
150
360
280400
405
650
550
420
1
4
916
25
36
4964
81
100
121
144
31
33
3537
39
41
4345
47
49
51
53
-1
-13
10-2
-9
19
-35
-2
16
-1
-18
1
169
1004
81
361
925
4
256
1
324
= 78
= 6,5
505
= 423560 650 = 1335
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b = 12.(3560)505.78
12(650)(78.78)
= 3330 = 1,94
1716
= 421,94 (6,5)
= 4212,61
= 29,39 dt = 29,39 + 1,94t ~ dt = 29 + 2t
55,11
5,133212
1335
1
SEE
SEE
fn
dtdt
SEE
n
t
i
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Untuk regresi konstan : dt= 42
SEE = 13,05
Untuk regresi linier : dt
= 29 + 2tSEE = 11,55