PERENCANAAN & PENGENDALIAN...
Transcript of PERENCANAAN & PENGENDALIAN...
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PERENCANAAN & PENGENDALIAN PRODUKSI
TIN 4113
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Pertemuan 2
• Outline: – Karakteristik Peramalan – Cakupan Peramalan – Klasifikasi Peramalan – Metode Forecast: Time Series – Simple Time Series Models:
• Moving Average (Simple & Weighted)
• Referensi: – Smith, Spencer B., Computer Based Production and Inventory
Control, Prentice-Hall, 1989. – Tersine, Richard J., Principles of Inventory and Materials
Management, Prentice-Hall, 1994. – Pujawan, Demand Forecasting Lecture Note, IE-ITS, 2011.
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Memprediksi masa depan...
Hal yang sangat sulit!!!!! Every woman is frightened of a mouse.
MGM head Louts B. Mayer in 1926, to young cartoonist named Walt Disney
640k ought to be enough for anybody.
Bill Gates, Microsoft founder, 1981
The Internet will collapse within a year. Bob Metcalf, founder of 3Com Corporation, in December 1995
Sumber: Forecasting for the Pharmaceutical Industry (Cook, 2006)
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Cakupan Peramalan
• Berdasarkan Kategori Tingkat Keputusan
– Tingkat Kebijakan
– Tingkat Produk
– Tingkat Proses
– Tingkat Desain Pabrik
– Tingkat Operasional
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Cakupan Peramalan
• Berdasarkan Unit Bisnis
– Perencanaan Keuangan
– Perencanaan Pemasaran
– Perencanaan Produksi
– Perencanaan Penjadwalan
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Characteristic of Forecasts
• Forecast involves error >>> they are usually wrong
• Family forecast are more accurate than item forecast. Aggregate forecasts are more accurate.
• Short-range forecasts are more accurate than long-range forecasts
• A good forecast is more than a single number.
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Demand Management
A
Independent Demand (finished goods and spare parts)
B(4) C(2)
D(2) E(1) D(3) F(2)
Dependent Demand (components)
Where possible, calculate demand rather than forecast. If not possible...
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Demand Estimates
Sales Forecast
Production Resource Forecast
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Examples of Production Resource Forecasts
Forecast
Horizon Time Span Item Being Forecast
Units of
Measure
Long-Range Years
• Product lines
• Factory capacities
• Planning for new products
• Capital expenditures
• Facility location or expansion
• R&D
Dollars, tons,
etc.
Medium-
Range Months
• Product groups
• Department capacities
• Sales planning
• Production planning and
budgeting
Dollars, tons,
etc.
Short-Range Weeks
• Specific product quantities
• Machine capacities
• Planning
• Purchasing
• Scheduling
• Workforce levels
• Production levels
• Job assignments
Physical units
of products
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Klasifikasi Peramalan
• Kualitatif
– Sales force composite
– Survey Pasar
– Keputusan Manajemen (Jury of executive opinion)
– The Delphi Method
• Kuantitatif
– Time series
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Time Series
• Selalu menggunakan data historis (Naïve methods)
• Komponen time series:
– Trend
– Seasonality
– Cycles
– Randomness
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Simple Time Series Models
• Moving Average (Simple & Weighted)
• Exponential Smoothing (Single)
• Double Exponential Smoothing (Holt’s)
• Winter’s Method for Seasonal Problems
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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
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Example of Simple Moving Average
Week Demand 3-Week 6-Week
1 650
2 678
3 720
4 785 682.67
5 859 727.67
6 920 788.00
7 850 854.67 768.67
8 758 876.33 802.00
9 892 842.67 815.33
10 920 833.33 844.00
11 789 856.67 866.50
12 844 867.00 854.83
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Weighted Moving Average
)( 11111 ntntttttt DwDwDwF
Forecast is based on n past demand data, each
given a certain weight. The total weight must equal
to 1.
Re-do the above example, using 3 past data, each given
a weight of 0.5, 0.3, and 0.2 (larger for more recent data)
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Pertemuan 3 - Persiapan
• Tugas Baca: – Metode Peramalan:
• Simple Time Series Model: – Exponential Smoothing (Single)
– Double Exponential Smoothing (Holt’s)
– Winter’s Method for Seasonal Problems
– Error Forecast • MAD
• MSE
• MAPE
• MFE atau Bias
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