Ppc Chapter 3

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  • Topic: Moving average, Exponential smoothing, Time series analysis
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PERENCANAAN & PENGENDALIAN PRODUKSI
TIN 4113

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.

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)

Cakupan Peramalan
• Berdasarkan Kategori Tingkat Keputusan
– Tingkat Kebijakan – Tingkat Produk – Tingkat Proses – Tingkat Desain Pabrik – Tingkat Operasional

Cakupan Peramalan
• Berdasarkan Unit Bisnis
– Perencanaan Keuangan – Perencanaan Pemasaran – Perencanaan Produksi – Perencanaan Penjadwalan

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.

Demand Management
Where possible, calculate demand rather than forecast. If not possible... Independent Demand
(finished goods and spare parts)

A

Dependent Demand
(components)

B(4)

C(2)

D(2)

E(1)

D(3)

F(2)

Demand Estimates

Sales Forecast

Production Resource Forecast

Examples of Production Resource Forecasts
Forecast Horizon Time Span Item Being Forecast
• Product lines • Factory capacities • Planning for new products • Capital expenditures • Facility location or expansion • R&D • Product groups • Department capacities • Sales planning • Production planning and budgeting

Units of Measure

Long-Range

Years

Dollars, tons, etc.

MediumRange

Months

Dollars, tons, etc.

Short-Range

Weeks

• Specific product quantities • Machine capacities • Planning • Purchasing • Scheduling • Workforce levels • Production levels • Job assignments

Physical units of products

Klasifikasi Peramalan
• Kualitatif
– Sales force composite – Survey Pasar – Keputusan Manajemen (Jury of executive opinion) – The Delphi Method

• Kuantitatif
– Time series

Time Series
• Selalu menggunakan data historis (Naïve methods) • Komponen time series: – Trend – Seasonality – Cycles – Randomness

Simple Time Series Models
• • • • Moving Average (Simple & Weighted) Exponential Smoothing (Single) Double Exponential Smoothing (Holt’s) Winter’s Method for Seasonal Problems

• Forecast Ft is average of n previous observations or actuals Dt :

Simple Moving Average

Ft 1 Ft 1

1  ( Dt  Dt 1    Dt 1n ) n 1 t  D n i n i t 1

• 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.

Example of Simple Moving Average
Week 1 2 3 4 5 6 7 8 9 10 11 12 Demand 3-Week 6-Week 650 678 720 785 682.67 859 727.67 920 788.00 850 854.67 768.67 758 876.33 802.00 892 842.67 815.33 920 833.33 844.00 789 856.67 866.50 844 867.00 854.83

Weighted Moving Average
Forecast is based on n past demand data, each given a certain weight. The total weight must equal to 1.

Ft 1  ( wt Dt  wt 1Dt 1    wt 1n Dt 1n )

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)

Pertemuan 3 -...
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