PERENCANAAN & PENGENDALIAN PRODUKSI, Lecture notes of Product Development

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PERENCANAAN &

PENGENDALIAN PRODUKSI

TIN 4113

Pertemuan 2

• Outline: – Karakteristik Peramalan
  • – Cakupan PeramalanKlasifikasi Peramalan
  • – Metode Forecast:Simple Time Series Models: Time Series
• Referensi:^ •^ Moving Average (Simple & Weighted)
  • Smith, Spencer B. Control , Prentice-Hall,, Computer Based Production and Inventory 198 9.
  • Tersine, Richard J., Management , Prentice Principles of Inventory and Materials - 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

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

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...

Examples of Production Resource Forecasts

Forecast Horizon Time Span Item Being Forecast MeasureUnits of

Long-Range Years

• • Product linesFactory capacities
• • Planning for new productsCapital expenditures
• • Facility location or expansionR&D

Dollars, tons, etc.

Medium-Range Months^ •^ • • •^ Product groupsDepartment capacitiesSales planningProduction planning and

budgeting

Dollars, tons,etc.

Short-Range Weeks

• • Specific product quantitiesMachine capacities
• • PlanningPurchasing
• • SchedulingWorkforce levels
• • Production levelsJob assignments

Physical unitsof products

Klasifikasi Peramalan

• Kualitatif
  • Sales force composite
  • Survey Pasar
  • Keputusan Manajemen (Jury of executive opinion)
  • The Delphi Method
• Kuantitatif
  • Time series

Simple Time Series Models

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

• Forecast F Simple Moving Average

t 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 thanRequires that n past observations be retained; n are not included at all;
  • Problem when 1000's of items are being forecast.

   

t t i t n i

t t t t n

F n D

F n D D D

(^11)

1 1 1

Weighted Moving Average

Ft (^)  1  ( wtDt  wt  1 Dt  1  wt  1  nDt  1  n )

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)

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