Model Ramalan Peretemuan 13:

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Model RamalanPeretemuan 13

• Mata kuliah K0194-Pemodelan Matematika Terapan
• Tahun 2008

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Learning Outcomes

• Mahasiswa akan dapat menjelaskan definisi,
  pengertian dan proses model ramalan.

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Outline Materi

• Definisi ramalan
• Pengertian Trend/ramalan
• Model proses ramalan.
• Metoda ramalan.
• Contoh kasus..

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What is Forecasting?

• Art and science of predicting future events.
• Underlying basis of all business decisions.
• Production Inventory.
• Personnel Facilities.
• Focus on forecasting demand.

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Examples

• Predict the next number in the pattern
• a) 3.7, 3.7, 3.7, 3.7, 3.7, ?
• b) 2.5, 4.5, 6.5, 8.5, 10.5, ?
• c) 5.0, 7.5, 6.0, 4.5, 7.0, 9.5,
  8.0, 6.5, ?

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Examples

• Predict the next number in the pattern
• a) 3.7, 3.7, 3.7, 3.7, 3.7,
  y 3.7
• b) 2.5, 4.5, 6.5, 8.5, 10.5,
  y 0.5 2x
• c) 5.0, 7.5, 6.0, 4.5, 7.0, 9.5,
  8.0, 6.5,
• y 4.5 0.5x ci
• c1 0 c2 2 c3 0 c4 -2 etc

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Types of Forecasts by Time Horizon

• Short-range forecast Usually lt 3 months.
• Job scheduling, worker assignments.
• Medium-range forecast 3 months to 3 years.
• Sales production planning, budgeting.
• Long-range forecast gt 3 years.
• New product planning, facility location.

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Short- vs. Long-term Forecasting

• Medium Long range forecasts
• Long range for design of system.
• Deal with comprehensive issues.
• Support management decisions regarding planning.
• Short-term forecasts
• To plan detailed use of system.
• Usually use quantitative techniques.
• More accurate than longer-term forecasts.

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Forecasting During the Life Cycle
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Eight Steps in Forecasting

• Determine the use of the forecast.
• Select the items to be forecast.
• Determine the time horizon of the forecast.
• Select the forecasting model(s).
• Gather the data.
• Make the forecast.
• Validate and implement results.
• Monitor forecasts and adjust when needed.

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Realities of Forecasting

• Assumes future will be like the past (causal
  factors will be the same).
• Forecasts are imperfect.
• Forecasts for groups of product are more accurate
  than forecasts for individual products.
• Accuracy decreases with length of forecast.

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Forecasting Approaches
Qualitative Methods
Quantitative Methods

• Used when situation is stable historical data
  exist.
• Existing products current technology.
• No significant changes expected.
• Involves mathematical techniques.
• Example forecasting sales of color televisions.

• Used when little data or time exist.
• New products technology.
• Long time horizon.
• Major changes expected.
• Involves intuition, experience.
• Example forecasting for e-commerce
  sales.

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Overview of Qualitative Methods

• Jury of executive opinion.
• Combine opinions from executives.
• Sales force composite.
• Aggregate estimates from salespersons.
• Delphi method.
• Query experts interatively.
• Consumer market survey.
• Survey current and potential customers.

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Quantitative Forecasting Methods
Quantitative
Forecasting
Associative
Time Series
Models
Models
Linear
Exponential
Moving
Trend
Smoothing
Average
Regression
Projection
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What is a Time Series?

• Set of evenly spaced numerical data.
• From observing response variable at regular time
  periods.
• Forecast based only on past values.
• Assumes that factors influencing past will
  continue influence in future.
• Example
• Year 1 2 3 4 5
• Sales 78.7 63.5 89.7 93.2 92.1

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Time Series Components
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Product Demand over 4 Years
Demand for product or service
Year 1
Year 2
Year 3
Year 4
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Product Demand over 4 Years
Trend component
Seasonal peaks
Demand for product or service
Cyclic component
Actual demand line
Random variation
Year 1
Year 2
Year 3
Year 4
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Trend Component

• Persistent, overall upward or downward pattern.
• Due to population, technology etc.
• Several years duration.

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Seasonal Component

• Regular pattern of up down fluctuations.
• Due to weather, customs etc.
• Occurs within 1 year.
• Quarterly, monthly, weekly, etc.

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Cyclical Component

• Repeating up down movements.
• Due to interactions of factors influencing
  economy.
• Usually 2-10 years duration.

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Random Component

• Erratic, unsystematic, residual fluctuations.
• Due to random variation or unforeseen events.
• Union strike
• Tornado
• Short duration non-repeating.

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General Time Series Models

• Any value in a time series is a combination of
  the trend, seasonal, cyclic, and random
  components.
• Multiplicative model Yi Ti Si Ci Ri
• Additive model Yi Ti Si Ci Ri

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