Learning Outcomes
• Mahasiswa akan dapat menjelaskan definisi, pengertian
dan proses model ramalan.
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Outline Materi:
•
•
•
•
•
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Definisi ramalan
Pengertian Trend/ramalan
Model proses ramalan.
Metoda ramalan.
Contoh kasus..
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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Sales will
be $200
Million!
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 < 3 months.
– Job scheduling, worker assignments.
• Medium-range forecast: 3 months to 3 years.
– Sales & production planning, budgeting.
• Long-range forecast: > 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
Introduction
Growth
Hard to forecast.
Forecasting
critical, both for
future magnitude
and growth rate.
Need long-range
forecasts.
Often use
qualitative
models.
Maturity
Easier to
forecast.
Use quantitative
models.
Long-range
forecasts still
important.
Sales
Time
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Decline
Hard to forecast,
but forecasting
is less important.
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 little data or time exist.
•
– New products & technology.
– Long time horizon.
– Major changes expected.
•
– Existing products & current
technology.
– No significant changes
expected.
Involves intuition, experience.
– Example: forecasting for
commerce sales.
e-
Used when situation is ‘stable’ &
historical data exist.
•
Involves mathematical
techniques.
– Example: forecasting sales of
color televisions.
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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
Models
Time Series
Models
Moving
Average
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Exponential
Smoothing
Trend
Projection
Linear
Regression
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:
Sales:
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1
78.7
2
63.5
3
89.7
4
93.2
5
92.1
Time Series Components
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Trend
Cyclical
Seasonal
Random
Demand for product or service
Product Demand over 4 Years
Year
1
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Year
2
Year
3
Year
4
Product Demand over 4 Years
Trend component
Demand for product or service
Seasonal peaks
Random
variation
Year
1
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Year
2
Actual
demand line
Year
3
Year
4
Cyclic
component
Trend Component
• Persistent, overall upward or downward pattern.
• Due to population, technology etc.
• Several years duration.
Time
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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.
Summer
Demand
Time
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Cyclical Component
• Repeating up & down movements.
• Due to interactions of factors influencing economy.
• Usually 2-10 years duration.
Cycle
Demand
Year
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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:
• Additive model:
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Yi = Ti · Si · Ci · Ri
Yi = Ti + Si + Ci + Ri
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