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## FORECASTING

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Title: FORECASTING

1
FORECASTING
2
FORECASTING
• Forecasts serve as a basis for planning--capacity,
budgeting, sales, production, inventory,
personnel
• Successful forecasting requires a skillful
blending of both art and science
• Two uses of forecasts
• Planning the system--Long Range
• Planning the use of the system--Short Range

3
Forecasting
• Assumes causal system
• past gt future
• Forecasts rarely perfect because of randomness
• Forecasts more accurate forgroups vs.
individuals
• Forecast accuracy decreases as time horizon
increases

I see that you willget an A this semester.
4
Elements of a Good Forecast
Timely
Accurate
Reliable
Written
Easy to use
Meaningful
5
Steps in the Forecasting Process
The forecast
6
APPROACHES TO FORECASTING
• QUALITATIVE--based on subjective inputs, soft
data
• judgmental forecasts, opinions, hunches,
experience, etc.
• QUANTITATIVE--based on historical data
• --project past experience into the future
• --uncover relationships between variables that
can be used to predict the future

7
Types of Forecasts
• Judgmental - uses subjective inputs
• Time series - uses historical data assuming the
future will be like the past
• Associative models - uses explanatory variables
to predict the future

8
Judgmental Forecasts
• Executive opinions
• Sales force composite
• Consumer surveys
• Outside opinion
• Opinions of managers and staff
• Delphi technique

9
QUANTITATIVE FORECASTS
• Time-Series techniques
• --Naïve
• --Moving Average models
• --Exponential Smoothing models
• --Classical Decomposition
• --Box-Jenkins ARIMA models
• --Neural Networks

10
QUANTITATIVE FORECASTS
• Causal or Associative techniques
• --Simple linear regression
• --Multiple linear regression
• --Nonlinear regression

11
FORECASTING DATA
• time-series
• --time-ordered sequence of observations taken
at regular intervals over a period of time
• Annual, Quarterly, Monthly, Weekly,
• Daily, Hourly, etc.

12
UNDERLYING BEHAVIOR
• Trend - long-term movement in data
• Seasonality - short-term, regular, periodic
variations in data
• Cycles - wave-like variations of more than one
years duration
• Irregular variations - caused by unusual
circumstances
• Random variations - caused by chance

13
Forecast Variations
Irregularvariation
Trend
Cycles
90
89
88
Seasonal variations
14
Naive Forecasts
Uh, give me a minute.... We sold 250 wheels
last week.... Now, next week we should
sell. the latest observation in a sequence is
used as the forecast for the next period Ft
At-1
15
Simple Moving Average
16
Exponential Smoothing
Ft Ft-1 a(At-1 - Ft-1)
• Premise--The most recent observations might have
the highest predictive value.
• Therefore, we should give more weight to the more
recent time periods when forecasting.

17
Forecast Accuracy
• Error difference between actual value
and
predicted value
• - Average absolute error
• Mean squared error (MSE)
• - Average of squared error
• Mean absolute percent error (MAPE)
• - Average absolute percent error
• Tracking Signal
• - Ratio of cumulative error and MAD

18