Forecasting
Introduction
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What: Forecasting Techniques
Where: Determine Trends
Why: Make better decisions
What is Forecasting?
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The art and science of predicting future
events
Time Horizon
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Short Range – 3 – 12 months
Medium Range – 3 months – 3 years
Long Range – 3+ years
Where do we Use Forecasts?
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Economic Forecast
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Technological Forecast
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Inflation rate
Exchange Rate
Probabilities of new discoveries
Time to commercialize technologies
Demand Forecast
Impact of Forecasts
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Human Resources: forecast gives
warning of need to hire or lay off
Production Capacity: forecast gives
warning of need for more or less
capacity
Supply Chain: forecast gives warning of
need for more or less inputs to
production
How to Make a Forecast
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Determine use of forecast
Select variable to be forecasted
Determine time horizon
Select forecasting model
Gather data
Make forecast
Implement results and review model
Qualitative Methods
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Jury of Executive Opinion
Sales Force Composite
Delphi
Consumer Marketing Survey
Quantitative Methods
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Time Series
Associative
Time Series Methods
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A sequence of evenly spaced data
points (weekly, monthly, quarterly, etc)
Future values predicted only from past
values
X axis is always time
Example of a Time Series
Demand for product or service
Seasonal peaks
Trend component
Actual
demand line
Random
variation
Year
1
Year
2
Average demand
over four years
Year
3
Year
4
Trend
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Upward or downward pattern
Due to changes in income, population,
technology, etc
Several years duration
Demand
Time
© 1984-1994 T/Maker Co.
Seasonality
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Repeating pattern over a period
Could be quarterly, monthly, weekly
Due to weather or customs
Summer
Demand
© 1984-1994 T/Maker Co.
Time
Cycles
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Pattern that occurs over several years
Affected by political events or
international turmoil
Cycle
Demand

Time
Random Variations
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Erratic, unsystematic
Caused by random chance and unusual
situations
Short duration, non-repeating
Naïve Approach
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Forecast for next period is the same as
demand in most recent period
Moving Average Approach
Demand in Previous n Periods

MA 
n
Weighted Moving Average
WMA =
Σ(Weight for period n) (Demand in period n)
ΣWeights
Exponential Smoothing
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Ft = Ft-1 + (At-1 - Ft-1)
MAD
MAD 
 forecast errors
n
MSE
forecast errors 
MSE 
n
2
Exponential Smoothing With
Trend Adjustment
Ft = (At) + (1- )Ft-1 + Tt-1
Tt = (Ft - Ft-1) + (1- )Tt-1
Linear Trend Projection
Equation:
Ŷi  a  bx i
n
Slope:
 x i y i  nx y
b  i n
 x i  nx 
i 
Y-Intercept:
a  y  bx
Seasonal Variations
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Calculate average historical demand for each
season
Compute average demand over all periods
Compute a seasonal index – historical
demand / average demand
Estimate next year’s total demand
Divide estimate by number of seasons,
multiply by seasonal index
Regression Analysis
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An associative method
Find the relationship between an
independent variable and a dependant
variable
Independent variable is a variable other
than time
Regression Analysis
Equation:
Ŷi  a  bx i
n
Slope:
 x i y i  nx y
b  i n
 x i  nx 
i 
Y-Intercept:
a  y  bx
Standard Error of Estimate
n

yi  ŷi 
S y,x  i 

n
n 
n
n
 yi  a  yi  b  x i yi
i 
i 
i 
n
What Does Standard Error
Mean?
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Standard Deviation of data forming the
regression line.
If error becomes large, regression data
is widely dispersed and less reliable
Correlation Coefficient
r
n x
n  xy   x  y
2

  x  n  y   y 
2
2
2
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What Does Correlation
Coefficient Mean?
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Strength of linear relationship between
independent variable and dependant
variable
A number between +1 and -1
What Does Correlation
Coefficient Mean?
Perfect
Negative
Correlation
-1.0
Perfect
Positive
Correlation
No Correlation
-.5
Increasing degree of
negative correlation
0
+.5
+1.0
Increasing degree of
positive correlation
Coefficient of Determination
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r2
Percent of variation in dependant
variable that is explained by the
regression equation
Evaluating the Forecast
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Monitor the forecast with a tracking
signal
= RSFE / MAD
Small deviations are ok and should
cancel each other out over time
A consistent tendency for the forecast
to be higher or lower than actual values
is called a bias error
Tracking Signal Limits
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+/- 2, 3 or 4 MAD’s
Smaller range = less tollerance of error
But smaller range = higher costs
Other Ways to Forecast
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Adaptive Smoothing – Exponential
smoothing constants adapted when
tracking signal outside limits
Focus Forecasting – Computer tries all
forecast methods and selects best fit for
next month’s forecast