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- 1. CHAPTER THREE
Irwin/McGraw-Hill ©The McGraw-Hill Companies, Inc., 1999
FORCASTING
3-1
FORECASTING
PART TWO
•Chapter Three
•Forecasting
Irwin/McGraw-Hill ©The McGraw-Hill Companies, Inc., 1999
- 4. CHAPTER THREE
Irwin/McGraw-Hill ©The McGraw-Hill Companies, Inc., 1999
FORCASTING
3-4
• Assumes causal system
past ==> future
• Forecasts rarely perfect because of
randomness
• Forecasts more accurate for
groups vs. individuals
• Forecast accuracy decreases
as time horizon increases
I see that you will
get an A this semester.
- 5. CHAPTER THREE
Irwin/McGraw-Hill ©The McGraw-Hill Companies, Inc., 1999
FORCASTING
3-5
Steps in the Forecasting Process
Step 1 Determine purpose of forecast
Step 2 Establish a time horizon
Step 3 Select a forecasting technique
Step 4 Gather and analyze data
Step 5 Prepare the forecast
Step 6 Monitor the forecast
“The forecast”
- 6. CHAPTER THREE
Irwin/McGraw-Hill ©The McGraw-Hill Companies, Inc., 1999
FORCASTING
3-6
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
- 7. CHAPTER THREE
Irwin/McGraw-Hill ©The McGraw-Hill Companies, Inc., 1999
FORCASTING
3-7
Judgmental Forecasts
• Executive opinions
• Sales force composite
• Consumer surveys
• Outside opinion
• Opinions of managers and staff
– Delphi technique
- 8. CHAPTER THREE
Irwin/McGraw-Hill ©The McGraw-Hill Companies, Inc., 1999
FORCASTING
3-8
Time Series Forecasts
• Trend - long-term movement in data
• Seasonality - short-term regular
variations in data
• Irregular variations - caused by
unusual circumstances
• Random variations - caused by
chance
- 9. CHAPTER THREE
Irwin/McGraw-Hill ©The McGraw-Hill Companies, Inc., 1999
FORCASTING
3-9
Forecast Variations
Trend
Irregular
variatio
n
Cycles
Seasonal variations
90
89
88
Figure 3-1
- 11. CHAPTER THREE
Irwin/McGraw-Hill ©The McGraw-Hill Companies, Inc., 1999
FORCASTING
3-11
Naive Forecasts
Uh, give me a minute....
We sold 250 wheels last
week.... Now, next
week we should sell....
- 12. CHAPTER THREE
Irwin/McGraw-Hill ©The McGraw-Hill Companies, Inc., 1999
FORCASTING
3-12
Simple Moving Average
600
700
800
900
1 2 3 4 5 6 7 8 9 10 11 12 13
A c tual
MA 3
MA 5
MAn =
n
Ai
i = 1
n
Figure 3-4
- 13. CHAPTER THREE
Irwin/McGraw-Hill ©The McGraw-Hill Companies, Inc., 1999
FORCASTING
3-13
Exponential Smoothing
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.
Ft = Ft-1 + (At-1 - Ft-1)
- 14. CHAPTER THREE
Irwin/McGraw-Hill ©The McGraw-Hill Companies, Inc., 1999
FORCASTING
3-14
Picking a Smoothing Constant
9 0 0
9 5 0
1 0 0 0
1 0 5 0
1 1 0 0
1 1 5 0
1 2 0 0
1 2 5 0
1 3 0 0
1 3 5 0
1 4 0 0
A p r i l J u l y O c t o b e r J a n u a r y
D e m a n d
F ( . 0 5 )
F ( . 2 )
.2
.05
- 16. CHAPTER THREE
Irwin/McGraw-Hill ©The McGraw-Hill Companies, Inc., 1999
FORCASTING
3-16
Linear Trend Equation
• b is similar to the slope. However,
since it is calculated with the
variability of the data in mind, its
formulation is not as straight-
forward as our usual notion of
slope.
Yt = a + bt
0 1 2 3 4 5 t
Y
- 18. CHAPTER THREE
Irwin/McGraw-Hill ©The McGraw-Hill Companies, Inc., 1999
FORCASTING
3-18
Linear Trend Equation Example
t y
Week t2
Sales ty
1 1 150 150
2 4 157 314
3 9 162 486
4 16 166 664
5 25 177 885
t = 15 t2
= 55 y = 812 ty = 2499
(t)2
= 225
- 19. CHAPTER THREE
Irwin/McGraw-Hill ©The McGraw-Hill Companies, Inc., 1999
FORCASTING
3-19
Linear Trend Calculation
y = 143.5 + 6.3t
a =
812 - 6.3(15)
5
=
b =
5 (2499) - 15(812)
5(55) - 225
=
12495-12180
275-225
= 6.3
143.5
- 20. CHAPTER THREE
Irwin/McGraw-Hill ©The McGraw-Hill Companies, Inc., 1999
FORCASTING
3-20
Associative Forecasting
• Predictor variables - used to
predict values of variable interest
• Regression - technique for fitting a
line to a set of points
• Least squares line - minimizes sum
of squared deviations around the
line
- 21. CHAPTER THREE
Irwin/McGraw-Hill ©The McGraw-Hill Companies, Inc., 1999
FORCASTING
3-21
Linear Model Seems Reasonable
0
10
20
30
40
50
0 5 10 15 20 25
X Y
7 15
2 10
6 13
4 15
14 25
15 27
16 24
12 20
14 27
20 44
15 34
7 17
Computed
relationship
- 22. CHAPTER THREE
Irwin/McGraw-Hill ©The McGraw-Hill Companies, Inc., 1999
FORCASTING
3-22
Forecast Accuracy
• Error - difference between actual
value and predicted value
• Mean absolute deviation (MAD)
– Average absolute error
• Mean squared error (MSE)
– Average of squared error
• Tracking signal
– Ratio of cumulative error and MAD
- 24. CHAPTER THREE
Irwin/McGraw-Hill ©The McGraw-Hill Companies, Inc., 1999
FORCASTING
3-24
Tracking Signal
Tracking signal =
(Actual-forecast)
MAD
Tracking signal =
(Actual-forecast)
Actual-forecast
n
