Transcript Forecasting - Bradley University

Forecasting
Ross L. Fink
Forecasting
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Definition--Forecasting tries to predict
the future
Used for planning purposes
Forecasting goes beyond predicting
demand
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Internal manufacturing costs
Cost of raw materials and component parts
Cost and availability of energy
Interest rates
Stock prices
Rules of Forecasting
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Your forecast is never correct
Forecasts assume some underlying causal
system
Generally, the shorter the time horizon of
the forecast, the more accurate the
forecast
Generally, forecasting aggregations
(groups of items) is more accurate than
forecasting individual items
Forecasting Methods
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Qualitative
Quantitative
Qualitative Forecasting Methods
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Consumer surveys
Test marketing
Sales force composite
Executive opinion
Delphi technique
Panel of experts
Quantitative Forecasting
Methods
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Time series
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Naïve
Moving averages
Exponential smoothing
Box-Jenkins
Associative or causal models
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Simple regression
Multiple regression
Decomposition of Time Series
Data
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Believe that data consists of components
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Trend
Seasons
Cycles
Random and irregular variations
Approach--isolate components and
forecast separately and then recombine to
obtain final forecast
Ways to Select Forecasting
Method
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Noise-dampening (smoothing)
Response (lag)
Error measurement
Error Measurement
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Mean Absolute Deviation (MAD)--Most
popular
Average Error (AE) or bias
Mean Square Error (MSE)
Mean Absolute Percent Error (MAPE)
Standard deviation
Error
Et  At  Ft
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where,
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Et = error for period t
At = actual demand for period t
Ft = forecast for period t
Mean Absolute Deviation
n
MAD 
E
t 1
n
t
Example
Month
1
2
3
4
5
6
7
8
9
10
11
12
Demand
11.98
9.91
11.11
12.4
11.48
10.86
11.86
13.07
14.06
15.27
13.62
17.1
Example
Demand
18
16
14
12
10
8
1
2
3
4
5
6
7
Month
8
9
10
11
12
Example--3-month Moving
Average
Month
1
2
3
4
5
6
7
8
9
10
11
12
13
Demand
11.98
9.91
11.11
12.4
11.48
10.86
11.86
13.07
14.06
15.27
13.62
17.1
Forecast
3-mo. MA
11.00
11.14
11.66
11.58
11.40
11.93
13.00
14.13
14.32
15.33
Example
18
16
Demand
14
Forecast
3-mo. MA
12
10
8
1 2 3 4
5 6 7 8 9 10 11 12 13
Example
18
16
Demand
14
12
Forecast
3-mo. MA
10
Forecast
4-mo MA
8
1 2 3 4
5 6 7 8 9 10 11 12 13
Simple Exponential Smoothing
Ft  Ft 1    At 1  Ft 1 
OR
Ft  1   Ft 1  At 1
Alpha (smoothing Constant) is between 0
and 1