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Module: Forecasting Operations Management as a Competitive Weapo
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Module:
Forecasting
Operations Management as a Competitive Weapon
2Module: Forecasting
Learning ObjectivesAt the end of this module, each student will be able
to:
1. Describe forecasting
2. Describe time series
3. Explain forecast selection and monitoring
3Module: Forecasting
1. What is Forecasting?Process of predicting
a future eventUnderlying basis of
all business decisions
Company needsOperations needs
Sales will be $200 Million!
4Module: Forecasting
Major Demand Components
Average demand for the period Trend Cyclical Seasonal Random
5Module: Forecasting
Realities of Forecasting
Forecasts are seldom perfect Most forecasting methods assume
that there is some underlying stability in the system
Both product family and aggregated product forecasts are more accurate than individual product forecasts
6Module: Forecasting
Forecast based only on past values Assumes that factors influencing past,
present, & future will continue
Example:Year: 1999 2000 2001 2002 2003
2004Sales: 78.7 63.5 89.7 93.2 92.1 ?
2. Time Series
7Module: Forecasting
Form of weighted moving average Weights decline exponentially Most recent data weighted most
Requires smoothing constant () Ranges from 0 to 1 Subjectively chosen
Involves little record keeping of past data
Exponential Smoothing Method
8Module: Forecasting
You’re organizing a Kwanza meeting. You want to forecast attendance for 2004 using exponential smoothing ( = .10). The 2003 forecast was 175, actual was 190..
© 1995 Corel Corp.
Exponential Smoothing Example
9Module: Forecasting
Exponential Smoothing Solution
Ft = Ft-1 + · (At-1 - Ft-1)
F2004 = F2003 + · (A2003 – F2003) = 175 + 0.10 (180 – 175) = 175 + 0.10 (5) = 175.5
10Module: Forecasting
You want to achieve: Low forecast error pattern Low forecast error size
3. Forecasting Selection Guidelines
11Module: Forecasting
Desired Pattern
Time (Years)
Error
0
Time (Years)
Error
0
Trend Not Fully Accounted for
Pattern of Forecast Error
12Module: Forecasting
Forecast Error Equations
MSE
A F
nForecast errors
n
i ii
n
2
1
2
Mean Squared Error
MADA F
nForecast errors
n
i ii
n
1
Mean Absolute Deviation
13Module: Forecasting
You’re a marketing analyst for Hasbro Toys. You’ve forecast sales with two models. Which model should you use?
Actual Model 1 Model 2 Year Sales Forecast Forecast
1 10 6 102 10 13 103 20 20 194 20 27 205 40 34 38
Selecting a Forecasting Model
15Module: Forecasting
Forecasting Model SelectionModel 1
Year Actual Forecast Error Error2 |Error|1 10 6 4 16 4 MAD2 10 13 -3 9 3 43 20 20 0 0 04 20 27 -7 49 7 MSE5 40 34 6 36 6 22
0 110 20Model 2
Year Actual Forecast Error Error2 |Error|1 10 10 0 0 0 MAD2 10 10 0 0 0 0.63 20 19 1 1 14 20 20 0 0 0 MSE5 40 38 2 4 2 1
3 5 3
16Module: Forecasting
Forecasting Model SelectionModel 1
Year Actual Forecast Error Error2 |Error|1 10 6 4 16 4 MAD2 10 13 -3 9 3 43 20 20 0 0 04 20 27 -7 49 7 MSE5 40 34 6 36 6 22
0 110 20Model 2
Year Actual Forecast Error Error2 |Error|1 10 10 0 0 0 MAD2 10 10 0 0 0 0.63 20 19 1 1 14 20 20 0 0 0 MSE5 40 38 2 4 2 1
3 5 3
17Module: Forecasting
Forecasting Model SelectionModel 1
Year Actual Forecast Error Error2 |Error|1 10 6 4 16 4 MAD2 10 13 -3 9 3 43 20 20 0 0 04 20 27 -7 49 7 MSE5 40 34 6 36 6 22
0 110 20Model 2
Year Actual Forecast Error Error2 |Error|1 10 10 0 0 0 MAD2 10 10 0 0 0 0.63 20 19 1 1 14 20 20 0 0 0 MSE5 40 38 2 4 2 1
3 5 3
18Module: Forecasting
Forecasting Model SelectionModel 1
Year Actual Forecast Error Error2 |Error|1 10 6 4 16 4 MAD2 10 13 -3 9 3 43 20 20 0 0 04 20 27 -7 49 7 MSE5 40 34 6 36 6 22
0 110 20Model 2
Year Actual Forecast Error Error2 |Error|1 10 10 0 0 0 MAD2 10 10 0 0 0 0.63 20 19 1 1 14 20 20 0 0 0 MSE5 40 38 2 4 2 1
3 5 3
19Module: Forecasting
Forecasting Model SelectionModel 1
Year Actual Forecast Error Error2 |Error|1 10 6 4 16 4 MAD2 10 13 -3 9 3 43 20 20 0 0 04 20 27 -7 49 7 MSE5 40 34 6 36 6 22
0 110 20Model 2
Year Actual Forecast Error Error2 |Error|1 10 10 0 0 0 MAD2 10 10 0 0 0 0.63 20 19 1 1 14 20 20 0 0 0 MSE5 40 38 2 4 2 1
3 5 3
20Module: Forecasting
Forecasting Model SelectionModel 1
Year Actual Forecast Error Error2 |Error|1 10 6 4 16 4 MAD2 10 13 -3 9 3 43 20 20 0 0 04 20 27 -7 49 7 MSE5 40 34 6 36 6 22
0 110 20Model 2
Year Actual Forecast Error Error2 |Error|1 10 10 0 0 0 MAD2 10 10 0 0 0 0.63 20 19 1 1 14 20 20 0 0 0 MSE5 40 38 2 4 2 1
3 5 3
21Module: Forecasting
Measures how well forecast is predicting actual values
Is my forecast tool out of control?
Tracking Signal
23Module: Forecasting
Tracking Signal ComputationMonth Actual Forecast Error RSFE |Error| RSAE MADMonth TSMonth
1 90 1002 95 1003 115 1004 100 1005 125 1006 140 100
Error = Actual-Forecast
24Module: Forecasting
Tracking Signal Computation
RSFE = (Error)
Month Actual Forecast Error RSFE |Error| RSAE MADMonth TSMonth
1 90 100 -102 95 100 -53 115 100 154 100 100 05 125 100 256 140 100 40
25Module: Forecasting
Tracking Signal Computation
|Error| = ABS(Error)
Month Actual Forecast Error RSFE |Error| RSAE MADMonth TSMonth
1 90 100 -10 -102 95 100 -5 -153 115 100 15 04 100 100 0 05 125 100 25 256 140 100 40 65
26Module: Forecasting
Tracking Signal ComputationMonth Actual Forecast Error RSFE |Error| RSAE MADMonth TSMonth
1 90 100 -10 -10 102 95 100 -5 -15 53 115 100 15 0 154 100 100 0 0 05 125 100 25 25 256 140 100 40 65 40
RSAE = (|Error|)
27Module: Forecasting
Tracking Signal Computation
MADMonth = RSAE / Month
Month Actual Forecast Error RSFE |Error| RSAE MADMonth TSMonth
1 90 100 -10 -10 10 102 95 100 -5 -15 5 153 115 100 15 0 15 304 100 100 0 0 0 305 125 100 25 25 25 556 140 100 40 65 40 95
28Module: Forecasting
Tracking Signal Computation
TSMonth = RSFE / MADMonth
Month Actual Forecast Error RSFE |Error| RSAE MADMonth TSMonth
1 90 100 -10 -10 10 10 10.02 95 100 -5 -15 5 15 7.53 115 100 15 0 15 30 10.04 100 100 0 0 0 30 7.55 125 100 25 25 25 55 11.06 140 100 40 65 40 95 15.8
29Module: Forecasting
Tracking Signal ComputationMonth Actual Forecast Error RSFE |Error| RSAE MADMonth TSMonth
1 90 100 -10 -10 10 10 10.0 -1.02 95 100 -5 -15 5 15 7.5 -2.03 115 100 15 0 15 30 10.0 0.04 100 100 0 0 0 30 7.5 0.05 125 100 25 25 25 55 11.0 2.36 140 100 40 65 40 95 15.8 4.1
Out of control, > 3
