Homework SourseFolowing data reps the weekly demand for wings at a restaura
Folowing data reps the weekly demand for wings at a restaurant during the past 6 weeks:
Forecast demand for week 7 using a 5PMA or five-period moving average.
Forecast demand for week 7 using a 3PWMA three-period weighted moving average. Use the these weights
W1 = 0.5
W2 = 0.3
W3 = 0.2
Forecast demand for week 7 using exponential smoothing.
value is 0.1
Just assume forecast for wk 6 was 600 units
What can you say about each of the forecasts?
| Week | 1 | 2 | 3 | 4 | 5 | 6 |
|---|---|---|---|---|---|---|
| Demand | 650 | 521 | 563 | 735 | 514 | 596 |
Solution
We have given the following data reps the weekly demand for wings at a restaurant during the past 6 weeks:
Forecast demand for week 7 using a 5PMA or five-period moving average.
This we can done in MINITAB.
The path will be :
STAT --> Time series --> Moving average -->variable demand --> MA length 5 --> center the moving averages --> Storage moving average, fits , residuals --> Results summary rable and result table --> ok
This will gives us the following output.
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Moving Average for demand
Data demand
Length 6
NMissing 0
Moving Average
Length 5
Accuracy Measures
MAPE 8.09
MAD 41.60
MSD 3411.56
Time demand MA Predict Error
1 650 * * *
2 521 * * *
3 563 596.6 * *
4 735 585.8 * *
5 514 * 596.6 -82.6
6 596 * 596.6 -0.6
week demand AVER1 FITS1 RESI1
1 650 * * *
2 521 * * *
3 563 596.6 * *
4 735 585.8 * *
5 514 * 596.6 -82.6
6 596 * 596.6 -0.6
Forecast demand for week 7 using a 3PWMA three-period weighted moving average. Use the these weights
W1 = 0.5
W2 = 0.3
W3 = 0.2
Weighte moving aversge can be calculate by using the formula,
Weighte moving average = sum ( weight for period * value in period) / sum(weights)
= (735 * 0.5) + (514 * 0.3) + (596 * 0.2) / ( 0.5 + 0.3 +0.2)
= 640.9 / 1 = 640.9
Forecast demand for week 7 using exponential smoothing. value is 0.1
This can be done by using MINITAB command will be,
STAT --> Time series -->Single exponential smoothing -->variable emand -->Weight to use in smoothing use 0.1 -->Storage moving average, fits , residuals --> Results summary rable and result table --> ok
Single Exponential Smoothing for demand
Data demand
Length 6
Smoothing Constant
Alpha 0.1
Accuracy Measures
MAPE 11.10
MAD 66.76
MSD 6579.01
Time demand Smooth Predict Error
1 650 601.850 596.500 53.500
2 521 593.765 601.850 -80.850
3 563 590.689 593.765 -30.765
4 735 605.120 590.689 144.311
5 514 596.008 605.120 -91.120
6 596 596.007 596.008 -0.008
When the value of week 6 change to 600 then output will be
Single Exponential Smoothing for demand
Data demand
Length 6
Smoothing Constant
Alpha 0.1
Accuracy Measures
MAPE 11.22
MAD 67.43
MSD 6581.15
Time demand Smooth Predict Error
1 650 602.450 597.167 52.833
2 521 594.305 602.450 -81.450
3 563 591.175 594.305 -31.305
4 735 605.557 591.175 143.826
5 514 596.401 605.557 -91.557
6 600 596.761 596.401 3.599
When deman of the week 6 is 600 then all the value of smooth will bbe change by 1unit.
