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# Small questions on Forecasting

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Part B - You decide to use more sophisticated methods to forecast future sales. Prepare a linear regression and exponential smoothing forecast (with alpha = 0.2) for the data (assume that the initial forecast is equal to the average of all demand data). Calculate the mean absolute deviation (MAD) and tracking signal (TS) for each forecast.
Demand
April, 2011 22000
May, 2011 25,080
June, 2011 24,578
July, 2011 24,333
August, 2011 26,036
September, 2011 25,515
October, 2011 28,322
November, 2011 29,172
December, 2011 32,089
January, 2012 36,902
February, 2012 38,747
March, 2012 37,197

"Linear
Regression" "Exponential
Smoothing" Demand
1. What is the mean absolute deviation (MAD) through March, 2012 for each method?
2. What is the tracking signal (TS) through March, 2012 for each method? 3. 3. What is the linear regression forecast for April, 2012? . 4. What is the exponential smoothing forecast for April, 2012? 5. 5. Which method provides the best forecast (refer to part A for the moving average and weighted moving average forecast information)?
6. What is the mean absolute deviation (MAD) through March, 2012 for each method? linear Regression "Exponential
Smoothing"
a)1300 A)4000
b)1400 b)5000
c)1500 c)6000
d)1600 d)7000

2. What is the tracking signal (TS) through March, 2012 for each method?

linear Regression "Exponential
Smoothing"
a)-1 a)2.5
b)0 b)3.5
c)1 c)4.5
d)2 d)5.5

3. What is the linear regression forecast for April, 2012?

a)38000
b)38300
c)38600
d)38900

4. What is the exponential smoothing forecast for April, 2012?

a)32750
b)33,000
c)33,250
d)33,500

5. Which method provides the best forecast (refer to part A for the moving average and weighted moving average forecast information)?

Part D - You have narrowed your forecasting process down to an exponential smoothing method. You need to evaluate the simple exponential smoothing method (without trend) and compare it to the trend-adjusted exponential smoothing method to finalize your decision. Using the data shown below, prepare a forecast using each method and calculate the Cumulative Forecast Error (CFE) and Mean Absolute Deviation (MAD) to determine which method is more appropriate. Use the indicated values for alpha and beta for your forecasting process. The initial forecast (and trend where needed) are given with the data.

1. What is the Month 13 forecast for each method?
2. What is the total Cumulative Forecast Error (CFE) for each method?
3. What is the Mean Absolute Deviation (MAD) for each method?
4. Which method is better, and why?
5. If the value of alpha (?) were changed to 0.1 for both methods, which method would be better, and why?

Month Demand Forecast
1 2000 2,000
2 1940
3 1785
4 1518
5 1412
6 1271
7 1157
8 1111
9 912
10 885
11 744
12 707

Alpha (?) = 0.3
Beta (?) - 0.1
Month Demand Forecast Trend
1 2000 1800 200
2 1940
3 1785
4 1518
5 1412
6 1271
7 1157
8 1111
9 912
10 885
11 744
12 707

1. What is the Month 13 forecast for each method?

Smoothing Exponential Smoothing
a)900 a)900
b)1030 b)1030
c)1160 c)1160
d)1290 d)1290

2. What is the total Cumulative Forecast Error (CFE) for each method?

Smoothing Exponential Smoothing
a)5000 a)6200
b)4300 b)5800
c)3600 c)5400
d)2900 d)5000

3. What is the Mean Absolute Deviation (MAD) for each method?

Smoothing Exponential Smoothing
a)300 a)300
B)390 B)390
c)480 c)480
d)570 d)570

4. Which method is better, and why?

5. If the value of alpha (?) were changed to 0.1 for both methods, which method would be better, and why?

which method would be better, and why

##### Solution Summary

This posting containis 2 small forecasting questions which involve forecasting using Regression, Exponential Smoothing and Exponential smoothing with trend and calculation of MAD and tracking signal for each of the forecast.

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