California Baptist University Tires For you Students Template Case 7-1 Calculate a forecast using a simple three-month moving average. 2. Calculate a forec

California Baptist University Tires For you Students Template Case 7-1 Calculate a forecast using a simple three-month moving average.
2. Calculate a forecast using a three-period weighted moving average. Use weights of 0.60, 0.30, and 0.10 for the most recent period, the second most recent period, and
the third most recent period, respectively.
3. Calculate a forecast using the exponential smoothing method. Assume the forecast
for period 1 is 9,500. Use alpha = 0.40.
4. Once you have calculated the forecasts based on the above data, determine the error
terms by comparing them to the actual sales for 2012 given below:
PERIOD
2016
January
February
March
April
May
June
July
August
September
October
November
December
TIRES USED
10,696
9,665
10,179
11,760
9,150
9,571
8,375
11,826
10,696
11,212
9,750
9,380

use the number in Case 7-1 Tires For you Students Template.

use the formula in this Case 7-1 Tires for you TN – Zoom.xlsx Q1. Simple Moving Average
Demand
Oct
9,797
Nov
11,134
Dec
10,687
Jan
9,724
Feb
10,786
Mar
9,254
Apr
10,691
May
9,256
Jun
12,700
Jul
10,192
Aug
10,751
Sep
11,724
Oct
10,193
Nov
11,599
Dec
11,130
Total Demand
159,618
Avg. Demand
10,641
Total Bias
Ave. Bias
Abs Dev
MAD
MAPE
3PMA
3PF
Error
Abs Error
MAPE
Q2. Weighted Moving Average
Demand
Oct
9,797
Nov
11,134
Dec
10,687
Jan
9,724
Feb
10,786
Mar
9,254
Apr
10,691
May
9,256
Jun
12,700
Jul
10,192
Aug
10,751
Sep
11,724
Oct
10,193
Nov
11,599
Dec
11,130
Total Demand
159,618
Avg. Demand
10,641
Total Bias
Ave. Bias
Absolute Dev
Mean Abs Dev
MAPE
3PMA
3PF
Error
Abs Error
MAPE
alpha
beta
gama
0.6
0.3
0.1
Q3. Exponential Smoothing
Demand
Jan
9,724
Feb
10,786
Mar
9,254
Apr
10,691
May
9,256
Jun
12,700
Jul
10,192
Aug
10,751
Sep
11,724
Oct
10,193
Nov
11,599
Dec
11,130
Total Demand
128,000
Avg. Demand
10,667
Total Bias
Ave. Bias
Absolute Dev
Mean Abs Dev
MAPE
Forecast
9,500
Error
Abs Error
Abs % error
alpha
beta
0.2
0.8
Q1
3 AMU
Total Bias
Ave. Bias
Abs Dev
Mean Abs Dev
MAPE
Ranking
Q2
3 WAMU
Q3
Ex Smoothing
Q1. Simple Moving Average
Demand
Oct
9,797
Nov
11,134
Dec
10,687
Jan
9,724
Feb
8,786
Mar
9,254
Apr
10,691
May
9,256
Jun
8,700
Jul
10,192
Aug
10,751
Sep
9,724
Oct
10,193
Nov
11,599
Dec
11,130
Total Demand
151,618
Avg. Demand
10,108
Total Bias
Ave. Bias
Abs Dev
MAD
MAPE
3PMA
3PF
Error
Abs Error
10,539
10,515
9,732
10,539
10,515
-815
-1,729
815
1,729
MAPE
8.4%
19.7%
-2,544
-1,272
2,544
1,272
14.03%
Q2. Weighted Moving Average
Demand
Oct
9,797
Nov
11,134
Dec
10,687
Jan
9,724
Feb
8,786
Mar
9,254
Apr
10,691
May
9,256
Jun
8,700
Jul
10,192
Aug
10,751
Sep
9,724
Oct
10,193
Nov
11,599
Dec
11,130
Total Demand
151,618
Avg. Demand
10,108
Total Bias
Ave. Bias
Absolute Dev
Mean Abs Dev
MAPE
3PMA
10,732
10,154
9,258
3PF
Error
Abs Error
MAPE
alpha
beta
gama
10,732
10,154
-1,008
-1,368
1008
1368
10%
16%
-2,376
-1,188
2,376
1,188
12.97%
0.6
0.3
0.1
Q3. Exponential Smoothing
Demand
Jan
10,696
Feb
9,665
Mar
10,179
Apr
11,760
May
9,150
Jun
9,571
Jul
8,375
Aug
11,826
Sep
10,696
Oct
11,212
Nov
9,750
Dec
9,380
Total Demand
122,260
Avg. Demand
10,188.33
Total Bias
Ave. Bias
Absolute Dev
Mean Abs Dev
MAPE
Forecast
9,500
9,566
Error
1,196
99
Abs Error
1196
99
MAPE
11.2%
1.0%
1,295
108
1,295
648
6.10%
alpha
beta
0.4
0.6
Total Bias
Ave. Bias
Abs Dev
Mean Abs Dev
MAPE
Ranking
Q1
3 AMU
-2,544
-1,272
2,544
1,272
14.03%
3
Q2
3 WAMU
-2,376
-1,188
2,376
1,188
12.97%
2
Q3
Ex Smoothing
1,295
108
1,295
648
6.10%
1
Period
2014
Oct
Nov
Dec
2015
Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
Tires Used
9,797
11,134
10,687
9,724
8,786
9,254
10,691
9,256
8,700
10,192
10,751
9,724
10,193
11,599
11,130
Chapter 7
CASE QUESTIONS
1. Calculate a forecast using a simple three-month moving average.
2. Calculate a forecast using a three-period weighted moving average. Use weights of
0.60,0.30, and 0.10 for the most recent period, the second most recent period, and
the third most recent period, respectively.
3. Calculate a forecast using the exponential smoothing method. Assume the forecast
for period 1 is 9,500. Use alpha = 0.40.
4. Once you have calculated the forecasts based on the above data, determine the error
terms by comparing them to the actual sales for 2012 given below:
TIRES USED
PERIOD
2016
10,696
January
February
March
9,665
10,179
11,760
April
May
June
9,150
9,571
8,375
11,826
July
August
September
October
10,696
11,212
9.750
November
December
9,380
5. Based on the three methods used to calculate a forecast for TFY, which method
produced the best forecast? Why? What measures of forecast error did you use? How
could you improve upon this forecast?
Source: Robert A. Novack, Ph.D. Used with permission,
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