582369 1 Solution Case Chapter 06
Chapter 6
Forecasting
Case Problem 2: Forecasting Lost Sales
1. The data used for the forecast is the Carlson sales data for the 48 months preceding the storm. Using the trend and seasonal method, the seasonal indexes and forecasts of sales assuming the hurricane had not occurred are as follows:
Month
Seasonal Index Month
Forecast ($ million)
January
0.957
September
2.16
February
0.819
October
2.54
March
0.907
November
3.06
April
0.929
December
4.60
May
1.011
June
0.937
July
0.936
August
0.974
September
0.797
October
0.936
November
1.119
December
1.677
2. The data used for this forecast is the total sales for the 48 months preceding the storm for all department sores in the county. Using the trend and seasonal method, the seasonal indexes and forecasts of county-wide department store sales assuming the hurricane had not occurred are as follows:
Month
Seasonal Index Month
Forecast ($ million)
January
0.773
September
50.55
February
0.813
October
53.20
March
0.976
November
66.78
April
0.935
December
103.11
May
0.989
June
0.924
July
0.901
August
1.017
September
0.861
October
0.907
November
1.141
December
1.763
3. By comparing the forecast of county-wide department store sales with actual sales, one can determine whether or not there are excess storm-related sales. We have computed a "lift factor" as the ratio of actual sales to forecast sales as a measure of the magnitude of excess sales.
Forecast Sales ($ million)
Actual Sales ($ million)
Lift Factor
50.55
69.0
1.365
53.20
75.0
1.410
66.78
85.2
1.276
103.11
121.8
1.181 273.64
351.0
1.283
From the analysis a strong case can be made for excess storm related sales. For each month, actual sales exceed the forecast of what sales would have been without the hurricane. For the 4-month total, actual sales exceeded the forecast by 28.3%.
The explanation for the increase is that people had to replace real and personal property damaged by the storm. In addition,
Forecasting
Case Problem 2: Forecasting Lost Sales
1. The data used for the forecast is the Carlson sales data for the 48 months preceding the storm. Using the trend and seasonal method, the seasonal indexes and forecasts of sales assuming the hurricane had not occurred are as follows:
Month
Seasonal Index Month
Forecast ($ million)
January
0.957
September
2.16
February
0.819
October
2.54
March
0.907
November
3.06
April
0.929
December
4.60
May
1.011
June
0.937
July
0.936
August
0.974
September
0.797
October
0.936
November
1.119
December
1.677
2. The data used for this forecast is the total sales for the 48 months preceding the storm for all department sores in the county. Using the trend and seasonal method, the seasonal indexes and forecasts of county-wide department store sales assuming the hurricane had not occurred are as follows:
Month
Seasonal Index Month
Forecast ($ million)
January
0.773
September
50.55
February
0.813
October
53.20
March
0.976
November
66.78
April
0.935
December
103.11
May
0.989
June
0.924
July
0.901
August
1.017
September
0.861
October
0.907
November
1.141
December
1.763
3. By comparing the forecast of county-wide department store sales with actual sales, one can determine whether or not there are excess storm-related sales. We have computed a "lift factor" as the ratio of actual sales to forecast sales as a measure of the magnitude of excess sales.
Forecast Sales ($ million)
Actual Sales ($ million)
Lift Factor
50.55
69.0
1.365
53.20
75.0
1.410
66.78
85.2
1.276
103.11
121.8
1.181 273.64
351.0
1.283
From the analysis a strong case can be made for excess storm related sales. For each month, actual sales exceed the forecast of what sales would have been without the hurricane. For the 4-month total, actual sales exceeded the forecast by 28.3%.
The explanation for the increase is that people had to replace real and personal property damaged by the storm. In addition,