Forecasting Lost Sales Case Study Is your presentation of your solution readable?Did you answer all the questions?Did you provide support for all your answers – diagrams, tables, calculations …?Quick comment on the case study. Please make sure that you include your support on how you solve the problems in your Word document that you submit. Show your work! Show the equation that you used to do the calculations. Show one calculation fully worked out. Use Excel to create your tables for the document. Use powerpoint to create your graphics (AoAs or AoNs). Cut and paste them into your Word document. NEVER just put an answer. We also grade you on the PROCESS. No process (also known as showing your work), you will lose a lot of points on your case study. Show the process and make an algebra error … that is a minor deduction. Chapter 4 – Forecasting … Case Study #2
Case Study 2: Forecasting Lost Sales
The Carlson Department Store suffered heavy damage when a hurricane struck on August 31, 2003. The store was
closed for four months (Sept – Dec 2003) and Carlson is now involved in a dispute with its insurance
company concerning the amount of lost sales during the time the store was closed. Two key issues must
be resolved:
1. The amount of sales Carlson would have made if the hurricane had not struck; and
2. Whether Carlson is entitled to any compensation for excess sales from increased business
activity after the storm
More than $8 billion in federal disaster relief and insurance money came into the county, resulting in
increased sales at department stores and numerous other businesses.
The table below shows the sales data for the 48 months preceding the storm. The following table
reports total sales for the 48 months preceding the storm for all department stores in the county, as
well as the total sales in the county for the four months the Carlson Department Store was closed.
Management asks you to analyze this data and develop estimates of the lost sales at the Carlson
Department Store for the months of September through December 2003. Management also wants to
determine whether a case can be made for excess storm-related sales during the same period. If such a
case can be made, Carlson is entitled to compensation for excess sales it would have earned in addition
to ordinary sales.
Table 1 – Sales for Carlson Department Store, Sept ’99 through Aug ‘03
Month
January
February
March
April
May
June
July
August
September
October
November
December
1999
1.71
1.90
2.74
4.20
2000
1.45
1.80
2.03
1.99
2.32
2.20
2.13
2.43
1.90
2.13
2.56
4.16
2001
2.31
1.89
2.02
2.23
2.39
2.14
2.27
2.21
1.89
2.29
2.83
4.04
2002
2.31
1.99
2.42
2.45
2.57
2.42
2.40
2.50
2.09
2.54
2.97
4.35
Table 2 – Department Store Sales for the County, Sept ’99 through Dec ‘03
2003
2.56
2.28
2.69
2.48
2.73
2.37
2.31
2.23
Month
January
February
March
April
May
June
July
August
September
October
November
December
1999
55.8
56.4
71.4
117.6
2000
46.8
48.0
60.0
57.6
61.8
58.2
56.4
63.0
57.6
53.4
71.4
114.0
2001
46.8
48.6
59.4
58.2
60.6
55.2
51.0
58.8
49.8
54.6
65.4
102.0
2002
43.8
45.6
57.6
53.4
56.4
52.8
54.0
60.6
47.4
54.6
67.8
100.2
2003
48.0
51.6
57.6
58.2
60.0
57.0
57.6
61.8
69.0
75.0
85.2
121.8
Managerial Report is due 5 April 2019
Prepare a report for the management of the Carlson department store that summarizes your findings,
forecasts and recommendations. Include the following:
1. An estimate of the sales had there been no hurricane.
Hint: Remember to graph this time series data over the 48-month period to help you determine
the type of approach to use to determine the forecast sales for the final 4 months of 2003 for
Carlson department store. Is there a trend in the data (growth or decline)? Is there seasonality in
the data (this is a department store … Christmas time and beginning of school are normally big
sales periods)? Is there both trend and seasonality?
2. An estimate of the countywide department store sales had there been no hurricane.
Hint: 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. By computing what is known as a
“lift factor” – the ratio of actual sales to forecast sales – you have a measure of the magnitude of
excess sales, if these do indeed exist
3. Your final estimate of lost sales for the Carlson Department store for Sept – Dec 2003.
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