Question

Problem Solving CASE The attached case shows sales data for the Carison Department Store. There was a storm that caused the lost sales for four months. the Looking at the data (without actually making the forecasts), explain how you proceed to answer points required in the Managerial Report. Can you make a case for excess storm-related sales? Provide a rationale. Limit you answer to 2 typewritten pages, double spaced. The case is worth 100 points. You have until Tuesday, March 12* to return it to me. You may email it or return it to me in class. Forecasting Lost Sales The Carlson Department Store suffered heavy damage when a hunicane struck on August 31, The store was closed for four months(September through December), and Carlson is now about the amount of lost sales during the 1) the amount of sales Carlson ー' involved in a dispute with its insurance company mmpts- time the store was closed. Two key issues must be r would have made if the hurricane had mot struck and(2) whiether Carlson is entitled to any More than compensation for excess sales due to increased business activity after the 8 billion in federal disaster relief and insurance money came into the county, 'resulting in increased sales at department stores and numerous other businesses Table 6.18 gives Carlson's sales data for the 48 months preceding the storm. Table 6.19 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 Depart- ment Store was closed. Carlson's managers asked you to analyze these data and develop ?receding toe str TABLE 6.18 SALES FOR CARLSON DEPARTMENT STORE(S MILLIONS) Year2 1.45 1.80 2.03 1.99 2.32 2.20 Month Year 1 Year3 2.31 1.89 2.02 2.23 2.39 2.14 2.31 1.99 2.42 2.45 2.56 2.28 2.69 2.48 2.73 2.37 2.31 February March April June 2.42 July ugust loes 23221 203 2.43 1.90 2.13 2.56 4.16 2.21 1.89 1.71 1.90 2.74 Ψ 4.20 2.50 2.09 2.54 2.97 4.35 2.83 4.04

Answer 1

Answer:

1). Sales forecast for department "C": The first step would be to create a time series plot to examine the pattern of sales to determine how best to forecast. 4.5 4 3.5 2.5 Series1 1.5 0.5 0 The time series plot does not show a definite trend over time, but it does show a seasonal ethod to forecas September, October and November of the 5th year.

Forecasted sales for the 4 months following the hurricane assuming no hurricane can be cakulated below by taking an average of the sales of those months in prior years. See the table Month September 1.711.90 1.89 2.09 October November 2.74 2.56 2.83 2.97 December Year 2 Year 3 Year 4 Year 5 Forecast 1.90 2.22 2.78 4.19 Year 1 1.90 2.13 2.29 2.54 4.20 4.16 4.044.35

2). Sales forecast for countrywide department: Similar to the data for the sales for Carlson, the sales data for all department stores appears to have a seasonal pattern but not a clear trend over the years. You can estimate Year 5 countywide sales for September, October, November and December assuming no hurricane by using the dummy variable method. See the table below. Month September 55.80 57.60 49.80 47.40 October 56.40 53.40 54.60 54.60 November 71.40 71.40 65.40 67.80 December 117.60 114.00 102.00 100.20 Year 1 Year 2 Year 3 Year 4 Year 5 Forecast 52.65 54.75 69.00 108.45

3). Forecast of lost sales for department C: Compare the actual countywide sales for the last four months of year 5 with the forecasted sales assuming no hurricane. The differences varied from 12% up to 37% which is a significant increase in sales resulting from the hurricane. See the table below. Year 5 Year 5 Differ- Differ- Month Year 1 Year 2 Year 3 Year 4 Forecast Actual ence ence September 55.80 57.60 49.80 47.40 52.65 69.00 16.35 31% October 56.40 53.40 54.60 54.60 54.75 75.00 20.25 37% November 71.40 71.40 65.40 67.80 69.00 85.20 16.20 23% December 117.60 114.00 102.00 100.20 108.45 121.80 13.35 12% Apply that same monthly percentage increase in sales due to the hurricane to the forecasted sales for Carlson to get a corrected forecast for sales lost assummg imcreased sales due to the hurricane. See the table below

Difference Due to Year 5 Corrected Month September 1.71 1.90 .89 2.09 October November 2.74 2.56 2.83 2.97 December Year 1 Year 2Year 3 Year 4 Forecast Hurricane Forecast 1.90 2.22 2.78 4.19 31% 37% 23% 12% 2.49 3.03 1.90 2.13 2.292.54 4.20 4.16 4.04 4.35 4.69