Case Study: Compensation on Carlson Department

Introduction

This case study intends to determine whether Carlson Department ought to be compensated for the four months that they had a stop on their operation. The shutdown had been caused by heavy damaged due to the hurricane on August 31. The data of the past three years is given which will facilitate to anticipate the sales that would have been made were it not for the hurricane. There is seasonality in the data, thus to forecast the sales of Carlson store and that of the country department, time series decomposition is applied.

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Evaluation

In the time series decomposition, some factors need to be considered; they include; the trend of the sales, the seasonal factor, and a room for error. The following equation describes the process:

time series sale=Trend+seasonal factor+errorThe data shows a graph of the past three years. From observation, the volume of sales lowers in September and rises gradually through December where it is usually at the peak; this is caused by the festive season and the beginning of school the sales are high. However, there is an unprecedented rise in sales made on September through December focusing on the country's sale department. The Country's Sale Department is directly proportional to Carlson Department. Thus an increase in the sales in Carlson department causes an increase in the sales of the Country's Department. The real trends change their gradient and intercept over a duration of time (Anderson, pg. 241).

Statistical Analysis

Based on the Carlson information, Thirty-nine percent of their annual sales occur in the last four months of the year. It implies that sixty-one percent of the sales were made in the first eight months, thus following the trend we can estimate the sales made by Carlson as:

2.56+2.28+2.69+2.48+2.73+2.37+2.31+2.23=19.65 if 19.65=61%, then the total sale would be:19.6510061=32.21

The method of linear forecasting with seasonality is applied where the ratio of actual sales to forecasting sales is calculated. From the data, the mean absolute percentage error, the mean absolute error, and the mean square error all are close to zero. Basing the argument on seasonality of the sales, the time frame is during the holidays the sales would increase. The data displays that Carlson is entitled to 12.43M when it was closed.

The department store sale of the country shows an increase in the four months, and since they follow the same trend, there is an anticipated increase in the four months for Carlson. The number of sales was above what was typically forecasted. On average of the four months, the Country had 87.5M which is an increase from 67.5M on the previous year. The sales increased by 20M which is approximately 29.6%. Since the two departments are linearly related, if Carlson were still active in the sales for the four years, the same increase would be experienced. On the previous year, the average of the four months was 2.9875M, and a rise of 0.8843M is expected, translating to an average of 3.87M for the final four months.

Conclusion

In conclusion, Carlson should be provided extra compensation for the rise in sales from insurance and disaster relief money that they would have come upon were it not for the hurricane. Carlson department would have gained an estimated increase of 3.87M in sales, so the total compensation owed to Carlson by the insurance company for the lost sales to be 16.30M. The principle of subrogation should apply, to make Carlson remain in its stable financial state (Anderson, pg. 241).

Work Cited

Anderson, David R., et al. Quantitative methods for business. Cengage Learning, 2012.

Zikmund, William G., et al. Business research methods. Cengage Learning, 2013.