Forecasting Lost Sales Case Study Essay

Carlson Department computer memory suffered heavy damage from a hurricane on August 31. As a result the store was unlikable for four months, September by means of celestial latitude. Carlson is in dispute with its indemnity smart set regarding the lost gross revenue for the length of time the store was closed.Section II Problem IdentificationTwo issues to address argon the bill of gross revenue Carlson discussion section store would have made if there had been no hurricane and if they are authorise to any compensation for excess sales due to change magnitude business activity after the storm. One further important gene is that eight billion dollars in federal adventure relief and insurance money came in to the county which in turn increased sales at department stores and numerous another(prenominal) businesses in the area.Section III burn downThe method to be used is forecasting with seasonality in order to find oneself approximate sales data for the months that Carlson was closed.Section IV OptionsAfter reviewing opposite methods of forecasting and their measures of forecasting accuracy the linear forecasting method is shown to be the most effective given that the mean square error, and the mean dictatorial error and the mean absolute percentage error are truly close to zero. *See attached Excel spreadsheet for further clarification/ equipment failure of forecasting methods. However, although the linear trend line can be effectual it can also prove to be inappropriate for business sell sales. Real trends change their slope and intercept over time and seldom tend to follow a fixed straight line. Therefore, linear retroflection with seasonality will be used to determine lost sales.In the late(prenominal) five years Carlsons overall monthly average for sales was 2.43375. The monthly averages for the months under consideration are as follows September 1.8975 October 2.215 November 2.775 and December 4.1875. Approximately thirty nine percent of Carlsons sales go along within the Sept through December months. The seasonal index as show in figure 6.7 further breaks this down. While reviewing Carlsondepartment stores forecasted sales for September through December and taking into write up that the time frame is during the holiday season it is apparent that sales typically increase during this period in relation to seasonality.Section V Conclusions/Recommendations construe 6.6 displays the forecast of lost sales for Carlson had there been no hurricane. This table displays that Carlson is entitled to 12.43 jillion in lost sales for the four months that it was closed. The surrounding department stores showed a consistent increase in sales during the four listed months (September through December) as shown in figure 6.9. The amount of sales were well higher up what was typically forecasted (On average the surrounding department stores did 18.67 million above forecast). The amount of sales during this time frame increased by 27.03 percent. Based glum of this data, Carlson should be provided additional compensation for the increase of sales they would have encountered from disaster relief funds and insurance money. Carlson would have gained an approximate increase of 3.36 million in sales, therefore making the total compensation owed to Carlson from their insurance telephoner 15.788 million for lost sales.Section VI Other ConsiderationsSome other factors that may require further consideration are moving holidays, or the effect of holidays on the forecasting method. Some holidays may have changing dates which can impact more than one month in a way that depends upon the date.Section VII ResourcesReferencesAnderson, D. R., Sweeny, D., Williams, T., Cann, J., Cochran, J., Fry, M., & Ohlmann, J. (2013). Quantitative Methods For Business. Mason, OH South-Western Cengage Learning.