# Case Study 2.docx - Case Study 2 Forecasting Lost Sales...

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Case Study 2: Forecasting Lost Sales Texas A&M University Corpus Christ OPSY 5313-W01: Operatons Management
Executive Summary The Carlson Department Store was closed for four months due to a Hurricane. To receive payment for a claim, the insurance company for the amount of sales they would have had, the insurance company must understand the amount of sales Carlson would have made if the hurricane had not struck; and if Carlson is enttled to any compensaton for excess sales from increased business actvity after the storm. Based on my analysis, I have determined the total loss of sales from September 2003 to December 2003 exceeds 15.9 million. Introduction/Assumptions The natural disaster forced Carlson Department store to close resultng in a significant loss of sales. We must also consider the funding infused into the local economy made at the same tme, which resulted in an increase in county sales. Had there not been a hurricane Carlson Department store the following would have achieved the following sales: Month Forecast Seasonal Index Adjusted Forecast September 2.707 .797 2.707 (.80) = 2.16 October 2.718 .936 2.718 (0.) = 2.54 November 2.730 1.119 2.730 (1.119) = 3.05 December 2.741 1.678 2.741 (1.678) = 4.60 Overall Average 2.43 Forecast Carlson Department Store 2
Table 1 – Sales for Carlson Department Store, Sept ’99 through Aug ‘03 Month 1999 2000 2001 2002 2003 January 1.45 2.31 2.31 2.56 February 1.80 1.89 1.99 2.28 March 2.03 2.02 2.42 2.69 April 1.99 2.23 2.45 2.48 May 2.32 2.39 2.57 2.73 June 2.20 2.14 2.42 2.37 July 2.13 2.27 2.40 2.31 August 2.43 2.21 2.50 2.23 September 1.71 1.90 1.89 2.09 October 1.90 2.13 2.29 2.54 November 2.74 2.56 2.83 2.97 December 4.20 4.16 4.04 4.35 Graph 1 – Sales for Carlson Department Store, Sept ’99 through Aug ’03 (A seasonality with trend is evident in the sales data provided.) 0 10 20 30 40 50 60 0.00 0.50 1.00 1.50 2.00 2.50 3.00 3.50 4.00 4.50 5.00 Carlson Department Store Sales Number of Months Sales Sales forecastng between September 2003 to December 2003 involves using both calculated seasonal indicators and trend projecton principles to forecast sales despite the hurricane. To determine the seasonal index of each value, we must divide the period amount by the average of all periods. This creates a relatonship between the period amount and the average that 3
reflects how much a period is higher or lower than the average. The formula for calculatng the index is Period Amount / Average Amount . For example, the calculaton for August’s seasonal index is [ (2.43+2.21+2.5+2.23)/4 = 2.343]. After each month is averaged, the value of each month’s average will be averaged to get the annual. Average period sales and monthly sales are used to calculate seasonality index. The formula is