Lost Sales Forecast

TERM- PAPER Lost gross revenue Forecast plug-in of Contents Introduction3 Carlson subdivision instal Sales selective information for kinfolk 1992 done with(predicate) princely 19964 comprehensive subdivision hold ons Sales info for phratry 1992 through high-minded 19965 Choosing the anyow for foretelling method6 Trend and seasonal worker parcels in Forecasting7 An estimate of lost gross gross sales for the Carlson Department blood line10 Conclusion10 Introduction The Carlson Department stemma suffered heavy damage when a hurricane struck on August 31, 1996. The breed was closed for four months ( family line 1996 through celestial latitude 1996) causing our sales drop to $0.The task of this piece is to go bad sales in our segment store in past 48 months and develop estimates of the lost sales at the Carlson Department Store for the months of family line through celestial latitude 1996. The Carlson Department Store is involved in a dispute with amends co mpany concerning the make out of lost sales during the beat the store was closed. Two key issues must be resolved 1) The amount of sales Carlson would throw off made if the hurricane had non struck, and 2) Whether Carlson is entitled to any stipend for extravagance sales from increased business activity after the storm.to a greater extent than $8 billion in federal disaster relief and indemnification money came into the county, resulting in increased sales at incision stores and many some other businesses. Carlson Department Store Sales data for folk 1992 through August 1996 Certain conditions should be met by any inviolable forecast. A good forecast should usuall(a)y be based on adequate experience of the relevant past. With our company The Carlson Department Store we pose the sales data for the 48 months preceding the storm available. This amount of historical data fulfills the essential for the volume of relevant data.Table1 proves the sales data for the Carlson Department Store for the months of September 1992 through August 1996. Table 1 Sales for Carlson Department Store mil. $ Month 1992 1993 1994 1995 1996 February 1. 80 1. 89 1. 99 2. 28 knock against 2. 03 2. 02 2. 42 2. 69 April 1. 99 2. 23 2. 45 2. 48 whitethorn 2. 32 2. 39 2. 57 2. 3 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. 5 Series of verse is often difficult to interpret. Graphing the observations can be very helpful since the human body of a complicated series is more easily discerned from a picture. The data for Carlson Department Store, as can be seen in Graph 1, guide some seasonal worker fluctuations. It can be seen that the sales in furthermost quarter atomic number 18 higher than in the first 3 lodge of a year, with the highest volume of sales in December. pic Count ywide Department Stores Sales data for September 1992 through August 1996 The data for all department stores in the county are summarized in Table 2.Table 2 Department Store Sales for the county mil. $ Month 1992 1993 1994 1995 1996 February 48. 0 48. 6 45. 6 51. 6 attest 60. 0 59. 4 57. 6 57. 6 April 57. 6 58. 2 53. 4 58. 2 May 61. 8 60. 6 56. 4 60. 0 June 58. 2 55. 2 52. 8 57. 0 July 56. 4 51. 54. 0 57. 6 August 63. 0 58. 8 60. 6 61. 8 September 55. 8 57. 6 49. 8 47. 4 69. 0 October 56. 4 53. 4 54. 6 54. 6 75. 0 November 71. 4 71. 4 65. 4 67. 8 85. 2 December 117. 6 114. 0 102. 0 100. 2 121. 8 Sales of all department stores in the county, as can be seen from Graph 2, show similar seasonal fluctuations as sales of Carlson Department Store. pic From the above represent one can also observe that in past 3 years (years 1993-1995) the volume of sales in the month of September went down, and slowly went up again in October and November and usually reached its peak i n December. The extraordinary(predicate) behavior in September 1996 pulls our attention. For the first time in 4 years history we observe that the sales volume in September compared to August sales went up by 11. 7 % whereas in September 1993 they were down by 8. 6%, in September 1994 down by 15. 3%, and in September 1995 actually down by 21. 8%.The question is why such a change occurred? And the answer is that more than $8 billion in federal disaster relief and insurance money came into the county, which resulted in these increased sales at department stores. Choosing the separate forecasting method There are many different forecasting methods. One of the challenges we had to face was to choose the right technique. Smoothing methods are appropriate for a stable time series. When a time series consist of hit-or-miss fluctuations around a long-term bm line, a linear equating may be used to estimate the trend.When seasonal do are present, seasonal indexes can be computed and used to deseasonalize the data and to develop forecasts. When both(prenominal) seasonal and long-term trend effects are present, which is also the shield of Carlson Department Store as well as the case of all department stores in the county, a trend line is fitted to the deseasonalized data the seasonal indexes are then used to adjust the trend projections. Trend and seasonal worker Components in Forecasting The office of forecasting the sales for months September through December 1996 (had there been no hurricane) for The Carlson Department Store is summarized in Table 3.Table 3 Procedure of forecasting sales for Sep. -Dec. 1996 Sales 12-month Centered Seasonal Deseasonalized Moving Moving Irregular Sales Average Average tax 1992 Sept. 1. 71 - - - 2. 09 Oct. 1. 90 - - - 1. 95 Nov. 2. 74 - - - 2. 35 Dec. 4. 20 - - - 2. 41 1993 Jan. 1. 45 - - - 1. 46 Feb. 1. 80 - - - 2. 13 Mar. 2. 03 - - - 2. 09 Apr. 1. 99 - - - 2. 05 May 2. 32 - - - 2. 24 June 2. 20 - - - 2. 37 July 2. 13 - - - 2. 28 Aug. 2. 43 - - - 2. 2 Sept. 1. 90 2. 24 - - 2. 32 Oct. 2. 13 2. 26 2. 25 0. 95 2. 18 Nov. 2. 56 2. 28 2. 27 1. 13 2. 19 Dec. 4. 16 2. 26 2. 27 1. 83 2. 38 1994 Jan. 2. 31 2. 26 2. 26 1. 02 2. 32 Feb. 1. 89 2. 33 2. 29 0. 82 2. 23 Mar. 2. 02 2. 34 2. 33 0. 87 2. 08 Apr. 2. 23 2. 34 2. 34 0. 5 2. 30 May 2. 39 2. 36 2. 35 1. 02 2. 31 June 2. 14 2. 36 2. 36 0. 91 2. 30 July 2. 27 2. 36 2. 36 0. 96 2. 43 Aug. 2. 21 2. 37 2. 36 0. 94 2. 38 Sept. 1. 89 2. 35 2. 36 0. 80 2. 31 Oct. 2. 29 2. 35 2. 35 0. 97 2. 34 Nov. 2. 83 2. 36 2. 36 1. 20 2. 42 Dec. 4. 04 2. 39 2. 37 1. 70 2. 31 1995 Jan. 2. 31 2. 38 2. 38 0. 97 2. 32 Feb. 1. 99 2. 38 2. 38 0. 84 2. 35 Mar. 2. 42 2. 38 2. 38 1. 02 2. 49 Apr. 2. 45 2. 42 2. 40 1. 02 2. 52 May 2. 57 2. 44 2. 43 1. 06 2. 48 June 2. 42 2. 45 2. 44 0. 99 2. 60 July 2. 40 2. 47 2. 46 0. 7 2. 57 Aug. 2. 50 2. 49 2. 48 1. 01 2. 70 Sept. 2. 09 2. 51 2. 50 0. 84 2. 55 Oct. 2. 54 2. 53 2. 52 1. 01 2. 60 Nov. 2. 97 2. 55 2. 54 1. 17 2. 54 Dec. 4. 35 2. 56 2. 55 1. 70 2. 49 1996 Jan. 2. 56 2. 58 2. 57 1. 00 2. 57 Feb. 2. 28 2. 61 2. 59 0. 88 2. 69 Mar. 2. 9 2. 63 2. 62 1. 03 2. 77 Apr. 2. 48 2. 65 2. 64 0. 94 2. 55 May 2. 73 2. 65 2. 65 1. 03 2. 64 June 2. 37 2. 67 2. 66 0. 89 2. 55 July 2. 31 2. 66 2. 67 0. 87 2. 47 Aug. 2. 23 2. 66 2. 66 0. 84 2. 40 fall 113. 72 Columns 1 and 2 represent all the years and months.Column 3 shows the monthly sales data of Carlson Department Store. The first step of the deseasonalizing routine is to calculate the pitiful comelys. We had to decide how many observations to use in the pitiable average. One selection method is to calculate the destine error and the mean square error of the differences between the actual data and the forecast. The series with the smallest squared error would be preferred. The Management Scientist results for the Carlson Department Store show that the 12-month moving averag e gives the smallest squared error. The 12-month moving average value are shown in the Column 4.If the number of data points in a moving average calculation is an even number, we need to center the moving average values to correspond to a particular time period, as we did in the calculations in Column 5. By dividing each time series observation by the corresponding centered moving average value, we could identify the seasonal-irregular effect in the time series. Column 6 summarizes the resulting seasonal-irregular values for the entire time series. By dividing each time series observation by the corresponding seasonal index, we remove the effect of season from the time series.Deseasonalized sales data are shown in Column 7 and a graph of the data (graph 3) is on the next page. The first step of the decomposition procedure has now been completed. The upstart series has eliminated the seasonality. The next step is to calculate the trend. The observation of the deseasonalized sales da ta of Carlson Department Store appears to indicate that a straight line would be most appropriate form of equation that would describe the trend. Graph 3 Deseasonalized sales data of Carlson Department Store pic Applying regression analysis we have arrived to this the linear trend equation Tt = 2. 875 + 0. 0118t. The dispose of 0. 0118 in the trend equation indicates that over past 4 years the Carlson Department Store has experienced an average growth in sales of about $0. 0118 per year. If we accept that the past 4-year trend in sales is a good power for the future, we can use the equation above to project the trend fixings of the time series. Substituting t = 49, 50, 51, and 52 into the equation we yield the deseasonalized sales of Carlson DS for September through December 1996. For September 1996 we get $2. 67 mil, for October 1996 $2. 68, November 1996 $2. 9 and for December 1996 $2. 70. In order to apply the seasonal effects we multiply these projected deseasonalized sales b y the relevant seasonal indexes metrical in Table 4. Table 4 Seasonal Indexes Month Seasonal-Irregular Component Values Seasonal Index Jan. - 0. 63 0. 64 0. 65 0. 69 0. 65 Feb. - 0. 78 0. 80 0. 81 0. 87 0. 82 abut - 1. 12 1. 11 1. 4 1. 06 1. 11 April - 1. 00 1. 01 0. 99 1. 02 1. 01 May - 1. 04 1. 03 1. 03 1. 03 1. 03 June - 0. 99 0. 97 0. 97 0. 98 0. 98 July - 0. 96 0. 92 0. 98 0. 98 0. 96 Aug. - 1. 07 1. 09 1. 10 1. 02 1. 07 Sep. - 0. 98 0. 93 0. 88 1. 05 0. 96 Oct. - 0. 90 0. 8 0. 99 1. 03 0. 98 Nov. 1. 00 1. 02 1. 00 1. 04 1. 00 1. 01 Dec. 1. 47 1. 45 1. 41 1. 37 - 1. 43 An estimate of lost sales for the Carlson Department Store By multiplying the projected deseasonalized sales by the relevant seasonal indexes careful in Table 4 we will arrive to the levels of sales for months September 1996 through December 1996 had there been no hurricane September 1996 $2. 19 mil. October 1996$2. 62 mil November 1996 $3. 14 mil December 1996$4. 1 mil The above-described pro cedure for forecasting sales for Carlson Department Store can be utilise to countywide department stores too. It would give following results The estimated countywide department store sales had there been no hurricane (and no disaster relief money) for September 1996 is $46. 65 mil, for October 1996 $51. 22, for November 1996 $64. 4, and for December 1996 $99. 3. Comparing these figures to the actual sales of the countywide department stores one can see that the actual sales are over-valuated. I attribute this to the $8 billion of disaster relief money.Had the county department stores not veritable the disaster relief money they would probably continue their downward trend described by function Y = 63. 64 0. 13t. The slope of 0. 13 in the trend equation indicates that over past 4 years countywide department stores have experienced an average defy in sales of about $0. 13 per year. Conclusion The task of this report was to resolve two key issues. 1. Estimate the amount of sales Carlson would have made if the hurricane had not struck. We have come to a conclusion that the sales for September 1996 had there been no hurricane would be $2. 9 mil, in October 1996 it would be $2. 62 mil, in November 1996 it would be $3. 14 mil and in December 1996 it would be $4. 71 mil. 2. Find out whether Carlson is entitled to any requital 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. Based on our estimates we strongly believe that the countywide department stores would made much lower sales havent they received the relief money.Therefore we believe that our department store is entitled to compensation for excess sales from increased business activity after the hurricane and we will marvel the insurance company to cover our lost sales for months September through December 1996 in the a mount of $12. 66 mil. Reference Keat, P. G. , Young, K. Y. Managerial stintings. Economic tools for todays decision makers. 3rd edition. Anderson, D. R. , Sweeney, D. J. , Williams, T. A. Quantitative Methods for Business. 8th edition. Barr, Richard. southerly Methodist University. The Appeal of Network Models. 1997. 5 Feb 1997.