2002-02 pring 2002 Forecasting for the Future: Developing a Forecasting Model for Brinker International Latonya Morris, James On Forecasting for the Future: Developing a Forecasting Model for Brinker International Developed by: Latonya Morris and James Orr EMI 4395 May 7, 2002 io
Management ummary Brinker International is one of the leading trendsetters in the restaurant industry. The company started in 1975 with just the Chili's restaurants. Under the direction of Norman Brinker the company was renamed to Brinker International Inc. The Brinker International family now includes Chili's, Corner Bakery, Cozymel's, Maggiano's, Big Bowl, Eat Zi's, Rockfish, Macaroni Grill, and On The Border. The problem consisted of forecasting sales for Brinker International, more specifically The Corner Bakery. To obtain a more accurate forecast a time series decomposition model was used. The multiplicative model consisted of four parts: trend, seasonal, cyclical, and irregular component. All of these components were solved for using historical data, except for the irregular component, which was not computed. The model is designed using excel in a manner that allows employees to enter the data as it develops and update the forecast automatically. Using the trend, seasonal, and cyclical lines we were able to obtain a very accurate estimate of what the sales are going to be for the year 2003. The forecast was slightly limited due to a lack of data. As more data becomes available the model will become more accurate. The sales forecast for 2003 currently stands at $9,926,506.10. This forecast does not take into consideration the increase in revenue due to new stores opening.
Background and Description of the Problem ituation Brinker International, founded by Norman Brinker, is a company that started in 1975 with the one original Chili's location. Chili's is still apart of Brinker International family, but seven other restaurants join it. The Brinker International family now includes Chili's, Corner Bakery, Cozymel's, Maggiano's, Big Bowl, Eat Zi's, Rockfish, Macaroni Grill, and On The Border. Due to time constraints we were only able to focus on the problems of the Corner Bakery. The problem that Corner Bakery faces is that there is not an accurate model to forecast sales. In order to overcome this problem time series decomposition model was used. The multiplicative model consisted of four parts: trend, seasonal, cyclical, and irregular component. All of these components were solved for using historical data, except for the irregular component, which was not computed lack of data. Our goal is to use excel to produce an updateable model to forecast sales for the following years. The forecasting model is tested against the actual sales for each year to test the accuracy of the model. The forecast will allow Corner Bakery to have a very good estimate of the sales that they can expect for each of the months for 2003.
LI Analysis of the ituation The problem consisted of two parts: the developing an accurate forecasting of sales data for Brinker's 2003 fiscal year, and second, finding a way to put our forecasting model in a Microsoft Excel spreadsheet. The forecast was requested to be incorporated into a Microsoft Excel spreadsheet for the ease of our client's use. To forecast sales many different forecasting models were tried: first, exponential smoothing and moving average models were used to forecast sales. These methods were found to be unreliable and not very accurate. Finally, a multiplicative time series decomposition model was used to forecast sales. This model was found to be the most accurate and the most beneficial to our client's situation. The model that was used for the forecast was Y 1 = x i x C 1 x I. The multiplicative time series decomposition model consists of four parts: a trend component, seasonal component, cyclical component, and an irregular component. The forecast for month one would be Y 1 = T 1 x 1 x C 1 x Ii.
Technical Description of the Model The model that was used to forecast the following year's sale is a multiplicative time series decomposition model. The model takes the form Y 1 = Ti x C 1 x i x I. Ti is the trend component. The C 1 is the cyclical adjustment index. i represents seasonal adjustment index and I i is the irregular, random index. To compute for the T i first the seasonal index must be found. To find the seasonal index a twelve-month moving average was taken over the available sales data. The first average was taken from the first to the twelfth month and the second average was taken from the second to the thirteenth month. This process is repeated until all the months of sales data have been incorporated. From these twelve-month moving averages the centered moving averages can be computed. The first centered moving average is the average of the first and second twelve-month averages. This first centered moving average corresponds to the seventh period of sales data. To obtain the i for that month divide the sales data by centered moving average for that corresponding month. To get a more accurate seasonal index, the seasonal indexes are grouped by month. Once all the seasonal indexes from the same month are grouped together, an average of the indexes for that month are taken. The average for each individual month is summed with the other months and if the sum does not equal twelve, the indexes must be adjusted. The adjustment is made by taking the average seasonal index for each month, multiplying it by twelve and then dividing that number by the sum of the original indexes (i.e. [12 * j]/ um of original indexes).
Each individual Ti is just the trend data values that move through the deseasonalized sales data. The trend line was found by using the number of months of sales data and the deseasonalized sales data for that month. The deseasonalized sales data is found, by multiplying the actual sales data by the adjusted seasonal index. The cyclical component for each month was found by taking the centered moving average for that month divided by the trend forecast for that month. Then the cyclical components were adjusted in the same manner the seasonal indexes were adjusted. The irregular random index could not be computed and therefore was not integrated into our forecast. The data for the forecast was obtained from Brinker International. Brinker had the last three years of sales data available to us to use in putting together our forecasting model. In order to test the accuracy of the forecast, the model was compared to the sales data from Brinker's 2000, 2001, and 2002 fiscal years. The only simplifying assumption that was made is that the spreadsheet will only be used to forecast the next two fiscal years. But if the spreadsheet is needed past this point, it can be easily adjusted to incorporate the new sales data and forecast the next fiscal year. The computations for this model were all done using Microsoft Excel. The excel sheet was linked and set up in such a way that the user can produce a new accurate forecast with minimal effort and minimal keystrokes. Even though Microsoft Excel does not allow for written programs to be executed while it is running, it was chosen as our software package because of its wide distribution, our client's familiarity with the product, and its ease of use. Using Microsoft Excel may have made the designing and implementation of the model more difficult, but it made it easier for our client to use.
Analysis and Managerial Interpretation The forecast for the 2003 fiscal year is predicted to be $9,926,506.10. This does not take into consideration an increase in revenue due to the openings of new stores. When the forecasted sales data was compared to the actual sales values for the 2000, 2001, and part of the 2002 fiscal years the forecasted values were an average of 3.63% different from the actual values with a standard deviation of 4. 66%. ince the difference between the actual and forecasted sales data was relatively small, the model is very accurate and should be used as a reliable predictor for upcoming years sales data. The assumptions made in the project were that historical sales values were a good basis for forecasting future sales. This assumption is the foundation that the model is built on and if these values end up being flawed due to some unforeseen circumstances or problems. Another assumption made is that these sales trends will continue into the future. If the sales trends change in the future, this model will not be an acceptable model to use. 0
Conclusions and Critique The time-series forecasting model was successfully implemented in a Microsoft Excel spreadsheet. The spreadsheet is updateable and easy for the user to read future forecasted numbers. The recommendation for management would be to implement this model and to use the forecasted sales numbers as a guide when purchasing raw materials and shipping product to different locations. The limitations on this model include: not computing an irregular component and the lack of sales data. We did not find a way to compute for the irregular component, which limits the accuracy of our model. Also, the lack of original sales data causes some of the computed indexes to be slightly inaccurate. The fact that this model is updateable will slowly nullify this later limitation.
0 0 Appendix A
Instructions for Forecasting Model 1. Enter the Total ales for the most current month in the Total ales column on the ales Data heet. 2. Click on the easonal Indexes tab and update the Number of Indexes by counting the number of non-zeros in each row and putting that number in the Number of Indexes comlumn. 3. Next, undate the Trend Analysis worksheet by clicking on the Trend Analysis tab. Add the next number to the Month Number column by replacing the first zero in the column with the number that follows directly after the previous Month Number. (i.e. 32 F02 Feb 776265.3 0.90037 698926.3 33 F02 Mar 847943.4 0.916485 777127.5 0 F02 Apr 0 1.174454 0 0 F02 May 0 0.923086 0 0 F02 Jun 0 0.951417 0 1 will become 32 F02 Feb 776265.3 0.90037 698926.3 33 F02 Mar 847943.4 0.916485 777127.5 34 F02 Apr 0 1.174454 0 0 F02 May 0 0.923086 0 0 F02 Jun 0 0.951417 0 Also be sure to change the Number of months, in cell J5, to reflect the new data. (The last Month Number and Number of Months should be the same.) 4. Next, click on the Cyclical Indexes tab and update the Number of Indexes column by counting the number of non-zeros in each row and putting that number in the column. 5. Finally, click on the Final Forecast tab and update the Promotion Index if there is a particular promotion or event that will effect sales. (increasing the number in the Promotion Index will increase the forecast for that month) Also, if there are any new stores opening in that particular month put the total expected sales from the opening of those new stores in the Growth Numbers column. 6. Enjoy your forecast! The final forecasted numbers are produced in the Final Forecast column and a graph comparing actual sales and forecasted sales is available by clicking on the Graphs tab.
ales Data 12 Month Centered easonal - Irregular Total ales Moving Avg. Moving Avg. Component 1 FOOJuI $808,387 2FOOAug $692,759 3F00 ep $641,107 4F00 Oct $810,079 5 F00 Nov $674,904 6F00 Dec $713,570 7 F00 Jan $807,762 739052.182 1.092970604 8 F00 Feb $672,074 745993.331 0.90091 2035 9 F00 Mar $685,602 752311.324 0.911327848 10 F00 Apr $883,188 756964.012 1.16675025 11 F00 May $699,738 760586.29 0.919997927 12 F00 Jun $737,367 $735,545 762965.665 0.966448876 13 FOl Jul $892,564 $742,560 769840.639 1.159414475 14 FOl Aug $775,169 $749,427 777066.64 0.997557429 15 FOl ep $710,329 $755,196 778909.382 0.911952837 16 FOl Oct $852,522 $758,732 780943.756 1.09165594 17 FOl Nov $719,396 $762,440 782469.645 0.919391627 18 FOl Dec $726,183 $763491 782178.205 0.92841104 19 FOl Jan $960,149 $776,190 779796.69 1.231280939 20 FOl Feb $693,112 $777,943 776411.853 0.892711668 21 FOl Mar $708,791 $779,876 775144.498 0.914397899 22 FOl Apr $908,825 $782,012 774869.385 1.17287469 23 FOl May $710,722 $782,927 773468.217 0.91887733 24 FOl Jun $719,388 $781,429 774481.57 0.928864117 25 F02 Jul $853,387 $778,164 778624.342 1.09601 91 51 26 F02 Aug $733,110 $774,659 784577.525 0.934400587 27 F02 ep $721,971 $775,630 793840.283 0.909466331 28 F02 Oct $834,277 $774,109 761770.624 1.095181114 29 F02 Nov $704,013 $772,827 694289.495 1.014005102 30 F02 Dec $765,886 $776,136 634701.555 1.206687307 31 F02 Jan $1,019,872 $781,113 569169.25 1.791860472 32 F02 Feb $776,265 $788,042 503065.213 1.54307082 33 F02 Mar $847,943 $799,638 442436.85 1.916529694 34 F02 Apr $0 $723,903 377593.191 0 35 F02 May $0 $664,676 313497.779 0 36 F02 Jun $0 $604,727 252251.97 0 37 F03 Jul $0 $533,611 177845.379 0 38 F03 Aug $0 $472,519 103006.332 0 39 F03 ep $0 $412,355 35330.9733 0 40 F03 Oct $0 $342,832 0 #DIV/01 41 F03 Nov $0 $284,164 0 #DIV/0I 42 F03 Dec $0 $220,340 0 #DIV/01 43 F03 Jan $0 $135,351 0 #DIV/0I 44 F03 Feb $0 $70,662 0 #DIV/Ot 45 F03 Mar $0 $0 0 #Dl Viol 46 F03 Apr $0 $0 0 #DIV/0I 47 F03 May $0 $0 0 #DIV/01 48 F03 Jun $0 $0 0 #DIV/0I 49 F04 Jul $0 $0 0 #DIV/01 50 F04 Aug $0 $0 0 #DIV/01 51 F04 ep $0 $0 0 #DIV/0I 52 F04 Oct $0 $0 0 #DIV/01 53 F04 Nov $0 $0 0 #DIV/01 54 F04 Dec $0 $0 0 #DIV/01 55FO4Jan $0 $0 56 F04 Feb $0 $0 57 F04 Mar $0 $o 58 F04 Apr $0 $0 59 F04 May $0 $o 6OFO4Jun $0 $0
easonal Indexes. Third Fourth Avgerage First Year's econd Year's Year's Number of easonal Month Index Year's Index Index Index Indexes Index Jan 1.0929706 1.23128094 0 0 2 1.162126 Feb 0.900912 0.89271167-0 0 2 0.896812 Mar 0.9113278 0.9143979 0 0 2 0.912863 Apr 1.1667503 1.17287469 0 0 2 1.169812 May 0.9199979 0.91887733 0 0 2 0.919438 Jun 0.9664489 0.92886412 0 0 2 0.947656 Jul 1.1594145 1.09601915 0 0 2 1.127717 Aug 0.9975574 0.93440059 0 0 2 0.965979 ep 0.9119528 0.90946633 0 0 2 0.91071 Oct 1.0916559 0 0 0 1 1.091656 Nov 0.91 93916 0 0 0 1 0.91 9392 Dec 0.928411 0 0 0 1 0.928411 Total 11.95257 Adjusted Month Indexes Jan 1.1667372 Feb 0.9003705 Mar 0.91 64852 Apr 1.1744544 May 0.923086 Jun 0.9514169 Jul 1.1321917 Aug 0.9698121 ep 0.91 43234 Oct 1.0959877 Nov 0.9230399 Dec 0.9320951 Total 12
El Trend Analysis Fiscal Adjusted Deseason- Month Year Total easonal alized Number Month ales Index Values 1 FOO Jul 808386.7 1.132192 915248.7 Number of months = 33 2 F00 Aug 692758.7 0.969812 671845.8 um of x's= 561 3 F00 ep 641106.7 0.914323 586178.8 um of Vs= 25679969 4 F00 Oct 810079.4 1.095988 887837 um of xvs= 443120369 5 F00 Nov 674903.9 0.92304 622963.2 um of x2's= 12529 6FOODec 713570 0.932095 665115.1 Mean ofx= 17 7 FOO Jan 807762.3 1.166737 942446.3 Mean of y= 778180.88 8 F00 Feb 672074.4 0.90037 605115.9 9 F00 Mar 685602.3 0.916485 628344.3 lope = 21 92.81 26 10 F00 Apr 883188 1.174454 1037264 Interecept = 740903.07 11 F00 May 699737.8 0.923086 645918.2 12 FOO Jun 737367.3 0.951417 701543.7 13 FOl Jul 892564.4 1.132192 1010554 14 FOl Aug 775168.6 0.969812 751767.9 15 FOl ep 710328.6 0.914323 649470.1 16 FOl Oct 852521.9 1.095988 934353.5 17 FOl Nov 719396 0.92304 664031.2 18 FOl Dec 726182.9 0.932095 676871.5 19 FOl Jan 960148.8 1.166737 1120241 20 FOl Feb 693111.9 0.90037 624057.5 21 FOl Mar 708790.5 0.916485 649596 22 FOl Apr 908824.7 1.174454 1067373 23 FOl May 710722.4 0.923086 656057.9 24 FOl Jun 719388.1 0.951417 684438 25 F02 Jul 853387.2 1.132192 966197.9 26 F02 Aug 733109.7 0.969812 710978.7 27FO2ep 721971 0.914323 660115 28 F02 Oct 834276.8 1.095988 914357.1 29 F02 Nov 704013.1 0.92304 649832.1 30 F02 Dec 765886.3 0.932095 713878.8 31 F02 Jan 1019872 1.166737 1189922 32 F02 Feb 776265.3 0.90037 698926.3 33 F02 Mar 847943.4 0.916485 777127.5 0 F02 Apr 0 1.174454 0 0 F02 May 0 0.923086 0 0 F02 Jun 0 0.951417 0 0 F03 Jul 0 1.132192 0 0 F03 Aug 0 0.969812 0 0 F03 ep 0 0.914323 0 0 F03 Oct 0 1.095988 0 0 F03 Nov 0 0.92304 0 0 F03 Dec 0 0.932095 0 0 F03 Jan 0 1.166737 0 0 F03 Feb 0 0.90037 0
o F03 Mar 0 0.916485 0 0 F03 Apr 0 1.174454 0 0 F03 May 0 0.923086 0 0 F03 Jun 0 0.951417 0 0 F04 Jul 0 1.132192 0 0 F04 Aug 0 0.969812 0 0 F04 ep 0 0.914323 0 0 F04 Oct 0 1.095988 0 0 F04 Nov 0 0.92304 0 0 F04 Dec 0 0.932095 0 0 F04 Jan 0 1.166737 0 0 F04 Feb 0 0.90037 0 0 F04 Mar 0 0.916485 0 0 F04 Apr 0 1.174454 0 0 F04 May 0 0.923086 0 0 F04 Jun 0 0.951417 0
Cyclical Indexes. First econd Third Fourth Avgerage Year's Year's Year's Year's Number of Cyclical Month Index Index Index Index Indexes Index Jan 0.977256 0.996461 0 0 2 0.986858 Feb 0.983582 0.989363 0 0 2 0.986472 Mar 0.989053 0.984996 0 0 2 0.987024 Apr 0.992309 0.981 91 0 0 2 0.987109 May 0.994199 0.977419 0 0 2 0.985809 Jun 0.994459 0.975995 0 0 2 0.985227 Jul 1.00056 0.978511 0 0 2 0.989536 Aug 1.007082 0.983283 0 0 2 0.995182 ep 1.006609 0.992165 0 0 2 0.999387 Oct 1.006386 0 0 0 1 1.006386 Nov 1.005511 0 0 0 1 1.005511 Dec 1.002312 0 0 0 1 1.002312 Total 11.91681 Adjusted Month Indexes Jan 0.993747 Feb 0.993359 Mar 0.993914 Apr 0.994 May 0.99269 Jun 0.992104 Jul 0.996443 Aug 1.002129 ep 1.006363 Oct 1.013411 Nov 1.01253 Dec 1.009309 Total 12 0
Final Forecast I I. Adjusted Adjusted Rolled Month Fiscal Year Trend easonal Cyclical Promotion Growth Growth Number Month Total ales Forecast Index Indexes Index Numbers Numbers Final Forecast 1 FOO Jul 808386.67 743095.9 1.132192 0.996443 1 $0.00 $0.00 $838,334.54 2 F00 Aug 692758.74 745288.7 0.969812 1.002129 1 $0.00 $0.00 $724,328.97 3 F00 ep 641106.65 747481.5 0.914323 1.006363 1 $0.00 $0.00 $687,788.77 4 F00 Oct 810079.35 749674.3 1.095988 1.013411 1 $0.00 $0.00 $832,653.07 5 F00 Nov 674903.9 751867.1 0.92304 1.01253 1 $0.00 $0.00 $702,699.34 6 F00 Dec 713570.01 754059.9 0.932095 1.009309 1 $0.00 $0.00 $709,398.42 7 FOO Jan 807762.31 756252.8 1.166737 0.993747 1 $0.00 $0.00 $876,830.72 8 F00 Feb 672074.37 758445.6 0.90037 0.993359 1 $0.00 $0.00 $678,346.65 9 F00 Mar 685602.26 760638.4 0.916485 0.993914 1 $0.00 $0.00 $692,871.21 10 F00 Apr 883187.95 762831.2 1.174454 0.994 1 $0.00 $0.00 $890,534.87 11 F00 May 699737.81 765024 0.923086 0.99269 1 $0.00 $0.00 $701,021.01 12 FOO Jun 737367.31 767216.8 0.951417 0.992104 1 $0.00 $0.00 $724,179.52 13 FOl Jul 892564.38 769409.6 1.132192 0.996443 1 $0.00 $0.00 $868,020.78 14 FOl Aug 775168.6 771602.4 0.969812 1.002129 1 $0.00 $0.00 $749,902.70 15 FOl ep 710328.62 773795.3 0.914323 1.006363 1 $0.00 $0.00 $712,001.15 16 FOl Oct 852521.89 775988.1 1.095988 1.013411 1 $0.00 $0.00 $861,879.40 17 FOl Nov 719396.04 778180.9 0.92304 1.01253 1 $0.00 $0.00 $727,292.32 18 FOl Dec 726182.88 780373.7 0.932095 1.009309 1 $0.00 $0.00 $734,153.66 19 FOl Jan 960148.8 782566.5 1.166737 0.993747 1 $0.00 $0.00 $907,339.97 20 FOl Feb 693111.92 784759.3 0.90037 0.993359 1 $0.00 $0.00 $701,881.42 21 FOl Mar 708790.5 786952.1 0.916485 0.993914 1 $0.00 $0.00 $716,840.60 22 FOl Apr 908824.69 789144.9 1.174454 0.994 1 $0.00 $0.00 $921,253.74 23 FOl May 710722.41 791337.8 0.923086 0.99269 1 $0.00 $0.00 $725,133.32 24 FOl Jun 719388.14 793530.6 0.951417 0.992104 1 $0.00 $0.00 $749,017.19 25 F02 Jul 853387.19 795723.4 1.132192 0.996443 1 $0.00 $0.00 $897,707.03 26 F02 Aug 733109.7 797916.2 0.969812 1.002129 1 $0.00 $0.00 $775,476.43 27 F02 ep 721971.01 800109 0.914323 1.006363 1 $0.00 $0.00 $736,213.52 28 F02 Oct 834276.8 802301.8 1.095988 1.013411 1 $0.00 $0.00 $891,105.72 29 F02 Nov 704013.09 804494.6 0.92304 1.01253 1 $0.00 $0.00 $751,885.30 30 F02 Dec 765886.31 806687.4 0.932095 1.009309 1 $0.00 $0.00 $758,908.89 31 F02 Jan 1019871.88 808880.3 1.166737 0.993747 1 $0.00 $0.00 $937,849.22 32 F02 Feb 776265.25 811073.1 0.90037 0.993359 1 $0.00 $0.00 $725,416.19 33 F02 Mar 847943.36 813265.9 0.916485 0.993914 1 $0.00 $0.00 $740,809.99 34 F02 Apr 0 815458.7 1.174454 0.994 1 $0.00 $0.00 $951,972.61 35 F02 May 0 817651.5 0.923086 0.99269 1 $0.00 $0.00 $749,245.62 36 F02 Jun 0 819844.3 0.951417 0.992104 1 $0.00 $0.00 $773,854.87 37 F03 Jul 0 822037.1 1.132192 0.996443 1 $0.00 $0.00 $927,393.27 38 F03 Aug 0 824229.9 0.969812 1.002129 1 $0.00 $0.00 $801,050.16 39 F03 ep 0 826422.8 0.914323 1.006363 1 $18,500.00 $18,500.00 $778,925.90 40 F03 Oct 0 828615.6 1.095988 1.013411 1 $0.00 $18,500.00 $938,832.05 41 F03 Nov 0 830808.4 0.92304 1.01253 1 $0.00 $18,500.00 $794,978.29 42 F03 Dec 0 833001.2 0.932095 1.009309 1 $23,500.00 $42,000.00 $825,664.13 43 F03 Jan 0 835194 1.166737 0.993747 1 $0.00 $42,000.00 $1,010,358.47 44 F03 Feb 0 837386.8 0.90037 0.993359 1 $6,000.00 $48,000.00 $796,950.97 45 F03 Mar 0 839579.6 0.916485 0.993914 1 $0.00 $48,000.00 $812,779.39 46 F03 Apr 0 841772.4 1.174454 0.994 1 $0.00 $48,000.00 $1,030,691.48 47 F03 May 0 843965.3 0.923086 0.99269 1 $0.00 $48,000.00 $821,357.92 48 F03 Jun 0 846158.1 0.951417 0.992104 1 $17,500.00 $65,500.00 $864,192.54 49 F04 Jul 0 848350.9 1.132192 0.996443 1 $0.00 $65,500.00 $1,022,579.52 50 F04 Aug 0 850543.7 0.969812 1.002129 1 $0.00 $65,500.00 $892,123.89
51 F04 ep 0 852736.5 0.914323 1.006363 1 $0.00 $65,500.00 $850,138.27 52 F04 Oct 0 854929.3 1.095988 1.013411 1 $0.00 $65,500.00 $1,015,058.37 53 F04 Nov 0 857122.1 0.92304 1.01253 1 $0.00 $65,500.00 $866,571.27 54 F04 Dec 0 859314.9 0.932095 1.009309 1 $0.00 $65,500.00 $873,919.37 55 F04 Jan 0 861507.8 1.166737 0.993747 1 $0.00 $65,500.00 $1,064,367.72 56 F04 Feb 0 863700.6 0.90037 0.993359 1 $0.00 $65,500.00 $837,985.74 57 F04 Mar 0 865893.4 0.916485 0.993914 1 $0.00 $65,500.00 $854,248.78 58 F04 Apr 0 868086.2 1.174454 0.994 1 $0.00 $65,500.00 $1,078,910.35 59 F04 May 0 870279 0.923086 0.99269 1 $0.00 $65,500.00 $862,970.23 60 F04 Jun 0 872471.8 0.951417 0.992104 1 $0.00 $65,500.00 $889,030.21 0
Forecast vs. Total ales 1200000 1100000 1000000 900000 s--total ales -- Final Forecast 800000 700000 600000 1 I I I I I I I I I I I I I I I I I I I I I I I!) r- o - to N- C) - CO to N- C) i- CO LI) N- C) i- CO LO N 1 i i i CJ C4 CJ C\J C.J C") C") C") C") C") '- ' "t LI) LI) LI) L Month
I Project Name: Optimizing Distribution Project Team Members: Latonya Morris and James Orr Problem/Opportunity: Currently there is not a model designed for forecasting. We will select the most accurate model by using different comparisons for each type of model. The model needs to be able to be understood by all employees. Goals: Accurate forecast for fiscal 2003 Models needs to be able to be updated Accomplish sales forecasting in four different locations for accuracy If time permits do forecasting on the product mix Objectives: Gather appropriate data Gather information of all the software Determine who the stakeholders are Get the stakeholders buy in Decide on the format of the forecasting uccess and Completion Criteria: Have a model that can be used by anyone to forecast sales and income statements. Assumption: All financial data that includes market trends.