Super Bike Company (SBC) is a whole sale distributor of bicycles. Its distribution center in the town serves many retail stores within a 200-mile radius of the town. The data of bicycles ordered by the stores each month (i.e., monthly bicycle demand) during the last 5 years (year 1 year 5) is shown in Table 1 below. The analyst, Harry thought there may be a pattern of seasonal variation within the year, and decided to use the multiplicative seasonal variation method to forecast monthly demands of bicycles for next two years, year 6 and year 7. Harry started with calculating the aggregate demands, the annual demands for each of the past 5 years purpose: order to forecast the annual demands for BOTH year 6 and year 7. Several approaches were considered for this (a) Naive approach (b) 3-year moving average (c) Trend projection (d) Exponetial smoothing with trend, where Year 1's forecast = 12,000 units, Year 1's trend = 0, smoothing constant for forecast average = 0.6, and smoothing constant for trend = 0.3 (e) Associate method (i.e. simple linear regression) by relating bicycle annual demands to ad expense at the regional retail stores each year (see Tables 2.1 and 2.1) Answer the following questions, Q1 ~ Q3. 5 6 7 8 Table 1 Table 2.1 Bicycles demands Ad expense (in $000) Month 1 1244 Year 1 49.3 Month 2 878 Year 2 32.6 Month 3 1053 Year 3 41.6 Month 4 1080 Year 4 50.1 Year 1 Month 5 1541 Year 5 44.0 Month 6 Month 7 Month 8 871 1001 1265 Table 2.2 Month 9 879 Month 10 1147 Ad expense expected during the year (in $000). Month 11 988 Year 6 Month 12 1355 Year 7 45.0 48.1 Month Month 1 1200 1200 Month 2 1046 1046 Month Month S 1025 1025 Mond Month 4 871 071 Year 2 Month 1173 1175 Month 6 1269 Month 7 Month/ Month Month 9 808 1396 1550 1030 1050 Month 10 963 Month 11 899 Month 12 1034 Month 1 Month 1 1339 1559 Month 2 1164 KOTT Month 3 331 991 Mond Month 4 1009 1005 Year 3 Month 1148 1148 Month 6 1037 1057 Month 7 Month/ 986 960 Month 1455 1453 Month 9 1093 1095 Month 10 1003 1005 Month 11 823 Month 12 1406 Month 1 Month 1 1541 1541 Month 2 1058 1058 Month 3 1074 1074 Year 4 Mond Month Month 4 959 353 1526 1920 Month b Month 6 1052 1052 Month 7 Month/ 903 905 Month 1127 1127 Month 9 1057 1057 Month 10 1086 1060 Month 11 999 Month 12 1243 Month 1 Month 1 1342 1342 Month 2 1225 1225 Month 3 1015 1015 Mond Month 4 1007 1007 Year 5 Month 1562 Month 6 1109 Month/ 884 Month 1111 1111 Month 9 1081 1061 Month 10 907 Month 11 1067 Month 12 1143 Q1: Select the applicable approach(es) from (a)~(e) that are able to meet Harry's purpose. Present the forecasted annual demads of year 6 and year 7 using the approach(es) you select. Q2: For each approach you selected in Q1, calculate MAD to show its overall forecast error. Q3: Apparently, you will use the forecasts in Q1 from the approach with the least overall fortecast error found in Q2, to further calculate the monthly demand forecasts for year 6 and year 7, respectively. Show your calculation and result below.