Series" spreadsheet tab. en moit Juods You are a manufacturer of outdoor picnic furniture (benches, picnic tables, litter receptacles, etc.). You are planning your bench product line's demand for purchasing and production scheduling. 1. Optimize your forecast and provide the alpha, beta and forecast error. 2. Provide the forecast for month's 25, 26, 27 3. How is this forecast compared to a 3 month and 6 month moving average? Which model is preferrable to use? 4. If a Pandemic hits the world late in month 24, how would you forecast months 25, 26 and 27? Think about this from the perspective of this specific company. UDY onemeb g'aril faubong nona muoy polonat UDY niluberida noiloubong brin Smoothing Constants alpha= 20% beta = 20% Period (t) Actual (X) L T 1 992 942.4 2 820 921.2 3 764 889.0 4 905 886.4 5 742 852.5 6 1098 892.1 7 1178 948.1 8 951 956.6 9 1062 985.5 980 995.2 699 946.1 1154 988.0 1100 1017.4 991 1022.4 815 989.9 1029 999.7 939 990.8 1144 1022.5 984 1020.8 1488 1118.7 1483 1210.9 584 1115.6 960 1093.4 1345 1147.3 10 11 12 13 14 15 16 17 18 19 222222 20 21 23 24 4.08 -1.0 -7.2 -6.3 -11.8 -1.5 10.0 9.7 13.5 12.8 0.4 8.7 12.8 11.3 2.5 4.0 1.4 7.5 5.6 24.1 37.7 11.1 4.4 14.3 LO= 930 Error Fcst Initial Conditions TO= 2 ABS % Error Squared Error 126 15.4% 15997 156 20.4% 24400 23 2.6% 541 138 18.6% 19069 257 23.4% 66227 287 24.4% 82601 7 0.7% 50 96 9.0% 9154 19 1.9% 360 309 44.2% 95441 207 18.0% 43037 103 9.4% 10664 39 4.0% 1538 219 26.8% 47799 37 3.6% 1338 65 6.9% 4187 152 13.3% 23056 46 4.7% 2115 462 31.0% 213063 340 22.9% 115725 665 113.8% 441621 167 17.4% 27805 247 18.4% 61088 946 126.5 920 156.2 882 -23.3 880 138.1 841 -257.3 891 -287.4 958 7.0 966 -95.7 999 19.0 1008 308.9 947 -207.5 997 -103.3 1030 39.2 1034 218.6 992 -36.6 1004 64.7 992 -151.8 1030 46.0 1026 -461.6 1143 -340.2 1249 664.5 1127 166.7 1098 -247.2 ABS Error Error measures only based on last 12 months that were forecasted 1600 SQRT Expo Smoothed Average with Trend Forecast Mean 1400 Squared Error 1200 SMSE or RMSE 1000 281 Jap wody 800 MAE 212 600 MAPE 400 200 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 22.7% Series" spreadsheet tab. en moit Juods You are a manufacturer of outdoor picnic furniture (benches, picnic tables, litter receptacles, etc.). You are planning your bench product line's demand for purchasing and production scheduling. 1. Optimize your forecast and provide the alpha, beta and forecast error. 2. Provide the forecast for month's 25, 26, 27 3. How is this forecast compared to a 3 month and 6 month moving average? Which model is preferrable to use? 4. If a Pandemic hits the world late in month 24, how would you forecast months 25, 26 and 27? Think about this from the perspective of this specific company. UDY onemeb g'aril faubong nona muoy polonat UDY niluberida noiloubong brin Smoothing Constants alpha= 20% beta = 20% Period (t) Actual (X) L T 1 992 942.4 2 820 921.2 3 764 889.0 4 905 886.4 5 742 852.5 6 1098 892.1 7 1178 948.1 8 951 956.6 9 1062 985.5 980 995.2 699 946.1 1154 988.0 1100 1017.4 991 1022.4 815 989.9 1029 999.7 939 990.8 1144 1022.5 984 1020.8 1488 1118.7 1483 1210.9 584 1115.6 960 1093.4 1345 1147.3 10 11 12 13 14 15 16 17 18 19 222222 20 21 23 24 4.08 -1.0 -7.2 -6.3 -11.8 -1.5 10.0 9.7 13.5 12.8 0.4 8.7 12.8 11.3 2.5 4.0 1.4 7.5 5.6 24.1 37.7 11.1 4.4 14.3 LO= 930 Error Fcst Initial Conditions TO= 2 ABS % Error Squared Error 126 15.4% 15997 156 20.4% 24400 23 2.6% 541 138 18.6% 19069 257 23.4% 66227 287 24.4% 82601 7 0.7% 50 96 9.0% 9154 19 1.9% 360 309 44.2% 95441 207 18.0% 43037 103 9.4% 10664 39 4.0% 1538 219 26.8% 47799 37 3.6% 1338 65 6.9% 4187 152 13.3% 23056 46 4.7% 2115 462 31.0% 213063 340 22.9% 115725 665 113.8% 441621 167 17.4% 27805 247 18.4% 61088 946 126.5 920 156.2 882 -23.3 880 138.1 841 -257.3 891 -287.4 958 7.0 966 -95.7 999 19.0 1008 308.9 947 -207.5 997 -103.3 1030 39.2 1034 218.6 992 -36.6 1004 64.7 992 -151.8 1030 46.0 1026 -461.6 1143 -340.2 1249 664.5 1127 166.7 1098 -247.2 ABS Error Error measures only based on last 12 months that were forecasted 1600 SQRT Expo Smoothed Average with Trend Forecast Mean 1400 Squared Error 1200 SMSE or RMSE 1000 281 Jap wody 800 MAE 212 600 MAPE 400 200 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 22.7%
