Static Method
Sales Year 1 Year 2 Year 3 Year 4 Year 5
JAN 2,000 3,000 2,000 5,000 5,000
FEB 3,000 4,000 5,000 4,000 2,000
MAR 3,000 3,000 5,000 4,000 3,000
APR 3,000 5,000 3,000 2,000 2,000
MAY 4,000 5,000 4,000 5,000 7,000
JUN 6,000 8,000 6,000 7,000 6,000
JUL 7,000 3,000 7,000 10,000 8,000
AUG 6,000 8,000 10,000 14,000 10,000
SEP 10,000 12,000 15,000 16,000 20,000
OCT 12,000 12,000 15,000 16,000 20,000
NOV 14,000 16,000 18,000 20,000 22,000
DEC 8,000 10,000 8,000 12,000 8,000
Total 78,000 89,000 98,000 115,000 113,000
p = 12 (even) Deseasonalized Demand Regression
Year Month Period Demand Dt
Deseaso
nalized
Demand
Dt
Dt
(based
on
regressio
n)
Seasonal
Factor St
Forecast EtAtBias MSE MAD
Percent
Error
MAPE TS SUMMARY OUTPUT
1 JAN 1
2,000 6,068 0.33 2,588 588 588 588 346,273 588 29.42 29.42 1.00
1 FEB 2 3,000 6,138 0.49 2,913 (87) 87 501 176,952 338 2.91 16.17 1.48 Regression Statistics
1 MAR 3 3,000 6,208 0.48 2,872 (128) 128 373 123,433 268 4.27 12.20 1.39 Multiple R 0.974925619
1APR 43,000 6,278 0.48 2,500 (500) 500 (127) 155,075 326 16.67 13.32 -0.39 R Square 0.950479963
1 MAY 5 4,000 6,348 0.63 3,944 (56) 56 (183) 124,678 272 1.39 10.93 -0.67 Adjusted R Square 0.949403441
1 JUN 6 6,000 6,419 0.93 5,356 (644) 644 (827) 173,086 334 10.74 10.90 -2.48 Standard Error 226.9014716
1 JUL 7
7,000 6,542 6,489 1.08 5,534 (1,466) 1,466 (2,292) 455,240 496 20.94 12.33 -4.63 Observations 48
1 AUG 8 6,000 6,625 6,559 0.91 7,551 1,551 1,551 (742) 698,911 628 25.84 14.02 -1.18
1SEP 9
10,000 6,667 6,629 1.51 11,489 1,489 1,489 747 867,560 723 14.89 14.12 1.03 ANOVA
1 OCT 10 12,000 6,750 6,700 1.79 11,913 (87) 87 660 781,567 660 0.73 12.78 1.00 df SS MS F Significance F
1 NOV 11 14,000 6,875 6,770 2.07 14,377 377 377 1,037 723,454 634 2.69 11.86 1.64 Regression 1 45456339.83 45456339.83 882.9169172 1.14478E-31
1 DEC 12 8,000 7,000 6,840 1.17 7,488 (512) 512 525 685,002 624 6.40 11.41 0.84 Residual 46 2368276.778 51484.27779
2 JAN 13
3,000 6,917 6,910 0.43 2,948 (52) 52 473 632,517 580 1.73 10.66 0.82 Total 47 47824616.61
2 FEB 14
4,000 6,833 6,981 0.57 3,313 (687) 687 (214) 621,082 587 17.18 11.13 -0.36
2 MAR 15 3,000 7,000 7,051 0.43 3,262 262 262 48 584,250 566 8.73 10.97 0.08 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0%
2APR 16 5,000 7,083 7,121 0.70 2,836 (2,164) 2,164 (2,117) 840,507 666 43.29 12.99 -3.18 Intercept 5997.260518 79.19340748 75.72928996 6.1285E-50 5837.852609 6156.668427 5837.852609 6156.668427
2 MAY 17
5,000 7,167 7,191 0.70 4,468 (532) 532 (2,648) 807,704 658 10.64 12.85 -4.03 X Variable 1 70.24578448 2.364070087 29.7139179 1.14478E-31 65.48716276 75.00440621 65.48716276 75.00440621
2 JUN 18 8,000 7,333 7,262 1.10 6,059 (1,941) 1,941 (4,589) 972,128 729 24.26 13.48 -6.29
2 JUL 19 3,000 7,375 7,332 0.41 6,253 3,253 3,253 (1,336) 1,478,005 862 108.44 18.48 -1.55
2 AUG 20 8,000 7,375 7,402 1.08 8,521 521 521 (815) 1,417,679 845 6.51 17.88 -0.96
2SEP 21 12,000 7,500 7,472 1.61 12,950 950 950 135 1,393,120 850 7.91 17.41 0.16 Average of Seasonal Factor St Avg. Seasonal Factors
2 OCT 22 12,000 7,500 7,543 1.59 13,411 1,411 1,411 1,546 1,420,351 875 11.76 17.15 1.77 Month Total
2 NOV 23 16,000 7,375 7,613 2.10 16,167 167 167 1,713 1,359,815 845 1.05 16.45 2.03 JAN 0.427 JAN 0.427 0.427
2 DEC 24 10,000 7,250 7,683 1.30 8,411 (1,589) 1,589 124 1,408,374 876 15.89 16.43 0.14 FEB 0.475 FEB 0.475 0.475
3 JAN 25 2,000 7,333 7,753 0.26 3,308 1,308 1,308 1,432 1,420,438 893 65.38 18.39 1.60 MAR 0.463 MAR 0.463 0.463
3 FEB 26 5,000 7,583 7,824 0.64 3,713 (1,287) 1,287 145 1,429,544 908 25.75 18.67 0.16 APR 0.398 APR 0.398 0.398
3 MAR 27 5,000 7,792 7,894 0.63 3,652 (1,348) 1,348 (1,203) 1,443,909 924 26.96 18.98 –1.30 MAY 0.621 MAY 0.621 0.621
3APR 28 3,000 8,042 7,964 0.38 3,171 171 171 (1,032) 1,393,389 898 5.71 18.50 -1.15 JUN 0.834 JUN 0.834 0.834
3 MAY 29 4,000 8,250 8,034 0.50 4,992 992 992 (40) 1,379,266 901 24.80 18.72 -0.04 JUL 0.853 JUL 0.853 0.853
3 JUN 30 6,000 8,250 8,105 0.74 6,762 762 762 722 1,352,665 896 12.71 18.52 0.81 AUG 1.151 AUG 1.151 1.151
3 JUL 31 7,000 8,292 8,175 0.86 6,972 (28) 28 694 1,309,056 868 0.40 17.94 0.80 SEP 1.733 SEP 1.733 1.733
3 AUG 32 10,000 8,375 8,245 1.21 9,491 (509) 509 186 1,276,231 857 5.09 17.53 0.22 OCT 1.778 OCT 1.778 1.778
3SEP 33 15,000 8,292 8,315 1.80 14,411 (589) 589 (404) 1,248,087 849 3.93 17.12 -0.48 NOV 2.124 NOV 2.124 2.124
3 OCT 34 15,000 8,208 8,386 1.79 14,910 (90) 90 (493) 1,211,615 826 0.60 16.64 -0.60 DEC 1.095 DEC 1.095 1.095
3 NOV 35 18,000 8,208 8,456 2.13 17,958 (42) 42 (536) 1,177,049 804 0.24 16.17 -0.67
3 DEC 36 8,000 8,292 8,526 0.94 9,334 1,334 1,334 798 1,193,763 819 16.67 16.18 0.97
4 JAN 37 5,000 8,458 8,596 0.58 3,667 (1,333) 1,333 (535) 1,209,503 833 26.65 16.46 -0.64
4 FEB 38 4,000 8,750 8,667 0.46 4,113 113 113 (422) 1,178,008 814 2.82 16.10 -0.52
4 MAR 39 4,000 8,958 8,737 0.46 4,042 42 42 (380) 1,147,848 794 1.05 15.72 -0.48
4APR 40 2,000 9,042 8,807 0.23 3,507 1,507 1,507 1,127 1,175,927 812 75.35 17.21 1.39
4 MAY 41 5,000 9,167 8,877 0.56 5,516 516 516 1,642 1,153,731 805 10.31 17.04 2.04
4 JUN 42 7,000 9,417 8,948 0.78 7,466 466 466 2,108 1,131,425 796 6.65 16.79 2.65
4 JUL 43 10,000 9,583 9,018 1.11 7,691 (2,309) 2,309 (201) 1,229,085 832 23.09 16.94 –0.24
4 AUG 44 14,000 9,500 9,088 1.54 10,462 (3,538) 3,538 (3,739) 1,485,675 893 25.27 17.13 -4.19
4SEP 45 16,000 9,375 9,158 1.75 15,871 (129) 129 (3,868) 1,453,028 876 0.80 16.77 -4.41
4 OCT 46 16,000 9,333 9,229 1.73 16,409 409 409 (3,459) 1,425,079 866 2.56 16.46 -3.99
4 NOV 47 20,000 9,417 9,299 2.15 19,748 (252) 252 (3,711) 1,396,112 853 1.26 16.13 -4.35
4 DEC 48 12,000 9,458 9,369 1.28 10,256 (1,744) 1,744 (5,454) 1,430,356 872 14.53 16.10 -6.26
5 JAN 49 5,000 9,333 9,439 0.53 4,027 (973) 973 (6,427) 1,420,490 874 19.46 16.17 -7.36
5 FEB 50 2,000 9,083 9,510 0.21 4,513 2,513 2,513 (3,915) 1,518,356 906 125.64 18.36 -4.32
5 MAR 51 3,000 9,083 9,580 0.31 4,432 1,432 1,432 (2,483) 1,528,783 917 47.73 18.94 -2.71
5APR 52 2,000 9,417 9,650 0.21 3,843 1,843 1,843 (640) 1,564,679 934 92.13 20.34 -0.69
5 MAY 53 7,000 9,667 9,720 0.72 6,039 (961) 961 (1,601) 1,552,568 935 13.72 20.22 -1.71
5 JUN 54 6,000 9,583 9,791 0.61 8,169 2,169 2,169 568 1,610,944 958 36.15 20.51 0.59
5 JUL 55 8,000 9,861 0.81 8,410 410 410 978 1,584,712 948 5.13 20.23 1.03
5 AUG 56 10,000 9,931 1.01 11,432 1,432 1,432 2,410 1,593,039 957 14.32 20.13 2.52
5SEP 57 20,000 10,001 2.00 17,332 (2,668) 2,668 (257) 1,689,953 987 13.34 20.01 -0.26
5 OCT 58 20,000 10,072 1.99 17,908 (2,092) 2,092 (2,349) 1,736,276 1,006 10.46 19.84 -2.34
5 NOV 59 22,000 10,142 2.17 21,538 (462) 462 (2,812) 1,710,467 996 2.10 19.54 -2.82
5 DEC 60
8,000 10,212 0.78 11,179 3,179 3,179 368 1,850,424 1,033 39.74 19.88 0.36
6 JAN 61
Forecasts 4,386 Estimate of standard deviation of forecast error: 1,291
6 FEB 62 4,913
6 MAR 63 4,822
6APR 64 4,178
6 MAY 65 6,563
6 JUN 66 8,872
6 JUL 67 9,129
6 AUG 68 12,403
6SEP 69 18,793
6 OCT 70 19,407
6 NOV 71 23,328
6 DEC 72 12,102
10,000
15,000
20,000
25,000
Comparison between Demand and Forecast
demand
forecast
1. Start by
reformatting
data
Calculate
Errors
Using
Given Data
Red Forecast
Color coding
Moving Average
Sales Year 1 Year 2 Year 3 Year 4 Year 5
JAN 2,000 3,000 2,000 5,000 5,000
FEB 3,000 4,000 5,000 4,000 2,000
MAR 3,000 3,000 5,000 4,000 3,000
APR 3,000 5,000 3,000 2,000 2,000
MAY 4,000 5,000 4,000 5,000 7,000
JUN 6,000 8,000 6,000 7,000 6,000
JUL 7,000 3,000 7,000 10,000 8,000
AUG 6,000 8,000 10,000 14,000 10,000
SEP 10,000 12,000 15,000 16,000 20,000
OCT 12,000 12,000 15,000 16,000 20,000
NOV 14,000 16,000 18,000 20,000 22,000
DEC 8,000 10,000 8,000 12,000 8,000
Total 78,000 89,000 98,000 ###### ######
p = 12 (even)
Using a 12 period moving Average
Year Month Period Demand DtLevel Forecast EtAtBias MSE MAD
Percent
Error
MAPE TS
1 JAN 1 2000
1 FEB 2 3,000
1 MAR 3 3,000
1APR 43,000
1 MAY 5 4,000
1 JUN 6 6,000
1 JUL 7 7,000
1 AUG 8 6,000
1SEP 910,000
1 OCT 10 12,000
1 NOV 11 14,000
1 DEC 12 8,000 6,500
2 JAN 13 3,000 6,583 6,500 3,500 3,500 3,500 942,308 269 116.7 116.7 13.00
2 FEB 14 4,000 6,667 6,583 2,583 2,583 6,083 1,351,687 435 64.6 90.6 14.00
2 MAR 15 3,000 6,667 6,667 3,667 3,667 9,750 2,157,870 650 122.2 101.2 15.00
2APR 16 5,000 6,833 6,667 1,667 1,667 11,417 2,196,615 714 33.3 84.2 16.00
2 MAY 17 5,000 6,917 6,833 1,833 1,833 13,250 2,265,114 779 36.7 74.7 17.00
2 JUN 18 8,000 7,083 6,917 (1,083) 1,083 12,167 2,204,475 796 13.5 64.5 15.28
2 JUL 19 3,000 6,750 7,083 4,083 4,083 16,250 2,966,009 969 136.1 74.7 16.76
2 AUG 20 8,000 6,917 6,750 (1,250) 1,250 15,000 2,895,833 983 15.6 67.3 15.25
2SEP 21 12,000 7,083 6,917 (5,083) 5,083 9,917 3,988,426 1,179 42.4 64.6 8.41
2 OCT 22 12,000 7,083 7,083 (4,917) 4,917 5,000 4,905,934 1,348 41.0 62.2 3.71
2 NOV 23 16,000 7,250 7,083 (8,917) 8,917 (3,917) 8,149,457 1,678 55.7 61.6 –2.33
2 DEC 24 10,000 7,417 7,250 (2,750) 2,750 (6,667) 8,125,000 1,722 27.5 58.8 –3.87
3 JAN 25 2,000 7,333 7,417 5,417 5,417 (1,250) 8,973,611 1,870 270.8 75.1 –0.67
3 FEB 26 5,000 7,417 7,333 2,333 2,333 1,083 8,837,874 1,888 46.7 73.1 0.57
3 MAR 27 5,000 7,583 7,417 2,417 2,417 3,500 8,726,852 1,907 48.3 71.4 1.83
3APR 28 3,000 7,417 7,583 4,583 4,583 8,083 9,165,427 2,003 152.8 76.5 4.04
3 MAY 29 4,000 7,333 7,417 3,417 3,417 11,500 9,251,916 2,052 85.4 77.0 5.61
3 JUN 30 6,000 7,167 7,333 1,333 1,333 12,833 9,002,778 2,028 22.2 74.0 6.33
3 JUL 31 7,000 7,500 7,167 167 167 13,000 8,713,262 1,968 2.4 70.2 6.61
3 AUG 32 10,000 7,667 7,500 (2,500) 2,500 10,500 8,636,285 1,984 25.0 67.9 5.29
3SEP 33 15,000 7,917 7,667 (7,333) 7,333 3,167 10,004,209 2,146 48.9 67.0 1.48
3 OCT 34 15,000 8,167 7,917 (7,083) 7,083 (3,917) 11,185,662 2,292 47.2 66.1 -1.71
3 NOV 35 18,000 8,333 8,167 (9,833) 9,833 (13,750) 13,628,770 2,507 54.6 65.6 -5.48
3 DEC 36 8,000 8,167 8,333 333 333 (13,417) 13,253,279 2,447 4.2 63.1 –5.48
4 JAN 37 5,000 8,417 8,167 3,167 3,167 (10,250) 13,166,104 2,466 63.3 63.1 -4.16
4 FEB 38 4,000 8,333 8,417 4,417 4,417 (5,833) 13,332,968 2,518 110.4 64.9 -2.32
4 MAR 39 4,000 8,250 8,333 4,333 4,333 (1,500) 13,472,578 2,564 108.3 66.5 –0.58
4APR 40 2,000 8,167 8,250 6,250 6,250 4,750 14,112,326 2,656 312.5 75.3 1.79
4 MAY 41 5,000 8,250 8,167 3,167 3,167 7,917 14,012,703 2,669 63.3 74.9 2.97
4 JUN 42 7,000 8,333 8,250 1,250 1,250 9,167 13,716,270 2,635 17.9 73.0 3.48
4 JUL 43 10,000 8,583 8,333 (1,667) 1,667 7,500 13,461,886 2,612 16.7 71.2 2.87
4 AUG 44 14,000 8,917 8,583 (5,417) 5,417 2,083 13,822,759 2,676 38.7 70.2 0.78
4SEP 45 16,000 9,000 8,917 (7,083) 7,083 (5,000) 14,630,556 2,774 44.3 69.4 -1.80
4 OCT 46 16,000 9,083 9,000 (7,000) 7,000 (12,000) 15,377,717 2,866 43.8 68.6 –4.19
4 NOV 47 20,000 9,250 9,083 (10,917) 10,917 (22,917) 17,586,141 3,037 54.6 68.2 -7.55
4 DEC 48 12,000 9,583 9,250 (2,750) 2,750 (25,667) 17,377,315 3,031 22.9 67.0 –8.47
5 JAN 49 5,000 9,583 9,583 4,583 4,583 (21,083) 17,451,389 3,063 91.7 67.6 -6.88
5 FEB 50 2,000 9,417 9,583 7,583 7,583 (13,500) 18,252,500 3,153 379.2 75.8 –4.28
5 MAR 51 3,000 9,333 9,417 6,417 6,417 (7,083) 18,701,934 3,217 213.9 79.4 –2.20
5APR 52 2,000 9,333 9,333 7,333 7,333 250 19,376,469 3,296 366.7 86.5 0.08
5 MAY 53 7,000 9,500 9,333 2,333 2,333 2,583 19,113,601 3,278 33.3 85.2 0.79
5 JUN 54 6,000 9,417 9,500 3,500 3,500 6,083 18,986,497 3,282 58.3 84.6 1.85
5 JUL 55 8,000 9,250 9,417 1,417 1,417 7,500 18,677,778 3,248 17.7 83.1 2.31
5 AUG 56 10,000 8,917 9,250 (750) 750 6,750 18,354,291 3,204 7.5 81.3 2.11
5SEP 57 20,000 9,250 8,917 (11,083) 11,083 (4,333) 20,187,378 3,342 55.4 80.8 -1.30
5 OCT 58 20,000 9,583 9,250 (10,750) 10,750 (15,083) 21,831,777 3,470 53.8 80.2 -4.35
5 NOV 59 22,000 9,750 9,583 (12,417) 12,417 (27,500) 24,074,859 3,621 56.4 79.7 -7.59
6 NOV 71 9,417
6 DEC 72 9,417
0
5000
10000
15000
20000
25000
1357911 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51 53 55 57 59 61 63 65 67 69 71
Comparison between Demand & Forecast
1. Start by
reformatting
data
Calculate
Errors
Graph
Using
Winter’s
Model
Given Data
Red Forecast
Color coding
Simple Exponential Smoothing
Sales Year 1 Year 2 Year 3 Year 4 Year 5
JAN 2,000 3,000 2,000 5,000 5,000
FEB 3,000 4,000 5,000 4,000 2,000
MAR 3,000 3,000 5,000 4,000 3,000
APR 3,000 5,000 3,000 2,000 2,000
MAY 4,000 5,000 4,000 5,000 7,000
JUN 6,000 8,000 6,000 7,000 6,000
JUL 7,000 3,000 7,000 10,000 8,000
AUG 6,000 8,000 10,000 14,000 10,000
SEP 10,000 12,000 15,000 16,000 20,000
OCT 12,000 12,000 15,000 16,000 20,000
NOV 14,000 16,000 18,000 20,000 22,000
DEC 8,000 10,000 8,000 12,000 8,000
Total 78,000 89,000 98,000 115,000 113,000
p = 12 (even)
Exponential Smoothing
Alpha = 1
Year Month Period Demand
DtLevel Forecast EtAtBias MSE MAD
Percent
Error
MAPE TS
0 8,217
1 JAN 1 2,000 2,000 8,217 6,217 6,217 6,217 38,646,944 6,217 310.8 310.8 1.00
1 FEB 2 3,000 3,000 2,000 (1,000) 1,000 5,217 19,823,472 3,608 33.3 172.1 1.45
1 MAR 3 3,000 3,000 3,000 – – 5,217 13,215,648 2,406 0.0 114.7 2.17
1 APR 4 3,000 3,000 3,000 – – 5,217 9,911,736 1,804 0.0 86.0 2.89
1 MAY 5 4,000 4,000 3,000 (1,000) 1,000 4,217 8,129,389 1,643 25.0 73.8 2.57
1JUN 66,000 6,000 4,000 (2,000) 2,000 2,217 7,441,157 1,703 33.3 67.1 1.30
1JUL 77,000 7,000 6,000 (1,000) 1,000 1,217 6,520,992 1,602 14.3 59.5 0.76
1 AUG 8 6,000 6,000 7,000 1,000 1,000 2,217 5,830,868 1,527 16.7 54.2 1.45
1SEP 910,000 10,000 6,000 (4,000) 4,000 (1,783) 6,960,772 1,802 40.0 52.6 -0.99
1 OCT 10 12,000 12,000 10,000 (2,000) 2,000 (3,783) 6,664,694 1,822 16.7 49.0 -2.08
1 NOV 11 14,000 14,000 12,000 (2,000) 2,000 (5,783) 6,422,449 1,838 14.3 45.9 -3.15
1 DEC 12 8,000 8,000 14,000 6,000 6,000 217 8,887,245 2,185 75.0 48.3 0.10
2 JAN 13 3,000 3,000 8,000 5,000 5,000 5,217 10,126,688 2,401 166.7 57.4 2.17
2 FEB 14 4,000 4,000 3,000 (1,000) 1,000 4,217 9,474,782 2,301 25.0 55.1 1.83
2 MAR 15 3,000 3,000 4,000 1,000 1,000 5,217 8,909,796 2,214 33.3 53.6 2.36
2 APR 16 5,000 5,000 3,000 (2,000) 2,000 3,217 8,602,934 2,201 40.0 52.8 1.46
2 MAY 17 5,000 5,000 5,000 – – 3,217 8,096,879 2,072 0.0 49.7 1.55
2JUN 18 8,000 8,000 5,000 (3,000) 3,000 217 8,147,052 2,123 37.5 49.0 0.10
2JUL 19 3,000 3,000 8,000 5,000 5,000 5,217 9,034,050 2,275 166.7 55.2 2.29
2 AUG 20 8,000 8,000 3,000 (5,000) 5,000 217 9,832,347 2,411 62.5 55.6 0.09
2SEP 21 12,000 12,000 8,000 (4,000) 4,000 (3,783) 10,126,045 2,487 33.3 54.5 -1.52
2 OCT 22 12,000 12,000 12,000 – – (3,783) 9,665,770 2,373 0.0 52.0 -1.59
2 NOV 23 16,000 16,000 12,000 (4,000) 4,000 (7,783) 9,941,171 2,444 25.0 50.8 -3.18
2 DEC 24 10,000 10,000 16,000 6,000 6,000 (1,783) 11,026,956 2,592 60.0 51.2 -0.69
3 JAN 25 2,000 2,000 10,000 8,000 8,000 6,217 13,145,878 2,809 400.0 65.2 2.21
3 FEB 26 5,000 5,000 2,000 (3,000) 3,000 3,217 12,986,421 2,816 60.0 65.0 1.14
3 MAR 27 5,000 5,000 5,000 – – 3,217 12,505,442 2,712 0.0 62.6 1.19
3 APR 28 3,000 3,000 5,000 2,000 2,000 5,217 12,201,677 2,686 66.7 62.7 1.94
3 MAY 29 4,000 4,000 3,000 (1,000) 1,000 4,217 11,815,412 2,628 25.0 61.4 1.60
3JUN 30 6,000 6,000 4,000 (2,000) 2,000 2,217 11,554,898 2,607 33.3 60.5 0.85
3JUL 31 7,000 7,000 6,000 (1,000) 1,000 1,217 11,214,418 2,555 14.3 59.0 0.48
3 AUG 32 10,000 10,000 7,000 (3,000) 3,000 (1,783) 11,145,217 2,569 30.0 58.1 -0.69
3SEP 33 15,000 15,000 10,000 (5,000) 5,000 (6,783) 11,565,059 2,643 33.3 57.3 -2.57
3 OCT 34 15,000 15,000 15,000 – – (6,783) 11,224,910 2,565 0.0 55.6 -2.64
3 NOV 35 18,000 18,000 15,000 (3,000) 3,000 (9,783) 11,161,341 2,578 16.7 54.5 -3.80
3 DEC 36 8,000 8,000 18,000 10,000 10,000 217 13,629,082 2,784 125.0 56.5 0.08
4 JAN 37 5,000 5,000 8,000 3,000 3,000 3,217 13,503,971 2,790 60.0 56.6 1.15
4 FEB 38 4,000 4,000 5,000 1,000 1,000 4,217 13,174,920 2,743 25.0 55.8 1.54
4 MAR 39 4,000 4,000 4,000 – – 4,217 12,837,101 2,672 0.0 54.3 1.58
4 APR 40 2,000 2,000 4,000 2,000 2,000 6,217 12,616,174 2,655 100.0 55.5 2.34
4 MAY 41 5,000 5,000 2,000 (3,000) 3,000 3,217 12,527,974 2,664 60.0 55.6 1.21
4JUN 42 7,000 7,000 5,000 (2,000) 2,000 1,217 12,324,927 2,648 28.6 54.9 0.46
4JUL 43 10,000 10,000 7,000 (3,000) 3,000 (1,783) 12,247,603 2,656 30.0 54.4 -0.67
4 AUG 44 14,000 14,000 10,000 (4,000) 4,000 (5,783) 12,332,885 2,687 28.6 53.8 -2.15
4SEP 45 16,000 16,000 14,000 (2,000) 2,000 (7,783) 12,147,710 2,671 12.5 52.9 -2.91
4 OCT 46 16,000 16,000 16,000 – – (7,783) 11,883,629 2,613 0.0 51.7 -2.98
4 NOV 47 20,000 20,000 16,000 (4,000) 4,000 (11,783) 11,971,212 2,643 20.0 51.0 -4.46
4 DEC 48 12,000 12,000 20,000 8,000 8,000 (3,783) 13,055,145 2,755 66.7 51.4 -1.37
5 JAN 49 5,000 5,000 12,000 7,000 7,000 3,217 13,788,713 2,841 140.0 53.2 1.13
5 FEB 50 2,000 2,000 5,000 3,000 3,000 6,217 13,692,939 2,844 150.0 55.1 2.19
5 MAR 51 3,000 3,000 2,000 (1,000) 1,000 5,217 13,444,058 2,808 33.3 54.7 1.86
5 APR 52 2,000 2,000 3,000 1,000 1,000 6,217 13,204,749 2,773 50.0 54.6 2.24
5 MAY 53 7,000 7,000 2,000 (5,000) 5,000 1,217 13,427,301 2,815 71.4 54.9 0.43
5JUN 54 6,000 6,000 7,000 1,000 1,000 2,217 13,197,166 2,782 16.7 54.2 0.80
6JUL 67 8,000
6 AUG 68 8,000
6SEP 69 8,000
6 OCT 70 8,000
6 NOV 71 8,000
6 DEC 72 8,000
–
5,000
10,000
15,000
20,000
25,000
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51 53 55 57 59 61 63 65 67 69 71 73 75
Comparison between Demand and Forecast
Given Data
Red Forecast
Color coding
Calculate
Errors
Graph
Using
Winter’s
Model
1. Start by
reformatting
data
Holt’s Model
Sales Year 1 Year 2 Year 3 Year 4 Year 5
JAN 2,000 3,000 2,000 5,000 5,000
FEB 3,000 4,000 5,000 4,000 2,000
MAR 3,000 3,000 5,000 4,000 3,000
APR 3,000 5,000 3,000 2,000 2,000
MAY 4,000 5,000 4,000 5,000 7,000
JUN 6,000 8,000 6,000 7,000 6,000
JUL 7,000 3,000 7,000 10,000 8,000
AUG 6,000 8,000 10,000 14,000 10,000
SEP 10,000 12,000 15,000 16,000 20,000
OCT 12,000 12,000 15,000 16,000 20,000
NOV 14,000 16,000 18,000 20,000 22,000
DEC 8,000 10,000 8,000 12,000 8,000
Total 78,000 89,000 98,000 115,000 113,000
p = 12 (even)
Smoothing Constants
Alpha = 0.99294
Beta = 0Deseasonalized Demand Regression
Year Month Period Demand DtLevel Trend Forecast EtAtbias MSE MAD
Percent
Error
MAPE TS SUMMARY OUTPUT
0 5,997 70
1 JAN 1 2,000 2,029 70 6,068 4,068 4,068 4,068 16,544,608 4,068 203.4 203.4 1.00 Regression Statistics
1 FEB 2 3,000 2,994 70 2,099 (901) 901 3,166 8,678,220 2,484 30.0 116.7 1.27 Multiple R 0.974925619
1 MAR 3 3,000 3,000 70 3,064 64 64 3,230 5,786,840 1,677 2.1 78.5 1.93 R Square 0.950479963
1APR 43,000 3,000 70 3,071 71 71 3,301 4,341,380 1,276 2.4 59.5 2.59 Adjusted R Square 0.949403441
1 MAY 5 4,000 3,993 70 3,071 (929) 929 2,372 3,645,807 1,206 23.2 52.2 1.97 Standard Error 226.9014716
1 JUN 6
6,000 5,986 70 4,064 (1,936) 1,936 435 3,663,061 1,328 32.3 48.9 0.33 Observations 48
1 JUL 7 7,000 6,993 70 6,057 (943) 943 (508) 3,266,919 1,273 13.5 43.8 –0.40
1 AUG 8
6,000 6,008 70 7,064 1,064 1,064 556 2,999,955 1,247 17.7 40.6 0.45 ANOVA
1SEP 910,000 9,972 70 6,078 (3,922) 3,922 (3,367) 4,375,956 1,544 39.2 40.4 –2.18 df SS MS F Significance F
1 OCT 10 12,000 11,986 70 10,043 (1,957) 1,957 (5,324) 4,321,527 1,586 16.3 38.0 –3.36 Regression 1 45456339.83 45456339.83 882.9169172 1.14478E-31
1 NOV 11 14,000 13,986 70 12,056 (1,944) 1,944 (7,268) 4,272,072 1,618 13.9 35.8 –4.49 Residual 46 2368276.778 51484.27779
1 DEC 12
8,000 8,043 70 14,057 6,057 6,057 (1,211) 6,972,846 1,988 75.7 39.1 -0.61 Total 47 47824616.61
2 JAN 13
3,000 3,036 70 8,113 5,113 5,113 3,902 8,447,483 2,228 170.4 49.2 1.75
2 FEB 14 4,000 3,994 70 3,106 (894) 894 3,008 7,901,132 2,133 22.3 47.3 1.41 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0%
2 MAR 15 3,000 3,008 70 4,064 1,064 1,064 4,072 7,449,854 2,062 35.5 46.5 1.98 Intercept 5997.260518 79.19340748 75.72928996 6.1285E-50 5837.852609 6156.668427 5837.852609 6156.668427
2APR 16
5,000 4,986 70 3,078 (1,922) 1,922 2,150 7,215,175 2,053 38.4 46.0 1.05 X Variable 1 70.24578448 2.364070087 29.7139179 1.14478E-31 65.48716276 75.00440621 65.48716276 75.00440621
2 MAY 17 5,000 5,000 70 5,057 57 57 2,207 6,790,942 1,936 1.1 43.4 1.14
2 JUN 18 8,000 7,979 70 5,071 (2,929) 2,929 (723) 6,890,396 1,991 36.6 43.0 -0.36
2 JUL 19 3,000 3,036 70 8,050 5,050 5,050 4,327 7,869,741 2,152 168.3 49.6 2.01
2 AUG 20 8,000 7,965 70 3,106 (4,894) 4,894 (567) 8,673,855 2,289 61.2 50.2 -0.25 Avg. Seasonal Factors
2SEP 21 12,000 11,972 70 8,036 (3,964) 3,964 (4,532) 9,009,191 2,369 33.0 49.4 -1.91
2 OCT 22 12,000 12,000 70 12,042 42 42 (4,489) 8,599,764 2,263 0.4 47.1 -1.98 JAN 0.427
2 NOV 23 16,000 15,972 70 12,071 (3,929) 3,929 (8,419) 8,897,192 2,335 24.6 46.2 –3.60 FEB 0.475
2 DEC 24 10,000 10,043 70 16,042 6,042 6,042 (2,376) 10,047,794 2,490 60.4 46.8 -0.95 MAR 0.463
3 JAN 25 2,000 2,057 70 10,113 8,113 8,113 5,737 12,278,671 2,715 405.6 61.1 2.11 APR 0.398
3 FEB 26 5,000 4,980 70 2,128 (2,872) 2,872 2,864 12,123,756 2,721 57.4 61.0 1.05 MAY 0.621
3 MAR 27 5,000 5,000 70 5,050 50 50 2,914 11,674,821 2,622 1.0 58.7 1.11 JUN 0.834
3APR 28 3,000 3,015 70 5,071 2,071 2,071 4,985 11,410,983 2,602 69.0 59.1 1.92 JUL 0.853
3 MAY 29 4,000 3,994 70 3,085 (915) 915 4,070 11,046,379 2,544 22.9 57.9 1.60 AUG 1.151
3 JUN 30 6,000 5,986 70 4,064 (1,936) 1,936 2,133 10,803,131 2,524 32.3 57.0 0.85 SEP 1.733
3 JUL 31 7,000 6,993 70 6,057 (943) 943 1,190 10,483,355 2,473 13.5 55.6 0.48 OCT 1.778
3 AUG 32 10,000 9,979 70 7,064 (2,936) 2,936 (1,746) 10,425,205 2,487 29.4 54.8 -0.70 NOV 2.124
3SEP 33 15,000 14,965 70 10,050 (4,950) 4,950 (6,697) 10,851,940 2,562 33.0 54.1 -2.61 DEC 1.095
3 OCT 34 15,000 15,000 70 15,035 35 35 (6,662) 10,532,802 2,488 0.2 52.5 -2.68
3 NOV 35 18,000 17,979 70 15,070 (2,930) 2,930 (9,591) 10,477,064 2,500 16.3 51.5 -3.84
3 DEC 36 8,000 8,071 70 18,050 10,050 10,050 458 12,991,408 2,710 125.6 53.6 0.17
4 JAN 37 5,000 5,022 70 8,141 3,141 3,141 3,600 12,906,976 2,722 62.8 53.8 1.32
4 FEB 38 4,000 4,008 70 5,092 1,092 1,092 4,692 12,598,724 2,679 27.3 53.1 1.75
4 MAR 39 4,000 4,001 70 4,078 78 78 4,770 12,275,836 2,612 1.9 51.8 1.83
4APR 40 2,000 2,015 70 4,071 2,071 2,071 6,841 12,076,145 2,599 103.5 53.1 2.63
4 MAY 41 5,000 4,979 70 2,085 (2,915) 2,915 3,926 11,988,872 2,606 58.3 53.2 1.51
4 JUN 42 7,000 6,986 70 5,050 (1,950) 1,950 1,975 11,793,991 2,591 27.9 52.6 0.76
4 JUL 43 10,000 9,979 70 7,056 (2,944) 2,944 (968) 11,721,209 2,599 29.4 52.1 -0.37
4 AUG 44 14,000 13,972 70 10,049 (3,951) 3,951 (4,919) 11,809,519 2,630 28.2 51.5 -1.87
4SEP 45 16,000 15,986 70 14,042 (1,958) 1,958 (6,876) 11,632,251 2,615 12.2 50.7 -2.63
4 OCT 46 16,000 16,000 70 16,056 56 56 (6,820) 11,379,445 2,559 0.4 49.6 -2.67
4 NOV 47 20,000 19,972 70 16,071 (3,929) 3,929 (10,749) 11,465,837 2,588 19.6 48.9 -4.15
4 DEC 48 12,000 12,057 70 20,042 8,042 8,042 (2,707) 12,574,498 2,702 67.0 49.3 -1.00
5 JAN 49 5,000 5,050 70 12,127 7,127 7,127 4,420 13,354,509 2,792 142.5 51.2 1.58
5 FEB 50 2,000 2,022 70 5,121 3,121 3,121 7,541 13,282,181 2,799 156.0 53.3 2.69
5 MAR 51 3,000 2,994 70 2,092 (908) 908 6,633 13,037,902 2,762 30.3 52.9 2.40
5APR 52 2,000 2,008 70 3,064 1,064 1,064 7,697 12,808,937 2,729 53.2 52.9 2.82
5 MAY 53 7,000 6,965 70 2,078 (4,922) 4,922 2,775 13,024,400 2,770 70.3 53.2 1.00
5 JUN 54 6,000 6,007 70 7,035 1,035 1,035 3,810 12,803,063 2,738 17.3 52.5 1.39
5 JUL 55 8,000 7,986 70 6,078 (1,922) 1,922 1,888 12,637,475 2,723 24.0 52.0 0.69
5 AUG 56 10,000 9,986 70 8,057 (1,943) 1,943 (56) 12,479,245 2,709 19.4 51.4 -0.02
5SEP 57 20,000 19,930 70 10,057 (9,943) 9,943 (9,999) 13,994,922 2,836 49.7 51.4 -3.53
5 OCT 58 20,000 20,000 70 20,000 (0) 0 (9,999) 13,753,630 2,787 0.0 50.5 -3.59
5 NOV 59 22,000 21,986 70 20,070 (1,930) 1,930 (11,929) 13,583,636 2,773 8.8 49.8 -4.30
5 DEC 60
8,000 8,099 70 22,057 14,057 14,057 2,128 16,650,381 2,961 175.7 51.9 0.72
6 JAN 61
Forecasts 8,170 Estimate of standard deviation of forecast error: 3701
6 FEB 62 8,240
6 MAR 63 8,310
6APR 64 8,380
6 MAY 65 8,451
6 JUN 66 8,521
6 JUL 67 8,591
6 AUG 68 8,661
6SEP 69 8,732
6 OCT 70 8,802
6 NOV 71 8,872
6 DEC 72 8,942
–
5,000
10,000
15,000
20,000
25,000
1357911 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51 53 55 57 59 61 63 65 67 69 71 73 75
Comparison between Demand & Forecast
Given Data
Red Forecast
Color coding
Calculate
Errors
Using
Winter‘s
1. Start by
reformatting
data
Winter’s Model
Sales Year 1 Year 2 Year 3 Year 4 Year 5
JAN 2,000 3,000 2,000 5,000 5,000
FEB 3,000 4,000 5,000 4,000 2,000
MAR 3,000 3,000 5,000 4,000 3,000
APR 3,000 5,000 3,000 2,000 2,000
MAY 4,000 5,000 4,000 5,000 7,000
JUN 6,000 8,000 6,000 7,000 6,000
JUL 7,000 3,000 7,000 10,000 8,000
AUG 6,000 8,000 10,000 14,000 10,000
SEP 10,000 12,000 15,000 16,000 20,000
OCT 12,000 12,000 15,000 16,000 20,000
NOV 14,000 16,000 18,000 20,000 22,000
DEC 8,000 10,000 8,000 12,000 8,000
Total 78,000 89,000 98,000 115,000 113,000
p = 12 (even)
Smoothing Constants
Alpha = 0.0004
Beta = 0.93202
Gamma = 0
Year Month Period Demand DtLevel Trend
Seasonal
Factor St
Forecast EtAtBias MSE MAD
Percent
Error
MAPE TS Deseasonalized Demand Regression
0 5,997 70 SUMMARY OUTPUT
1 JAN 1 2,000 6,067 70 0.43 2,588 588 588 588 346,284 588 29.4 29.4 1.00
1 FEB 2 3,000 6,137 70 0.47 2,912 (88) 88 501 176,999 338 2.9 16.2 1.48 Regression Statistics
1 MAR 3 3,000 6,207 70 0.46 2,871 (129) 129 372 123,522 268 4.3 12.2 1.39 Multiple R 0.974925619
1 APR 4 3,000 6,277 70 0.40 2,499 (501) 501 (129) 155,306 326 16.7 13.3 -0.39 R Square 0.950479963
1 MAY 5 4,000 6,347 70 0.62 3,944 (56) 56 (185) 124,877 272 1.4 10.9 -0.68 Adjusted R Square 0.949403441
1 JUN 6 6,000 6,418 71 0.83 5,355 (645) 645 (830) 173,408 334 10.8 10.9 -2.48 Standard Error 226.9014716
1 JUL 7 7,000 6,490 71 0.85 5,534 (1,466) 1,466 (2,296) 455,551 496 20.9 12.3 -4.63 Observations 48
1 AUG 8 6,000 6,560 71 1.15 7,553 1,553 1,553 (743) 699,922 628 25.9 14.0 -1.18
1SEP 910,000 6,631 71 1.73 11,492 1,492 1,492 749 869,449 724 14.9 14.1 1.03 ANOVA
1 OCT 10 12,000 6,701 71 1.78 11,916 (84) 84 664 783,217 660 0.7 12.8 1.01 df SS MS F Significance F
1 NOV 11 14,000 6,772 70 2.12 14,381 381 381 1,046 725,237 635 2.7 11.9 1.65 Regression 1 45456339.83 45456339.83 882.9169172 1.14478E-31
1 DEC 12 8,000 6,842 71 1.09 7,490 (510) 510 536 686,442 624 6.4 11.4 0.86 Residual 46 2368276.778 51484.27779
2 JAN 13 3,000 6,913 71 0.43 2,949 (51) 51 485 633,837 580 1.7 10.7 0.84 Total 47 47824616.61
2 FEB 14 4,000 6,984 71 0.47 3,314 (686) 686 (201) 622,159 588 17.1 11.1 -0.34
2 MAR 15 3,000 7,055 71 0.46 3,264 264 264 64 585,331 566 8.8 11.0 0.11 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0%
2 APR 16 5,000 7,129 73 0.40 2,838 (2,162) 2,162 (2,099) 840,961 666 43.2 13.0 -3.15 Intercept 5997.260518 79.19340748 75.72928996 6.1285E-50 5837.852609 6156.668427 5837.852609 6156.668427
2 MAY 17 5,000 7,202 73 0.62 4,474 (526) 526 (2,624) 807,739 658 10.5 12.8 -3.99 X Variable 1 70.24578448 2.364070087 29.7139179 1.14478E-31 65.48716276 75.00440621 65.48716276 75.00440621
2 JUN 18 8,000 7,276 74 0.83 6,070 (1,930) 1,930 (4,554) 969,745 728 24.1 13.5 -6.25
2 JUL 19 3,000 7,349 73 0.85 6,269 3,269 3,269 (1,285) 1,481,111 862 109.0 18.5 -1.49
2 AUG 20 8,000 7,421 73 1.15 8,543 543 543 (742) 1,421,813 846 6.8 17.9 -0.88
2SEP 21 12,000 7,494 72 1.73 12,987 987 987 245 1,400,489 853 8.2 17.5 0.29 Avg. Seasonal Factors
2 OCT 22 12,000 7,566 72 1.78 13,453 1,453 1,453 1,698 1,432,798 880 12.1 17.2 1.93
2 NOV 23 16,000 7,638 72 2.12 16,220 220 220 1,918 1,372,611 851 1.4 16.5 2.25 JAN 0.427
2 DEC 24 10,000 7,710 73 1.09 8,440 (1,560) 1,560 358 1,416,813 881 15.6 16.5 0.41 FEB 0.475
3 JAN 25 2,000 7,782 71 0.43 3,320 1,320 1,320 1,679 1,429,864 899 66.0 18.5 1.87 MAR 0.463
3 FEB 26 5,000 7,854 72 0.47 3,727 (1,273) 1,273 405 1,437,229 913 25.5 18.7 0.44 APR 0.398
3 MAR 27 5,000 7,928 73 0.46 3,667 (1,333) 1,333 (928) 1,449,811 929 26.7 19.0 -1.00 MAY 0.621
3 APR 28 3,000 8,001 73 0.40 3,186 186 186 (742) 1,399,269 902 6.2 18.6 -0.82 JUN 0.834
3 MAY 29 4,000 8,074 73 0.62 5,017 1,017 1,017 275 1,386,662 906 25.4 18.8 0.30 JUL 0.853
3 JUN 30 6,000 8,146 72 0.83 6,797 797 797 1,072 1,361,619 902 13.3 18.6 1.19 AUG 1.151
3 JUL 31 7,000 8,218 72 0.85 7,009 9 9 1,081 1,317,699 874 0.1 18.0 1.24 SEP 1.733
3 AUG 32 10,000 8,291 72 1.15 9,544 (456) 456 625 1,283,027 860 4.6 17.6 0.73 OCT 1.778
3SEP 33 15,000 8,363 73 1.73 14,493 (507) 507 118 1,251,922 850 3.4 17.2 0.14 NOV 2.124
3 OCT 34 15,000 8,436 73 1.78 15,000 (0) 0 118 1,215,101 825 0.0 16.7 0.14 DEC 1.095
3 NOV 35 18,000 8,508 73 2.12 18,069 69 69 188 1,180,521 803 0.4 16.2 0.23
3 DEC 36 8,000 8,581 72 1.09 9,394 1,394 1,394 1,581 1,201,698 820 17.4 16.2 1.93
4 JAN 37 5,000 8,654 73 0.43 3,691 (1,309) 1,309 273 1,215,508 833 26.2 16.5 0.33
4 FEB 38 4,000 8,727 73 0.47 4,141 141 141 414 1,184,047 815 3.5 16.2 0.51
4 MAR 39 4,000 8,800 73 0.46 4,071 71 71 485 1,153,817 796 1.8 15.8 0.61
4 APR 40 2,000 8,872 72 0.40 3,533 1,533 1,533 2,019 1,183,745 814 76.7 17.3 2.48
4 MAY 41 5,000 8,943 71 0.62 5,557 557 557 2,575 1,162,431 808 11.1 17.2 3.19
4 JUN 42 7,000 9,014 71 0.83 7,521 521 521 3,097 1,141,224 801 7.4 16.9 3.87
4 JUL 43 10,000 9,086 72 0.85 7,748 (2,252) 2,252 845 1,232,575 835 22.5 17.1 1.01
4 AUG 44 14,000 9,159 73 1.15 10,542 (3,458) 3,458 (2,613) 1,476,260 894 24.7 17.2 -2.92
4SEP 45 16,000 9,233 73 1.73 16,000 (0) 0 (2,613) 1,443,454 874 0.0 16.9 -2.99
4 OCT 46 16,000 9,306 73 1.78 16,546 546 546 (2,066) 1,418,563 867 3.4 16.6 -2.38
4 NOV 47 20,000 9,379 73 2.12 19,917 (83) 83 (2,149) 1,388,526 851 0.4 16.2 -2.53
4 DEC 48 12,000 9,452 74 1.09 10,347 (1,653) 1,653 (3,802) 1,416,523 867 13.8 16.2 -4.38
5 JAN 49 5,000 9,527 74 0.43 4,064 (936) 936 (4,738) 1,405,499 869 18.7 16.2 -5.45
5 FEB 50 2,000 9,599 72 0.47 4,556 2,556 2,556 (2,182) 1,508,081 902 127.8 18.5 -2.42
5 MAR 51 3,000 9,670 71 0.46 4,474 1,474 1,474 (708) 1,521,128 914 49.1 19.1 -0.77
5 APR 52 2,000 9,740 70 0.40 3,879 1,879 1,879 1,172 1,559,780 932 94.0 20.5 1.26
5 MAY 53 7,000 9,810 70 0.62 6,095 (905) 905 266 1,545,814 932 12.9 20.4 0.29
5 JUN 54 6,000 9,879 69 0.83 8,244 2,244 2,244 2,510 1,610,406 956 37.4 20.7 2.63
5 JUL 55 8,000 9,948 69 0.85 8,484 484 484 2,994 1,585,392 947 6.1 20.4 3.16
5 AUG 56 10,000 10,016 68 1.15 11,531 1,531 1,531 4,525 1,598,915 958 15.3 20.3 4.72
5SEP 57 20,000 10,085 69 1.73 17,476 (2,524) 2,524 2,001 1,682,599 985 12.6 20.2 2.03
5 OCT 58 20,000 10,154 69 1.78 18,055 (1,945) 1,945 56 1,718,846 1,002 9.7 20.0 0.06
5 NOV 59 22,000 10,224 69 2.12 21,712 (288) 288 (232) 1,691,121 990 1.3 19.7 -0.23
5 DEC 60 8,000 10,292 68 1.09 11,268 3,268 3,268 3,036 1,840,941 1,028 40.9 20.0 2.95
6 JAN 61 Forecasts 0.43 4,420 Estimate of standard deviation of forecast error: 1,285
6 FEB 62 0.47 4,949
6 MAR 63 0.46 4,856
6 APR 64 0.40 4,207
6 MAY 65 0.62 6,607
6 JUN 66 0.83 8,929 Winter’s model seems to result in lowest forecast error
6 JUL 67 0.85 9,185
6 AUG 68 1.15 12,476
6SEP 69 1.73 18,901
6 OCT 70 1.78 19,514
6 NOV 71 2.12 23,452
6 DEC 72 1.09 12,164
–
5,000
10,000
15,000
20,000
25,000
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51 53 55 57 59 61 63 65 67 69 71 73 75
Comparison between Demand and Forecast
1. Start by
reformatting
data
Calculate
Errors
Graph
Using Winter‘s
Model
Given Data
Red Forecast
Color coding
Graph
Using Winter‘s
Model