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  • Week123456789

    101112131415161718192021222324252627282930313233343536373839404142434445

  • 464748495051

    -A positive tracking signal (TS) implies that the demand is gher than the forecasts- Because the TS is not close to 0, the forecasting method is flawed.-No trend is observed and also no seasonality is observed. So the forecasting method used is Exponential Smoothing-The forecast for the first week is assumed to be equal to the demand- The value of smoothing constant is taken to be 0.5-For week 2, it is assumed that the demand in week 2 will be equal to the demand in week 1.

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    Column HColumn I

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  • Orders(Actual) Forecast Difference Mod of Difference Square of Diff.220 220 220 48400100 220 -120 120 14400190 160 30 30 900240 175 65 65 4225480 208 273 273 74256470 344 126 126 15939320 407 -87 87 7547510 363 147 147 21481330 437 -107 107 11389

    60 383 -323 323 104561290 222 68 68 4668850 256 594 594 353026160 553 -393 393 154386

    90 356 -266 266 7100130 223 -193 193 37338

    180 127 53 53 2850780 153 627 627 392743100 467 -367 367 134435430 283 147 147 21513

    80 357 -277 277 76543150 218 -68 68 4669190 184 6 6 34520 187 333 333 110834180 354 -174 174 30117140 267 -127 127 16071300 203 97 97 9334290 252 38 38 1467650 271 379 379 143757180 460 -280 280 78637

    80 320 -240 240 5770260 200 -140 140 19630

    600 130 470 470 220850350 365 -15 15 226160 358 -198 198 39011110 259 -149 149 22129320 184 136 136 18393890 252 638 638 406803820 571 249 249 61954210 696 -486 486 235756305 453 -148 148 21837280 379 -99 99 9779350 329 21 21 423400 340 60 60 3633150 370 -220 220 48339300 260 40 40 1606

  • 120 280 -160 160 25589380 200 180 180 32406

    20 290 -270 270 72895400 155 245 245 60027250 277 -27 27 756280 264 16 16 264

    324 10190 3306529MAD 200

    MSE 64833.908385467MAPETS

    -A positive tracking signal (TS) implies that the demand is gher than the forecasts- Because the TS is not close to 0, the forecasting method is flawed.-No trend is observed and also no seasonality is observed. So the forecasting method used is Exponential Smoothing-The forecast for the first week is assumed to be equal to the demand- The value of smoothing constant is taken to be 0.5-For week 2, it is assumed that the demand in week 2 will be equal to the demand in week 1.

    1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 510

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    Column HColumn I

  • 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 510

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    Column HColumn I

  • 1.00 alpha 0.51.200.160.270.570.270.270.290.325.390.240.702.462.966.440.300.803.670.343.460.460.030.640.960.910.320.130.581.563.002.340.780.041.231.350.420.720.302.310.480.350.060.151.470.13

  • 1.330.47

    13.500.610.110.06

    6793%

    133%1.6203463794

    -A positive tracking signal (TS) implies that the demand is gher than the forecasts- Because the TS is not close to 0, the forecasting method is flawed.-No trend is observed and also no seasonality is observed. So the forecasting method used is Exponential Smoothing-The forecast for the first week is assumed to be equal to the demand- The value of smoothing constant is taken to be 0.5-For week 2, it is assumed that the demand in week 2 will be equal to the demand in week 1.

    1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 510

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    Column HColumn I

  • 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 510

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    Exponential Smooting


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