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- 1. The use, disclosure, reproduction, modification, transfer, or transmittal of this work for any purpose in any form or by any means without the written permission of United Parcel Service of America, Inc. is strictly prohibited. Scott (Guen) Pak Saint Peter’s University
- 2. 2 © Copyright 2016 United Parcel Service of America, Inc. All Rights Reserved Introduction • All work is solely hypothetical but used Real Statistical modeling. • Biggest point here is the data that the analyst used, has other variables to get his predictions. • Mine has no interference from the business requirement • Data is PURELY from Raw (Face Value)
- 3. 3 © Copyright 2016 United Parcel Service of America, Inc. All Rights Reserved The sample data I extracted from another analyst 1 2 3 4 5 6 7 8 9 10 11 12Total 2014 268 1 a 78,981 78,625 82,147 92,685 80,880 92,734 94,159 104,429 94,671 104,402 114,256 135,814 1,153,783 2015 268 1 a 105,280 95,531 101,834 97,145 98,481 103,324 136,203 149,595 130,023 142,857 140,046 181,172 1,481,491 26,299 16,906 19,687 4,460 17,601 10,590 42,044 45,166 35,352 38,455 25,790 45,358 327,708 33.30% 21.50% 23.97% 4.81% 21.76% 11.42% 44.65% 43.25% 37.34% 36.83% 22.57% 33.40% 28.40% 2016 268 1 a 122,993 101,539 102,169 106,036 110,253 118,571 117,092 118,270 114,110 115,246 119,871 157,026 1,403,176 2017 268 1 a 125,463 123,706 113,812 114,374 113,923 118,939 122,022 123,238 118,939 120,367 126,145 160,831 1,481,759 I ran a test for 2014 and 2015 = 24 months. And bumped it against his 2016 actuals/forecast numbers.
- 4. 4 © Copyright 2016 United Parcel Service of America, Inc. All Rights Reserved Smoothing and deseasonlizing 0 20000 40000 60000 80000 100000 120000 140000 160000 7 9 11 13 15 17 REsult of 2x12MA Smoother Unsmooth Smoothed -20% -10% 0% 10% 20% 30% 40% 1 2 3 4 5 6 7 8 9 10 11 12 % Skew y = 2255.2x + 83901 0 20000 40000 60000 80000 100000 120000 140000 160000 0 5 10 15 20 25 30 Deseasonalized Data You can see Dec (12) has a 30% spike in MIPS usage
- 5. 5 © Copyright 2016 United Parcel Service of America, Inc. All Rights Reserved -0.5 -0.4 -0.3 -0.2 -0.1 0 0.1 0.2 0.3 0.4 0.5 1 2 3 4 5 6 7 8 9 10 11 12 Autocorrelation Lag The Autocorrelation explains the deviation of error and you can see Jan and Feb touching close to the critical value. Which means uncertainty. This chart explains the widening of the gap on the next slide.
- 6. 6 © Copyright 2016 United Parcel Service of America, Inc. All Rights Reserved This data does fall in the interval because it is taken strictly from face(raw) value. *Assumption* - The January and February is wider than the rest because of more uncertainties. Coming from a very predictable holiday season. 0 50000 100000 150000 200000 250000 25 26 27 28 29 30 31 32 33 34 35 36 Holtz Perdiction interval 0 50000 100000 150000 200000 250000 25 26 27 28 29 30 31 32 33 34 35 36 Analyst Prediction interval This one is the analyst’s Data – Prediction doesn’t fall within the interval for data has been heavily influenced from outside variables.
- 7. 7 © Copyright 2016 United Parcel Service of America, Inc. All Rights Reserved Blue lines represents the raw data I extracted. Red is my forecast going through a series of tests. Grey is actual MIPS from 2016 Yellow is another analyst’s forecast. 0 50000 100000 150000 200000 250000 0 5 10 15 20 25 30 35 40 Beta Testing Forecast Actual MIPS forecast Actual 2016MIPS