Operations Management Dr. Ron Lembke FORECASTING Operations Management Dr. Ron Lembke

New Housing Starts 1959-2015 Who cares? How predict?

Naïve Forecast Error Avg Error (14 mos)= -0.4 Avg Error (55 yrs)= 0.02 Month Total Naïve Error Jan 1959 96.2 Feb 1959 99.0 2.8 Mar 1959 127.7 28.7 Apr 1959 150.8 23.1 May 1959 152.5 1.7 Jun 1959 147.8 -4.7 Jul 1959 148.1 0.3 Aug 1959 138.2 -9.9 Sep 1959 136.4 -1.8 Oct 1959 120.0 -16.4 Nov 1959 104.7 -15.3 Dec 1959 95.6 -9.1 Jan 1960 86.0 -9.6 Feb 1960 90.7 4.7 Avg Error (14 mos)= -0.4 Avg Error (55 yrs)= 0.02

Mean Error of 0 That’s good! not perfect. Just unbiased

Squared Errors No negatives No cancelling Large errors blow up One bad month and your method looks terrible MSE=303.25 Good? Bad?

Mean Absolute Error No negatives No cancelling Large errors do not blow up What’s a good score? MAD=12.99 Good? Bad?

Mean Absolute Percentage Error No negatives No cancelling Large errors do not blow up What’s a good score? MAPE=11.4% Good? Bad?

 =0.3

 =0.5 Tracking Signal

TAF a=0.2, beta=0.5

Scenario 3a R2=0.000096

Deseasonalized Van Usage R2 = 700 TIMES better!

New Housing Deseasonalized Avg SF 87.14 0.723 90.18 0.749 120.39 0.999 136.78 1.135 142.19 1.180 141.01 1.171 135.07 1.121 133.71 1.110 126.56 1.051 130.88 1.086 109.19 0.906 92.52 0.768 120.47 1.00

TAF with Seasonality

Housing-Selecting Data A>F A<F

1. Seasonal Factors 2009 2010 2011 2012 2013 2014 2015 Avg SF Jan 31.9 38.9 40.2 47.2 58.7 60.7 73.0 50.1 0.776 Feb 39.8 40.7 35.4 49.7 66.1 65.1 61.9 51.2 0.794 Mar 42.7 54.7 49.9 58.0 83.3 80.2 79.7 64.1 0.993 Apr 42.5 62.0 49.0 66.8 76.3 94.9 108.5 71.4 1.107 May 52.2 56.2 54.0 67.8 87.2 92.5 99.6 72.8 1.128 June 59.1 53.8 60.5 74.7 80.7 87.3 112.7 75.5 1.171 July 56.8 51.5 57.6 69.2 84.0 101.0 112.3 76.1 1.179 Aug 52.9 56.3 54.5 69.0 80.4 86.2 66.6 1.032 Sept 52.6 53.0 58.8 75.8 78.4 94.2 68.8 1.066 Oct 44.5 45.4 53.2 77.0 92.0 1.009 Nov 42.3 40.6 62.2 83.8 59.6 0.924 Dec 36.6 33.8 63.2 67.6 73.4 0.820 64.51

2. Deseasonalize Month Starts SF Deseas Jan 2009 31.9 0.776 41.1 Feb 2009 39.8 0.794 50.1 Mar 2009 42.7 0.993 43.0 Apr 2009 42.5 1.107 38.4 May 2009 52.2 1.128 46.3 Jun 2009 59.1 1.171 50.5 Jul 2009 56.8 1.179 48.2 Aug 2009 52.9 1.032 51.3 Sep 2009 52.6 1.066 49.3 Oct 2009 44.5 1.009 44.1 Nov 2009 42.3 0.924 45.8 Dec 2009 36.6 0.820 44.6

3. Linear Regression

4. Project Forward F(t) = 36.98 + t*0.69

5. Seasonalize

Summary Calculate seasonal relatives Deseasonalize Do a LR Divide actual demands by seasonal relatives Do a LR Project the LR into the future Seasonalize Multiply straight-line forecast by relatives