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

2. New Housing Starts
Who cares?
How predict?

3. 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

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

5.  =0.3

6.  =0.5 Tracking Signal

7. TAF a=0.2, beta=0.5

8. Scenario 3a
R2=

9. Deseasonalized Van Usage

10. 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

11. TAF with Seasonality

12. Housing-Selecting Data
A>F
A<F

13. 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

14. 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

15. 3. Linear Regression

16. 4. Project Forward
F(t) = t*0.69

17. 5. Seasonalize

18. 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