Erickson-Ch6 Case Studies-jan10

Erickson-Ch6 Case Studies-jan10 - April Erickson Case...

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April Erickson Case Study: Alabama Airlines 25-Jan-10 95% confidence (2 standard deviation) 100 flights Sample (week) Late Flights Percent of Defects UCL LCL UCL 1 2 0.02 0.08 0 0.1000 2 4 0.04 0.08 0 0.1000 3 10 0.1 0.08 0 0.1000 4 4 0.04 0.08 0 0.1000 5 1 0.01 0.08 0 0.1000 6 1 0.01 0.08 0 0.1000 7 13 0.13 0.08 0 0.1000 8 9 0.09 0.08 0 0.1000 9 11 0.11 0.08 0 0.1000 10 0 0 0.08 0 0.1000 11 3 0.03 0.08 0 0.1000 12 4 0.04 0.08 0 0.1000 13 2 0.02 0.08 0 0.1000 14 2 0.02 0.08 0 0.1000 15 8 0.08 0.08 0 0.1000 16 2 0.02 0.08 0 0.1000 17 3 0.03 0.08 0 0.1000 18 7 0.07 0.08 0 0.1000 19 3 0.03 0.08 0 0.1000 20 2 0.02 0.08 0 0.1000 21 3 0.03 0.08 0 0.1000 22 7 0.07 0.08 0 0.1000 23 4 0.04 0.08 0 0.1000 24 3 0.03 0.08 0 0.1000 25 2 0.02 0.08 0 0.1000 26 2 0.02 0.08 0 0.1000 27 0 0 0.08 0 0.1000 28 1 0.01 0.08 0 0.1000 29 3 0.03 0.08 0 0.1000 30 4 0.04 0.08 0 0.1000 1.2 0.04 p-chart UCL = p bar + 2 sqrt ((p bar (1- p bar)/n)) 0.08 LCL = p bar - 2 sqrt ((p bar (1- p bar)/n)) 0 0.04 + 2* ((0.04*0.96)/100)) 0.04 - 2* ((0.04*0.96)/100))
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3) Using a 95% confidence rate, a majority (approximately 85-90%) of flights sampled are on time based on Alaba definition because the control limits create a large area. However, using airline industry's control limits which hav range, Alabama Airlines has a lot of work to do!! By using the airline industries limits, there are more flights outsid than within the control limits. When a flight falls outside of the range, it should be examined what the issue/reaso delay. Was it weather and couldn't be controlled? Was it schedule related? If so, the airline might need to work airports for different arrival and departure times so that there isn't scheduling conflicts amongst the airlines. They consider if it is mechanical issues with the plane. If it is, they may need to consider looking at buying new planes
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Erickson-Ch6 Case Studies-jan10 - April Erickson Case...

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