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Summary Team 2 Wk 2 Airline Case Study update 6.docx OPS/420 Airline Delay Case Study University of Phoenix OPS/420 Airline Delay Case Study The scenario provided in this case study, is to identify high traffic areas within the metropolitan areas, to provid$7.49
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Summary Team 2 Wk 2 Airline Case Study update 6.docx OPS/420 Airline Delay Case Study University of Phoenix OPS/420 Airline Delay Case Study The scenario provided in this case study, is to identify high traffic areas within the metropolitan areas, to provid
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Team 2 Wk 2 Airline Case Study update OPS/420 Airline Delay Case Study University of Phoenix OPS/420 Airline Delay Case Study The scenario provided in this case study, is to identify high traffic areas within the metropolitan areas, to provide a service targeting wealthy business travelers. ...
team 2 wk 2 airline case study update 6docx ops420 airline delay case study university of phoenix ops420 airline delay case study the scenario provided in this case study
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OPS/420
Airline Delay Case Study
University of Phoenix
OPS/420
Airline Delay Case Study
The scenario provided in this case study, is to identify high traffic areas within the
metropolitan areas, to provide a service targeting wealthy business travelers. The Airport that
was chosen for this study is Newark Airport in New Jersey. Six airlines in the study include
Delta, United, Southwest, Spirit, Jet Blue, and American. A performance evaluation with each
of the airlines from best to worst at Newark Airport, NJ was made with the data provided. This
will show how with different methods and proper guidance a transportation service can become
successful.
Of these four chart types, which are the most appropriate given the data? Defend your
choice.
Identification of area with issues within each airline can be identified with Statistical
Process Control. Statistical Process Control or SPC’s inspects the outputs of a given process and
deciding whether the area falls between a predetermined range (Reid & Sanders, 2016). Controls
Charts are considered methods of SPC to identify the number of variations within processes that
can determine what is considered acceptable within a predetermined range. For this Case study,
the use of P-Charts, C-Charts, X-Bar Charts, and Range Charts, determined upper and lower
limits to identify areas of variation, and areas needing to be controlled. Use of these charts
allowed for identification of an acceptable variation.
Data collected from the US Department of Transportation showed a comparison of each
airline’s arrival delays, and causes of delays. This data allowed for Control charts to become
, separated into two separate groups, variables, and attributes. Variable control charts include X-
Bar charts, and R-Charts. Attribute control charts include P-Charts and C-Charts. Using data of
airline delays, a x-bar chart was developed, which allowed for an identification of a centerline or
mean, along with the Upper control Limit (UCL) and Lower control Limit (LCL).
X-Bar Charts help in determining what areas have issues that need to be addressed above
or below the provided control lines and provide a starting point to find solutions to the situation.
Range charts or R-charts monitors the dispersion of variability within the process (Reid &
Sanders, 2016). X-Bar Charts and R-Charts are both important variables. These charts used
together should monitor both the mean and the range. P-charts and C-charts are considered
attribute charts. P-charts used to measure the proportion that is defective within a sample (Reid
& Sanders, 2016). C-charts are used to monitor the number of defects per unit (Reid & Sanders,
2016).
For the best result with identifying high delay metropolitan areas, the recommendation
would be to use attribute control charts, more specifically the P-chart. P-chart information from
observations can be placed in two groups. Reviewing the multiple charts which compared the
major airlines, a determination was to which of the airlines are preferred from a quality
standpoint. Considerations were made based on how level the chart becomes as the sample data
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