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Data
Analysis / Data Mining
As
an inherently
technical-based industry, the vehicle inspection business involves the
collection and analysis of a tremendous amount of data. All motor vehicles that
are tested must meet quantitative inspection standards in order to pass their
test. This in turn leads to a number of critical data collection and analysis
issues, including:
·
Proper
identification of test vehicles so that they can be properly tested and the
correct pass / fail standards can be applied (different types of vehicles are
subject to different tests and standards).
·
Proper application
of test procedures and standards to the identified vehicle.
·
Proper test
decisions (pass or fail) depending on the test results for the vehicle.
·
Proper recording
of the test results and transmittal to the appropriate systems (e.g., to the
state vehicle registration database so that the owner is allowed to register a
passing vehicle).
·
Proper calibration
and auditing of test equipment performance to verify it continues to operate
within allowable specifications.
·
Proper oversight
and auditing of inspector performance to ensure they continue to correctly test
vehicles.
·
Proper reporting
of test result summaries and other program data to the oversight agency and the
U.S. EPA.
Given
its position as the vehicle inspection quality leader, Gordon-Darby performs the
above activities better than any of our competitors. Achieving this level of
performance has required the company to develop and implement systems and
processes that incorporate all of these functions. Part of these processes has
been the development of analysis techniques, and subsequent ongoing analysis of
many millions of vehicle test records and other data. While much of this effort
is specific to the vehicle inspection industry, a number of elements have
applicability to other business sectors:
·
Quality control
and error checking: Gordon-Darby personnel are expert at developing and
applying advanced automated data QC and error checking routines. The purpose of
these routines are to ensure all collected data comply with allowable
specifications, are free from unwanted biases, and do not reflect anomalous
equipment or inspector performance. Data QC is a standard required element of
all analysis activities regardless of the business sector.
·
Trends analysis:
An important element of Gordon-Darby analysis efforts include trends analyses
conducted on both test equipment calibration and other QC data, and vehicle test
results. Regarding the former, statistical process control (SPC) and other
types of analyses are routinely conducting on collected QC data to verify that
the test equipment is operated within specification and vehicles are being
properly tested. In addition, vehicle test data are analyzed to determine both
the effectiveness of the inspection program over time in reducing emissions and
trends in vehicle emissions independent of the program.
Our expertise in SPC analysis is applicable to a wide range of manufacturing
processes, while additional Gordon-Darby trends analysis capabilities can be
applied to any datasets involving information collected over an ongoing period
of time or from a disparate group of individuals.
Pattern recognition and
exceptions analysis: Gordon-Darby’s triggers analysis procedures largely
involve comparing the performance of individual test systems or inspectors to
the performance of each group as a whole. By identifying statistical outliers
or other anomalous results in the data, the software algorithms can quickly hone
in on patterns of questionable performance (human or equipment) and identify
exceptions to normal/acceptable behavior. By automating these algorithms and
the results they generate, it is also possible to sort through a tremendous
amount of data using relatively few human resources, who can instead be focused
on following up on identified anomalies in the trigger results.
These trigger principles and procedures have relevant applications in other
business sectors such as homeland security and those involving general fraud
prevention and equipment performance tracking.
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