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Fundamental & Economic Issues of AM/FM/GIS
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Life Cycle Approach to Managing an AM/FM/GIS Project
- Determining How Many Features to Inspect
The most thorough method for checking information accuracy is to check each
and every feature that is delivered - however, this method is cost prohibitive in
that each and every feature must be examined and justified by the inspector (a
technique similar to establishing a second, parallel conversion effort). Hence,
the challenge of implementing information accuracy procedures is to determine
how many features must be examined, and how many errors can be tolerated,
before an inspector can make a confident recommendation to accept or reject
the DU.
The ANSi/ASQC standards provide a means for identifying the quantity of
features that must be checked. This figure is derived from (1) the quantity of
total features for a DU, and (2) the current level of inspection intensity, as well
as the mandated level of data accuracy (set at the project level, usually as a
percentage, such as 95% or 99%).
- Quantity of Delivered Features
The quantity of features is a significant indicator as to how many features
will require examination before the inspector can make an informed
judgement about the overall quality of the DU. This quantity of inspection
features is not a direct percentage of the overall count of features in the
DU. Instead, the smaller the total count of features, the greater the actual
percentage of features that will be examined. The exact amount of
features that must be inspected in provided in a table in Arneticar?
iVationa/ Standard Sampling Procedures and Tables fbr Inspection by
Attributes, Z7.4-7993. This table also defines the amount of permissible
errors, thereby identifying how many detected errors would be required to
render the DU as unacceptable.
The number of sample [features] inspected shall be equal to the
sample size given by the plan. If the number of nonconforming
[features] found in the sample is equal to or less than the
acceptance number, the [DU] shall be considered acceptable. If the
number of nonconforming [features] is equal to or greater than the
rejection number, the [DU] shall be considered not acceptable.
(ANSI ZI.4 p.6, paragraph. 10.1.1, italics added)
The acceptance number k a prescribed value according to the ANSI ZI.4
sampling plans. As an example, should a DU contain 2,750 features,
under a normal sampling plan, 125 features would be selected from
throughout the entire DU and inspected. Given a 99?40acceptance
criteria, identifying three (3) erroneous features wmuld render a
recommendation to (nevertheless) accept the entire DU, while identifying
four (4) erroneous features would render a recommendation to reject the
entire DU.
The client’s ANSl/ASQC-based sampling plan should accept the great
majority of the DU’S that the conversion vendor submits, provided the
nonconformities per hundred units in these DU’S be no greater than one
(1) unit. This requirement establishes the AQL level as 1.0, and can also
be referred to as an “Acceptance Criteria of 99%”.
- Level of Intensity
The level of intensity refers to whether the DU is to be inspected via a
more relaxed or more stringent inspection criteria, identified according to
whether more or less features are to be examined. This so-called level of
intensity is a system for allowing an inspector to reduce inspection costs
for product that is consistently of highest quality, as well as allowing an
inspector to devote more resources to examine product that is
consistently below criteria.
In general, an example DU of 2,750 units having a 99°A acceptance
criteria, would render the following sampling plans, each according to
their respective inspection level:
- Normal: inspect 125 features, three (3) errors allowed.
- Relaxed: inspect 20 features, zero (0) errors allowed, one(1) error
allowed although normal inspection is reinstituted.
- Tiqhtened: inspect 200 features, three (3) errors allowed.
Prior to releasing a set of DU’S for inspection, the RA identifies the level
of intensity for each of the DU’S that are to be inspected, based on
inspection trends. ANSI ZI.4 sampling plans include controlled
provisions for changing the intensity of inspection activity throughout the
life of the QA/QC operation. These controlled provisions, based on the
performance trends of the current inspection queue, indicate when a plan
should be switched from normal to tightened and normal to relaxed, as
well as tightened to normal and relaxed to normal. The switching rules
are summarized, based on ANSI Z1.4 (pages 4 and 5), as follows (refer to
said document for authoritative details):
- From Normal to Tightened (poor trend): any two (2) out of five (5)
consecutive DU’S have been found to be non-acceptable, after being
inspected at a normal level of intensity.
- From Tightened to Normal (recovering trend): five (5) consecutive
DU’S have been found to be acceptable after being inspected at a
“tightened” level of inspection intensity.
- From Normal to Relaxed (excellent trend): four (4) conditions are
met, including (a) preceding ten (1 O) DU’S have been found to be
acceptable, (b) preceding ten (1 O) DU’S were identified as having a
significantly less quantity of errors than is allowed (per applicable
tables), (c) production is at a steady rate, and (d) relaxed inspection
is considered desirable by the RA.
- From Relaxed to Normal (normalizing trend): any one of four (4)
conditions are encountered while in relaxed level of inspection,
including (a) a DU is rejected, or (b) during inspection, the sampling
procedure may terminate without reaching a decision (DU passes,
although it “almost failed” per applicable tables), or (c) production
becomes irregular or delayed, or (d) other conditions warrant a
switch to normal inspection, as determined by the RA.
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Switching the inspection plan lends significant power to the RA in
enhancing their confidence in the acceptability of the overall project data
product, while taking advantage of opportunities to minimize inspection
labor at times when the data is judged to be of excellent quality.
Once the Inspection Level is assigned for a DU, that inspection level
should be maintained throughout the DU’S current inspection cycle.
- Identifying Sequence of Specific Features to be Inspected
Once the inspector knows the quantity of features that is to be inspected, they
must randomly select the features that are to be inspected. The random
targeting of features assures all parties that there is no bias toward obviously
correct features nor toward obviously defective features (although the
ANSi/ASQC standards allow for the client to mark obviously erroneous
features for subsequent repair by the data provider).
In order to be able to demonstrate complete objectivity in choosing the
particular features, it is suggested that objective procedures be used in
randomly targeting the features. Two objective procedures are as follows:
- Place a Cartesian coordinate grid over the DU map area, and by using
randomly generated numbers, derive the X,Y coordinates of the features
that are eligible for examination.
- Develop and use a software tool that is capable of randomly selecting a
specified quantity of features directly from the digital data file.
- Objectively Defining Errors
While this requirement is not derived from directly ANS1/ASQC standards, it is
important to be able to specify, objectively, what is wrong with any defective
features. In most industries, it is readily obvious how to describe an error with
a tangible product. However, with the product being AM/FM/GIS data features,
the nature of the information error is not as readily discernible. Hence, a
suggestion for rectifying this challenge is for each feature to be described
according to the three following error characteristics:
- Completeness: whether or not the feature is extra, or whether a feature is
missing from the conversion data.
- Layout/Position: refers explicitly to the position of features with respect to
other features.
- Accuracy: refers to whether or not a feature portrays correct information,
usually refers to an attribute of a feature such as a diameter, vintage,
status, etc.
Any feature that is inspected and found to be in error for any of these three
reasons should be counted as a single erroneous feature.
Conclusion
The techniques described herein illustrate many ways in which an AM/FM/GIS client
can establish an objective environment for discussing data quality issues with the
data provider. Adopting the techniques described in Arneficar? /Vafiona/ Standard
Sampling Procedures and Tables for Inspection by Attributes, Z1. 4-1993 lends
objective authority to the client in evaluating whether or not to accept contractually
delivered data product. Indeed, these standards include a mechanism whereby the
client can refuse any further deliveries until such time the data provider
demonstrates that it has taken corrective action in its data production operation.
Ironically, the greatest benefit of this type of a QA/QC environment is that these
techniques can also be applied by the
assure that the quality of their product
own business standards.
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