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Methods and Tools for Process Validation
Dr. Wayne A.
Taylor
ABSTRACT
There are many statistical tools that can be used as part of
validation. Control charts, capability studies, designed experiments, tolerance analysis,
robust design methods, failure modes and effects analysis, sampling plans, and mistake
proofing are but a few. Each of these tools will be summarized and their application in
validation described.
1. INTRODUCTION
Validation requires documented evidence that a process consistently
conforms to requirements. It requires that you first obtain a process that can
consistently conform to requirements and then that you run studies demonstrating that this
is the case. Statistical tools can aid in both tasks.
2. USES OF THE TOOLS
This section describes the many contributions that statistical tools
can make to validation. Each tool appearing in bold is further described in Section 4.
One tool that is particularly useful in organizing the overall
validation effort is a failure modes and effects analysis (FMEA) or a closely
related fault tree analysis (FTA). An FMEA involves listing out the potential
problems or failure modes and evaluating their risk in terms of their severity, likelihood
of occurring and ease of detection. Where potential risks exists, the FMEA can be used to
document which failure modes have been addressed and which still need to be addressed. As
each failure mode is addressed, the controls established are documented. The end result is
a control plan. Addressing the individual failure modes will require the use of many
different statistical tools.
Failures or nonconformities occur because of errors made and because of
excessive variation. Obtaining a process that consistently conforms to requirements
requires a balanced approach using both mistake proofing and variation reduction tools.
When a nonconformance occurs because of an error, mistake proofing methods should
be used. Mistake proofing attempts to make it impossible for the error to occur or at
least to go undetected.
However, many nonconformities are not the result of errors, instead
they are the result of excessive variation and offtarget processes. Reducing variation
and proper targeting of a process requires identifying the key input variables and
establishing controls on these inputs to ensure that the outputs conform to requirements.
Strategies and tools for reducing variation and optimizing the process average are
described in Section 3.
The end result is a control plan. The final phase of validation
requires demonstrating that this control plan works, i.e., that it results in a process
that can consistently conform to requirements. One key tool here is a capability study.
A capability study measures the ability of the process to consistently meet the
specifications. It is appropriate for measurable characteristics where nonconformities are
due to variation and offtarget conditions. Testing should be performed not only at
nominal, but also under worstcase conditions. When pass/fail data is involved, acceptance
sampling plans can be used to demonstrate conformance to specifications. Finally, in
the event of potential errors, challenge tests should be performed to demonstrate
that mistake proofing methods designed to detect or prevent such errors are working.
Depending of circumstances, not all tools need be used, other tools
could be used instead and the application of the tools can vary.
3. STRATEGIES AND TOOLS FOR REDUCING VARIATION AND OPTIMIZATION
Each unit of product differs to some small degree from all other units
of product. These differences, no matter how small, are referred to as variation.
Variation can be characterized by measuring a sample of the product and drawing a
histogram. For example, one operation involves cutting wire into 100 cm lengths. The
tolerance is
100 ± 5 cm. A sample of 12 wires is selected at random and the
following results obtained:
98.7 99.3
100.4 97.6 101.4
102.0
100.2 96.4 103.4
102.0 98.0 100.5
A histogram of this data follows. The width of the histogram represents
the variation.
Of special interest is whether the histogram is properly centered and
whether the histogram is narrow enough to easily fit within the specification limits. The
center of the histogram is estimated by calculating the average of the 12 readings. The
average is 99.99. The width of the histogram is estimated by calculating either the range
or standard deviation. The range of the above readings is 7.0 cm. The standard deviation
is 2.06 cm. The standard deviation represents the typical distance a unit is from the
average. Approximately half of the units are within ± 1
standard deviation of the average and about half of the units are more than one standard
deviation away from the average. On the other hand, the range represents an interval
containing all the units. The range is typically 3 to 6 times the standard deviation,
depending on the sample size.
Frequently, histograms take on a bellshaped appearance that is
referred to as the normal curve as shown below. For the normal curve, 99.73% of the units
fall within ± 3 standards deviation of the average.
For measurable characteristics like wire length, fill volume, and seal
strength, the goal is to optimize the average and reduce the variation. Optimization of
the average may mean to center the process as in the case of fill volumes, to maximize the
average as is the case with seal strengths, or to minimize the average as is the case with
harmful emissions. In all cases, variation reduction is also required to ensure all units
are within specifications. Reducing variation requires the achievement of stable and
capable processes. The figure below shows an unstable process. The process is constantly
changing. The average shifts up and down. The variation increases and decreases. The total
variation increases due to the shifting.
Instead, stable processes are desired as shown below. Stable processes
produce a consistent level of performance. The total variation is reduced. The process is
more predictable.
However, stability is not the only thing required. Once a consistent
performance has been achieved, the remaining variation must be made to safely fit within
the specification limits. Such a process is said to be stable and capable. Such a process
can be relied on to consistently produce good product.
A capability study is used to determine whether a process is
stable and capable. It involves collecting samples over a period of time. The average and
standard deviation of each time period is estimated and these estimates plotted in the
form of a control chart. These control charts are used to determine if the process is
stable. If it is, the data can be combined into a single histogram to determine its
capability. To help determine if the process is capable, several capability indices are
used to measure how well the histogram fits within the specification limits. One index,
called C_{p}, is used to evaluate the variation. Another index, C_{pk}, is
used to also evaluate the centering of the process. Together these two indices are used to
decide whether the process passes. The values required to pass depend on the severity of
the defect (major, minor, critical).
While capability studies evaluate the ability of a process to
consistently produce good product, it does little to help achieve such processes. Reducing
variation and the achievement of stable processes requires the use of numerous variation
reduction tools. Variation of the output is caused by variation of the inputs. Consider a
pump. An output is flow rate. Suppose the pump uses a piston to draw solution into a
chamber through one opening and then pushes it back out another opening. Valves are used
to keep the solution moving in the right direction. Flow rate will be affected by piston
radius, stroke length, motor speed and valve backflow to name a few. Flow rate varies
because piston radius, stroke length, etc. varies. Variation of the inputs is transmitted
to the output as shown below.
Reducing variation requires identifying the key input variables
affecting the outputs and then establishing controls on these inputs to ensure that the
outputs conform to their established specifications. In general, one must identify the key
input variables, understand the effect of these inputs on the output, understand how the
inputs behave and finally, use this information to establish targets (nominals) and
tolerances (windows) for the inputs. One type of designed experiment called a screening
experiment can be used to identify the key inputs. Another type of designed experiment
called a response surface study can be used to obtain a detailed understanding of
the effects of the key inputs on the outputs. Capability studies can be used to
understand the behavior of the key inputs. Armed with this knowledge, robust design
methods can be used to identify optimal targets for the inputs and tolerance
analysis can be used to establish operating windows or control schemes that ensure the
output consistently conforms to requirements.
The obvious approach to reducing variation is to tighten tolerances on
the inputs. This improves quality but generally drives up costs. The robust design methods
provide an alternative. Robust design works by selecting targets for the inputs that make
the outputs less sensitive (more robust) to the variation of the inputs as shown below.
The result is less variation and higher quality but without the added costs. Several
approaches to robust design exist including Taguchi methods, dual response
approach and robust tolerance analysis.
Another important tool is a control chart. A control chart can
be used to help determine whether any key input has been missed and if so to help identify
them. Many other tools also exist for identifying key inputs and sources of variation
including component swapping studies, multivari charts, analysis of
means (ANOM), variance components analysis, and analysis of variance
(ANOVA).
When studying variation, good measurements are required. Many times an
evaluation of the measurement system should be performed using a gage R&R or
similar study.
4. DESCRIPTIONS OF THE TOOLS
A brief description of each of the cited tools follows:
 Acceptance Sampling Plan – An acceptance sampling plan takes a sample of
product and uses this sample to make an accept or reject decision. Acceptance sampling
plans are commonly used in manufacturing to decide whether to accept (release) or to
reject (hold) lots of product. However, they can also be used during validation to accept
(pass) or to reject (fail) the process. Following the acceptance by a sampling plan, one
can make a confidence statement such as: "With 95% confidence, the defect rate is
below 1% defective."
 Analysis of Means (ANOM) – Statistical study for determining if significant
differences exist between cavities, instruments, etc. It has many uses including
determining if a measurement device is reproducible with respect to operators and
determining if differences exists between fill heads, etc. Simpler and more graphical
alternative to Analysis of Variance (ANOVA)
.
 Analysis of Variance (ANOVA) – Statistical study for determining if significant
differences exist between cavities, instruments, etc. Alternative to Analysis of Means
(ANOM).
 Capability Study – Capability studies are performed to evaluate the ability of
a process to consistently meet a specification. A capability study is performed by
selecting a small number of units periodically over time. Each period of time is called a
subgroup. For each subgroup, the average and range is calculated. The averages and ranges
are plotted over time using a control chart to determine if the process is stable or
consistent over time. If so, the samples are then combined to determine whether the
process is adequately centered and the variation is sufficiently small. This is
accomplished by calculating capability indexes. The most commonly used capability indices
are C_{p} and C_{pk}. If acceptable values are obtained, the process
consistently produces product that meets the specification limits. Capability studies are
frequently towards the end of the validation to demonstrate that the outputs consistently
meet the specifications. However, they can also be used to study the behavior of the
inputs in order to perform a tolerance analysis.
 Challenge Test – A challenge test is a test or check performed to demonstrate
that a feature or function is working. For example, to demonstrate that the power backup
is functioning, power could be cut to the process. To demonstrate that a sensor designed
to detect bubbles in a line works, bubbles could be purposely introduced.
 Component Swapping Study – Study to isolate the cause of a difference between
two units of product or two pieces of equipment. Requires the ability to disassemble units
and swap components in order to determine if the difference remains with original units or
goes with the swapped components.
 Control Chart – Control charts are used to detect changes in the process. A
sample, typically consisting of 5 units, is selected periodically. The average and range
of each sample is calculated and plot. The plot of the averages is used to determine if
the process average changes. The plot of the ranges is used to determine if the process
variation changes. To aid in determining if a change has occurred, control limits are
calculated and added to the plots. The control limits represent the maximum amount that
the average or range should vary if the process does not change. A point outside the
control limits indicates that the process has changed. When a change is identified by the
control chart, an investigation should be made as to the cause of the change. Control
charts help to identify key input variables causing the process to shift and aid in the
reduction of the variation. Control charts are also used as part of a capability study to
demonstrate that the process is stable or consistent.
 Designed Experiment – The term designed experiment is a general term that
encompasses screening experiments, response surface studies, and analysis of variance. In
general, a designed experiment involves purposely changing one or more inputs and
measuring the resulting effect on one or more outputs.
 Dual Response Approach to Robust Design – One of three approaches to robust
design. Involves running response surface studies to model the average and variation of
the outputs separately. The results are then used to select targets for the inputs that
minimize the variation while centering the average on the target. Requires that the
variation during the study be representative of long term manufacturing. Alternatives are
Taguchi methods and robust tolerance analysis.
 Failure Modes and Effects Analysis (FMEA) – An FMEA is systematic analysis of
the potential failure modes. It includes the identification of possible failure modes,
determination of the potential causes and consequences and an analysis of the associated
risk. It also includes a record of corrective actions or controls implemented resulting in
a detailed control plan. FMEAs can be performed on both the product and the process.
Typically an FMEA is performed at the component level, starting with potential failures
and then tracing up to the consequences. This is a bottom up approach. A variation is a
Fault Tree Analysis, which starts with possible consequences and traces down to the
potential causes. This is the top down approach. An FMEA tends to be more detailed and
better at identifying potential problems. However, a fault tree analysis can be performed
earlier in the design process before the design has been resolved down to individual
components.
 Fault Tree Analysis (FTA) – A variation of a FMEA. See FMEA for a comparison.
 Gauge R&R Study – Study for evaluating the precision and accuracy of a
measurement device and the reproducibility of the device with respect to operators.
Alternatives are to perform capability studies and analysis of means on measurement
device.
 Mistake Proofing Methods – Mistake proofing refers to the broad array of
methods used to either make the occurrence of a defect impossible or to ensure that the
defect does not pass undetected. The Japanese refer to mistake proofing as PokaYoke. The
general strategy is to first attempt to make it impossible for the defect to occur. For
example, to make it impossible for a part to be assembled backwards, make the ends of the
part different sizes or shapes so that the part only fits one way. If this is not
possible, attempt to ensure the defect is detected. This might involve mounting a bar
above a chute that will stop any parts that are too high from continuing down the line.
Other possibilities include mitigating the effect of a defect (seat belts in cars) and to
lessen the chance of human errors by implementing selfchecks.
 MultiVari Chart – Graphical procedure for isolating the largest source of
variation so that further efforts concentrate on that source.
 Response Surface Study – A response surface study is a special type of designed
experiment whose purpose is to model the relationship between the key input variables and
the outputs. Performing a response surface study involves running the process at different
settings for the inputs, called trials, and measuring the resulting outputs. An equation
can then be fit to the data to model the affects of the inputs on the outputs. This
equation can then be used to find optimal targets using robust design methods and to
establish targets or operating windows using a tolerance analysis. The number of trials
required by a response surface study increases exponentially with the number of inputs. It
is desirable to keep the number of inputs studied to a minimum. However, failure to
include a key input can compromise the results. To ensure that only the key input
variables are included in the study, a screening experiment is frequently performed first.
 Robust Design Methods – Robust design methods refers collectively to the
different methods of selecting optimal targets for the inputs. Generally, when one thinks
of reducing variation, tightening tolerances comes to mind. However, as demonstrated by
Taguchi, variation can also be reduced by the careful selection of targets. When nonlinear
relationships between the inputs and the outputs, one can select targets for the inputs
that make the outputs less sensitive to the inputs. The result is that while the inputs
continue to vary, less of this variation is transmitted to the output causing the output
to vary less. Reducing variation by adjusting targets is called robust design. In robust
design, the objective is to select targets for the inputs that result in ontarget
performance with minimum variation. Several methods of obtaining robust designs exist
including robust tolerance analysis, dual response approach and Taguchi methods.
 Robust Tolerance Analysis – One of three approaches to robust design. Involves
running a designed experiment to model the output’s average and then using the
statistical approach to tolerance analysis to predict the output’s variation.
Requires estimates of the amounts that the inputs will vary during longterm
manufacturing. Alternatives are Taguchi methods and the dual response approach.
 Screening Experiment – A screening experiment is a special type of designed
experiment whose primary purpose is to identify the key input variables. Screening
experiments are also referred to as fractional factorial experiments or Taguchi Larrays.
Performing a screening experiment involves running the process at different settings for
the inputs, called trials, and measuring the resulting outputs. From this, it can be
determined which inputs affect the outputs. Screening experiments typically require twice
as many trials as input variables. For example, 8 variables can be studied in 16 trials.
This makes it possible to study a large number of inputs in a reasonable amount of time.
Starting with a larger number of variables reduces the chances of missing an important
variable. Frequently a response surface study is performed following a screening
experiment to gain further understanding of the affects of the key input variables on the
outputs.
 Taguchi Methods – One of three approaches to robust design. Involves running a
designed experiment to get a rough understanding of the effects of the input targets on
the average and variation. The results are then used to select targets for the inputs that
minimize the variation while centering the average on the target. Similar to the dual
response approach except that while the study is being performed, the inputs are purposely
adjusted by small amounts to mimic longterm manufacturing variation. Alternatives are the
dual response approach and robust tolerance analysis.
 Tolerance Analysis – Using tolerance analysis, operating windows can be set for
the inputs that ensure the outputs will conform to requirements. Performing a tolerance
analysis requires an equation describing the effects of the inputs on the output. If such
an equation is not available, a response surface study can be performed to obtain one. To
help ensure manufacturability, tolerances for the inputs should initially be based on the
plants and suppliers ability to control them. Capability studies can be used to estimate
the ranges that the inputs currently vary over. If this does not result in an acceptable
range for the output, the tolerance of at least one input must be tightened. However,
tightening a tolerance beyond the current capability of the plant or supplier requires
that improvements be made or that a new plant or supplier selected. Before tightening any
tolerances, robust design methods should be considered.
 Variance Components Analysis – Statistical study used to estimate the relative
contributions of several sources of variation. For example, variation can on a multihead
filler could be the result of shifting of the process average over time, filling head
differences and shortterm variation within a fill head. A variance components analysis
can be used to estimate the amount of variation contributed by each source.
Written for
Global Harmonization Task Force (GHTF) Study
Group #3
Quality Management Systems  Process Validation Guidance – Edition 2 document.
Appears as Annex A of document.
Copyright © 1998 Taylor Enterprises, Inc.
