This document discusses best practices for supplier quality management. It outlines six key best practices: 1) Measuring and tracking the cost of poor supplier quality, 2) Implementing cost recovery from suppliers, 3) Conducting supplier audits, 4) Using supplier scorecards, 5) Implementing a closed-loop corrective action system, and 6) Engaging suppliers in quality systems. It also provides examples of common quality management tools like check sheets, control charts, Pareto charts, scatter plots, Ishikawa diagrams, and histograms. The document aims to help companies improve supplier quality and reduce costs.

1. Supplierquality management
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I. Contents of supplier quality management
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Supplier quality management has emerged as one of the leading business practices in the past
few years. World-class manufacturers are making significant investments in systems and
processes to improve supplier quality. This white paper briefly outlines some of the best
practices implemented by such manufacturers in supplier quality management.
Why Supplier Quality is critical?
With companies outsourcing their manufacturing to strategic partners across the globe, the
supply chains have become very long. Many consumer products are manufactured in Mexico or
the Far East and then shipped to North American markets using multiple logistics providers via
ocean, air and trucks. It can take weeks for a finished product to reach the store shelves from a
supplier in the Far East. In addition, many of these manufacturers have streamlined their supply
chain and implemented lean inventory techniques. As a result, any issue in supplier quality can
quickly result in stock outs.
Companies that sell industrial products need to preserve their preferred supplier status to
continue to be considered for future business. As a result, they are under pressure to ensure that
their products continue to meet or exceed acceptable PPM and Corrective Action thresholds set
by their customers. Hence, managing their own supplier’s quality is very high on the agenda for
these companies.
The following best practices enable these companies to improve their own quality by improving

2. their supplier’s product and delivery quality.
Best Practice #1: Measuring & tracking cost of poor supplier quality
Most organizations do not track and measure the cost of poor supplier quality (COPQ) attributed
to their suppliers. Such COPQ may add up to over 10% of the organization’s revenue. Some
companies only track supplier COPQ by measuring scrap and increase in MRB inventory.
Results have shown that materials account for less than 50% of the total COPQ. The following
should be taken into account to calculate the actual COPQ.
Scrap, rework, sorting and processing costs due to poor quality
MRB inventory and processing costs due to inspection failure
Line shutdown attributed to poor quality
Using equipment that is capacity constrained for rework due to poor quality, reducing the overall
utilization of the production line
Freight costs due to expedited shipment to customers/downstream plants
Warranty expenses due to poor quality
Recall expenses due to poor quality of products shipped to customers
Quality Management Systems (QMS) or manufacturing systems can track whenever any of the
above costs are incurred due to supplier quality issues. World-class manufacturers are using all
of the above factors to track actual supplier-related COPQ.
Best Practice #2: Cost recovery
As we discussed above, the total COPQ is equal to the COPQ of OEM plus inherited COPQ of
suppliers. As a result, companies need to proactively work with their suppliers to improve their
quality, so that they can reduce their own COPQ. Hence a cost-recovery system, where suppliers
are charged back for providing poor quality of components, is an effective way to introduce
business discipline and accountability into the supply chain.
However, based on our findings, less than 50% of companies pursue cost recovery with their
suppliers. And majority of these companies only recover material costs from their suppliers.
According to a recent report by AMR, an industry analyst group, about 65% of the costs
attributed to the poor supplier quality are non-material related - see an example in the picture
below. If a company institutes a quality management system to aggregate such costs and use it
for charge-backs, not only would they be able to fully recover the costs of poor quality from their
suppliers, they would be able to institute a discipline that forces the suppliers to quickly improve
their quality of products shipped.

3. Please click on image for enhanced version
Best Practice #3: Supplier Audit
Supplier Audits are one of the best ways to ensure that supplier is following the processes and
procedures that you agreed to during the selection processes. The supplier audit identifies non-
conformances in manufacturing process, shipment process, engineering change process,
invoicing process and quality process at the supplier. After the audit, the supplier and
manufacturer jointly identify corrective actions which must be implemented by the supplier
within an agreed-upon timeframe. A future audit ensures that these corrective actions have been
successfully implemented.
In our research, over 50% of the manufacturers do not follow the best practices in audit, while
engaging with their suppliers. By implementing best practices, manufacturers ensure that the
audit process is effective and efficient and allows them to audit their entire supplier base at least
once a year while maintaining a lean staff of auditors. The following picture shows the best
practices process for internal auditing.

4. Please click on image for enhanced version
Best Practice #4: Supplier Scorecard
Supplier Scorecards are one of the best techniques in using facts to rank the supplier’s relative
performance within the supply base and tracking improvement in supplier’s quality over time.
Scorecards also provide a data point into any future business negotiations. Following are the key
operational metrics that leading manufacturers track in their supplier scorecard:
PPM of Supplier Components
# of Corrective Actions Last Quarter
Average Response and Resolution time for Corrective actions
# RMAs Processed per month
MRB Inventory Levels
# of Rework Hours due to Supplier Components
% of Actual COPQ Recovered from Suppliers
# of Customer Complaints on Product Quality
Warranty Reserves
Relative ranking of supplier
Performance against benchmark
Best Practice #5: Closed Loop Corrective Action
Systematic reductions in the Cost of Poor quality can be attained by implementing a Quality
Management System (QMS) that provides an integrated and closed loop corrective action
process. In a manufacturing organization, when deviations, nonconformance, out of
specifications, quality incidents or customer complaints occur, corrective and preventive actions
need to be initiated to remedy the problems.
Once a quality problem has been identified, the first step is to initiate an investigation and to
properly identify the root cause of the problem. After the root cause has been identified,
Corrective Action (CAPA) items are created and routed for approval. When approved,

5. appropriate changes are implemented in the environment and then the CAPA is closed out. These
changes may include amendments to a documented procedure, upgrading the skill set of an
employee through a training and certification process, or recalibrating the manufacturing
equipment. In addition, the system may capture COPQ associated with that non-conformance
and use that information to initiate and complete a cost recovery process with a supplier.
It is critical to deploy a closed-loop, integrated quality management system, rather than a set of
loosely connected modules from one or more vendors. Integration ensures that the information
flows out the corrective action process with a high degree of accuracy and velocity without
falling through the cracks. It also ensures that the entire change control process is auditable from
end-to-end - a critical requirement to support 21CFR Part 11 requirement in FDA regulated
industries. Finally an integrated system ensures that audits become a core driver into the
corrective action process and become a key tool for continuous improvement.
Please click on image for enhanced version
Best Practice #6: Engaging Suppliers in quality systems
It is critical for manufacturers to engage suppliers in all aspects of their quality management
system, so that the supply-base is fully integrated into the QMS being rolled out. Key
requirements include:
Supplier should be able to provide quality-related data to the manufacturer without having to
deploy a mandated quality management system within their environment. This can be achieved
by feeding information from supplier’s quality system into manufacturer’s quality system (for
larger suppliers or ones sharing their production line with multiple customers) or getting the

6. supplier to use a manufacturer’s web-based quality management system (for smaller suppliers or
ones with dedicated lines for a customer). A web-based quality management system dramatically
reduces the cost of ownership for a supplier by providing the right information to a key customer
without having to deploy software in-house.
Manufacturer should be able to get every relevant stakeholder within the supply base to use the
quality system without having to train every casual user. Emerging capability includes a scenario
where an application form is embedded within an email delivered by the system to the casual
user at a supplier. When the user opens an email, they hit reply, enter the data in the embedded
form and hit send. The data in the form is processed by the system as if it came from the screen.
As a result the user does not need to learn to navigate the quality application, yet can participate
in the quality system.
By deploying these best practices, manufacturers can dramatically improve their supplier quality
and achieve their own business objectives. Such practices have been implemented by world-class
manufacturers using enterprise quality management software. We invite you to take a look at
MetricStream’s software suite and see how our solution can help you deploy such practices
within your environment.
==================
III. Quality management tools
1. Check sheet
The check sheet is a form (document) used to collect data
in real time at the location where the data is generated.
The data it captures can be quantitative or qualitative.
When the information is quantitative, the check sheet is
sometimes called a tally sheet.
The defining characteristic of a check sheet is that data
are recorded by making marks ("checks") on it. A typical
check sheet is divided into regions, and marks made in
different regions have different significance. Data are
read by observing the location and number of marks on
the sheet.
Check sheets typically employ a heading that answers the
Five Ws:
 Who filled out the check sheet
 What was collected (what each check represents,
an identifying batch or lot number)

7.  Where the collection took place (facility, room,
apparatus)
 When the collection took place (hour, shift, day
of the week)
 Why the data were collected
2. Control chart
Control charts, also known as Shewhart charts
(after Walter A. Shewhart) or process-behavior
charts, in statistical process control are tools used
to determine if a manufacturing or business
process is in a state of statistical control.
If analysis of the control chart indicates that the
process is currently under control (i.e., is stable,
with variation only coming from sources common
to the process), then no corrections or changes to
process control parameters are needed or desired.
In addition, data from the process can be used to
predict the future performance of the process. If
the chart indicates that the monitored process is
not in control, analysis of the chart can help
determine the sources of variation, as this will
result in degraded process performance.[1] A
process that is stable but operating outside of
desired (specification) limits (e.g., scrap rates
may be in statistical control but above desired
limits) needs to be improved through a deliberate
effort to understand the causes of current
performance and fundamentally improve the
process.
The control chart is one of the seven basic tools of
quality control.[3] Typically control charts are
used for time-series data, though they can be used
for data that have logical comparability (i.e. you
want to compare samples that were taken all at
the same time, or the performance of different
individuals), however the type of chart used to do
this requires consideration.

8. 3. Pareto chart
A Pareto chart, named after Vilfredo Pareto, is a type
of chart that contains both bars and a line graph, where
individual values are represented in descending order
by bars, and the cumulative total is represented by the
line.
The left vertical axis is the frequency of occurrence,
but it can alternatively represent cost or another
important unit of measure. The right vertical axis is
the cumulative percentage of the total number of
occurrences, total cost, or total of the particular unit of
measure. Because the reasons are in decreasing order,
the cumulative function is a concave function. To take
the example above, in order to lower the amount of
late arrivals by 78%, it is sufficient to solve the first
three issues.
The purpose of the Pareto chart is to highlight the
most important among a (typically large) set of
factors. In quality control, it often represents the most
common sources of defects, the highest occurring type
of defect, or the most frequent reasons for customer
complaints, and so on. Wilkinson (2006) devised an
algorithm for producing statistically based acceptance
limits (similar to confidence intervals) for each bar in
the Pareto chart.
4. Scatter plot Method

9. A scatter plot, scatterplot, or scattergraph is a type of
mathematical diagram using Cartesian coordinates to
display values for two variables for a set of data.
The data is displayed as a collection of points, each
having the value of one variable determining the position
on the horizontal axis and the value of the other variable
determining the position on the vertical axis.[2] This kind
of plot is also called a scatter chart, scattergram, scatter
diagram,[3] or scatter graph.
A scatter plot is used when a variable exists that is under
the control of the experimenter. If a parameter exists that
is systematically incremented and/or decremented by the
other, it is called the control parameter or independent
variable and is customarily plotted along the horizontal
axis. The measured or dependent variable is customarily
plotted along the vertical axis. If no dependent variable
exists, either type of variable can be plotted on either axis
and a scatter plot will illustrate only the degree of
correlation (not causation) between two variables.
A scatter plot can suggest various kinds of correlations
between variables with a certain confidence interval. For
example, weight and height, weight would be on x axis
and height would be on the y axis. Correlations may be
positive (rising), negative (falling), or null (uncorrelated).
If the pattern of dots slopes from lower left to upper right,
it suggests a positive correlation between the variables
being studied. If the pattern of dots slopes from upper left
to lower right, it suggests a negative correlation. A line of
best fit (alternatively called 'trendline') can be drawn in
order to study the correlation between the variables. An
equation for the correlation between the variables can be
determined by established best-fit procedures. For a linear
correlation, the best-fit procedure is known as linear
regression and is guaranteed to generate a correct solution
in a finite time. No universal best-fit procedure is
guaranteed to generate a correct solution for arbitrary
relationships. A scatter plot is also very useful when we
wish to see how two comparable data sets agree with each
other. In this case, an identity line, i.e., a y=x line, or an
1:1 line, is often drawn as a reference. The more the two
data sets agree, the more the scatters tend to concentrate in
the vicinity of the identity line; if the two data sets are
numerically identical, the scatters fall on the identity line

10. exactly.
5.Ishikawa diagram
Ishikawa diagrams (also called fishbone diagrams,
herringbone diagrams, cause-and-effect diagrams, or
Fishikawa) are causal diagrams created by Kaoru
Ishikawa (1968) that show the causes of a specific
event.[1][2] Common uses of the Ishikawa diagram are
product design and quality defect prevention, to identify
potential factors causing an overall effect. Each cause or
reason for imperfection is a source of variation. Causes
are usually grouped into major categories to identify these
sources of variation. The categories typically include
 People: Anyone involved with the process
 Methods: How the process is performed and the
specific requirements for doing it, such as policies,
procedures, rules, regulations and laws
 Machines: Any equipment, computers, tools, etc.
required to accomplish the job
 Materials: Raw materials, parts, pens, paper, etc.
used to produce the final product
 Measurements: Data generated from the process
that are used to evaluate its quality
 Environment: The conditions, such as location,
time, temperature, and culture in which the process
operates
6. Histogram method

11. A histogram is a graphical representation of the
distribution of data. It is an estimate of the probability
distribution of a continuous variable (quantitative
variable) and was first introduced by Karl Pearson.[1] To
construct a histogram, the first step is to "bin" the range of
values -- that is, divide the entire range of values into a
series of small intervals -- and then count how many
values fall into each interval. A rectangle is drawn with
height proportional to the count and width equal to the bin
size, so that rectangles abut each other. A histogram may
also be normalized displaying relative frequencies. It then
shows the proportion of cases that fall into each of several
categories, with the sum of the heights equaling 1. The
bins are usually specified as consecutive, non-overlapping
intervals of a variable. The bins (intervals) must be
adjacent, and usually equal size.[2] The rectangles of a
histogram are drawn so that they touch each other to
indicate that the original variable is continuous.[3]
III. Other topics related to Supplier quality management (pdf download)
quality management systems
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iso 9001 quality management system
quality management process
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quality system management
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quality management books