This document discusses vendor quality management. It provides an overview of common quality issues attributed to vendors in the biopharmaceutical industry, including poor change control, quality problems, and unmet promises. The document also shares quality management tools that can help address vendor issues, such as check sheets, control charts, Pareto charts, scatter plots, Ishikawa diagrams, and histograms. Additional related topics on vendor quality management are listed for further reference.

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I. Contents of vendor quality management
==================
Biopharmaceutical manufacturing is one of the most demanding industries on its suppliers:
demanding that its vendors be prequalified as primary or secondary, requiring confirmations of
product provenance, certificates of analysis, and other sometimes onerous documentation. All of
this is done for drug product quality and consistency. So it comes as no surprise that quality
managers are continuing to take a close look at their vendors. Our 11th annual biopharmaceutical
manufacturing industry report indicates that companies are vetting suppliers more closely than
ever, demanding even higher levels of GMP/ GLP compliance (see: "Who's Improving
Bioprocessing in 2014?" in our January 2014 issue). In our current industry study — 11th
Annual Report and Survey of Biopharmaceutical Manufacturers — we review quality problems
attributed to vendors, finding that half of our biopharmaceutical company participants complain
of a variety of problems initiated by their vendors, from change notification problems, where
they didn't notify clients of changes, to regulatory inexperience.
The most common complaint in this highly regulated environment relates to suppliers not
informing customers of changes, with half of the respondents frustrated by the problems
suppliers create in this regard. In addition, 43 percent of respondents note that, overall, vendor
change control is poor.8 IN 10 SUPPLIERS HAVE CREATED QUALITY PROBLEMS
Results from the study indicate that the vast majority of industry decision makers attribute at
least some quality problems to their vendors. Overall, only 18 percent of respondents said that
vendors have not created quality problems for them and that they are generally satisfied with
their vendors in this regard.

2. The high rate of problems due to poor vendor change control and poor product quality are
troublesome because these factors potentially lead to batch failures and/or regulatory compliance
issues for the biopharmaceutical manufacturer. It is, therefore, critical for manufacturers to
develop stronger relationships with their vendors and to maintain quality agreements with
specific requirements.
Clearly, change control is a quality problem plaguing the industry, but there are other issues, too
(see figure 1). Other complaints cited by at least one-quarter of the industry include include poor
service quality and poor product quality.
SIGNS OF VENDOR IMPROVEMENT
There are reasons to believe that vendors are improving, though. For example, the 18 percent of
respondents this year saying they have not experienced any quality problems traced to vendors is
a step up from nearly 16 percent last year. And on some of the quality issues identified, fewer
participants are seeing problems this year.
That's particularly the case when it comes to the inadequacy of certificates of analysis for
products. This year, fewer than one in five said that vendors had created problems for them in
this area, down from roughly one-quarter of participants in the prior three years of surveys and
roughly one-third of respondents in the three years prior to that.
Also this year, just 7 percent complained of vendors not filing a Device Master File on their
product. That marks a new low for this issue, which has remained above 10 percent during most
of the previous six years of surveys.
SERVICE PROBLEMS NOW OUTWEIGH QUALITY ISSUES
Interestingly, issues of product and service quality are moving in opposite directions this year. In
a reversal of trends we observed in recent years, our latest report shows that vendors may be
getting better at producing their products and services, but getting worse in delivering their
products and services.
This year, 28 percent of respondents identified poor product quality as causing problems, down
from 39 percent last year. In fact, this year's results are a sizable improvement from years past, in
which as many as 45 percent of participants had cited product quality problems.
Instead, for the first time, we found more respondents complaining of poor quality service (29
percent) than poor quality products (28 percent). The uptick in service complaints came from
about one-quarter of participants noting service quality problems in each of the past two years.
VENDORS STILL OVERPROMISE
Over a third (38 percent) of biomanufacturers are concerned that vendors continue to make
promises they can't keep. And while this problem isn't unique to the biopharma industry, it
becomes far more critical because the FDA and EMA are involved. Biopharma companies
realize that their reputations and even their existence could be undone if suppliers don't provide
what they say. This figure has hovered around this level each year since 2008. So the problem
seems to be somewhat ingrained.

3. When vendors — particularly sales reps — make promises they or their companies cannot keep
(and/or provide defective or inadequate products), it can be presumed that their customers will
seek out other vendors with better, more documented products and better followthrough.
However, qualifying a new vendor can be arduous, and most companies prefer to avoid
switching.
To some extent, when vendors don't meet their promises, it may be due to vendor-customer
communication problems and customers not "hearing" negative information concerning their
purchase. Also, end users may not be asking the right questions or requesting all the available
documentation regarding their bioprocessing equipment, materials, and supply chain — and
vendors may not be routinely providing this info.
Also, end users may not be asking the right questions or requesting all the documentation
available regarding bioprocessing equipment and their materials manufacturing and supply
chain, and/or vendors are not providing it.
QUALITY AGREEMENTS ARE CRITICAL
One of the challenges faced by biopharmaceutical manufacturers is that, due to regulatory
demands, it is often difficult to change vendors for key materials. For this reason, manufacturers
must continue relationships with vendors even when there is a high degree of dissatisfaction with
various aspects of the material or service provided.
In order for manufacturers to build confidence in their relationships, they must have solid quality
agreements with suppliers and perform regular audits to be certain that both parties have a clear
understanding of exactly what is expected. One of the biggest issues with vendors is managing
their own unanticipated manufacturing changes. Vendors' raw materials suppliers may not be
communicating up the chain in a timely way to either the manufacturer or the equipment users.
In some cases, vendors do not realize that a change has been made by their supplier until long
after the fact. This creates problems that some attribute to lack of communication. Solid quality
agreements can help reduce these problems for client companies and build confidence into their
relationships.
==================
III. Quality management tools
1. Check sheet

4. 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)
 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

5. 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.
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

6. algorithm for producing statistically based acceptance
limits (similar to confidence intervals) for each bar in
the Pareto chart.
4. Scatter plot Method
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

7. 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
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

8. 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]
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