1. Quality management system sample
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I. Contents of quality management system sample
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MOTIVA has introduced a series of information videos on laboratory accreditation. As each is
developed, it is added to the list shown on this page. These videos are in Flash video format.
They are an excellent start point to learn about accreditation and how to prepare and implement
a laboratory quality system conformant to ISO/IEC 17025. As soon as the webinars have
provided someone with the learning in Best Practice they need, they may come back to this page
and download the documents and forms needed.
Sample Documents and Procedures
These documents are provided free of charge to anyone who wishes to create a quality manual
that conforms to ISO/IEC 17025. They may also be used for part of the creation of quality
manuals required by the other technical competence standards used by Inspection Bodies
(ISO/IEC 17020), Product Certification Bodies (ISO/IEC 17065), Proficiency Testing Providers
(ISO/IEC 17043), and Reference Material Producers (ISO Guide 34).
The manual and its associated procedures are based on the approach identified in MOTIVA
training coursesfor the creation of a conformant quality system for these types of technical
conformity assessment bodies (CABs). The approach is based on tried and true strategic
planning processes. In essence, the quality policy is expanded into "Key Result Areas" which
are the objectives at the beginning of each of ten chapters and the remainder of each chapter is
dedicated to the processes by which the laboratory meets the objective of that chapter.

2. Laboratories, Inspection Bodies and other types of CABs may make use of these approaches in
their own QMS by simply downloading the documents and implementing them directly. These
approaches represent best practice in CAB QMS implementation. These documents are
presented in pdf format for ease of downloading.
 MOTIVA Sample Quality Manual. This document forms the basis of the MOTIVA
Quality Manual Template Course and provides a straighforward approach that meets or
exceeds current requirements for quality system components. It includes a comprehensive
quality policy and a sample document structure. Note that it is not written in the clause
structure of any of the technical competence standards. There are other quality manual
templates offered on the web that are structured in that fashion.
 MOTIVA Sample Code of Ethics. This document details a code of ethics for
implementation within a laboratory that meets or exceeds the impartiality requirements,
including conflict of interest and confidentiality requirements of ISO/IEC 17025 and the
other technical competence standards.
 MOTIVA Sample Document Control and Control of Records Procedure. This
document details an comprehensive approach for the control of laboratory documents and
records. It is based primarily on the control of paper based documents and electronic
records. It meets or exceeds the requirements in ISO/IEC 17025 for these two disciplines as
detailed in clauses 4.3 and 4.13 as well as the associated clauses for the other technical
competence standards. Its companion MasterDocument List Form (described below) is
also available inPDF or EXCEL.
 MOTIVA Sample Continual Improvement Procedure. This document details a
comprehensive continual improvement approach for use in CABs that wish to meet or
exceed the requirements detailed in the applicable clauses of the technical competence
standard that governs their own operation. This approach allows for the identification of
non-conformances, potential-non conformances, and opportunities for improvement from
all of these source clauses and describes a congruent approach to addressing them as
prescribed more and more in later revisions of the technical completence standards. It
contains all the elements required for a CAB to track all non-conformances, potential non-
conformances and opportunities for improvment from identification through root cause
analysis (where this is actually required) to solution and follow up. Included are suggested
forms and root cause analysis techniques specifically aimed at supporting CAB operations.
Simple yet effective approach that is the basis of the MOTIVA Root Cause Course and
the other continual improvement disciplines. Meets or exceeds all the requirements of all
technical competence standards, including ISO/IEC 17025 clauses 4.9, 4.10, 4.11 and 4.12.
 MOTIVA Sample Feedback Procedure. This document contains a comprehensive
approach in acquiring and addressing feedback, both positive (compliments) and negative
(complaints) in a single procedure that includes sample forms that can be used by lab
staff from reception to determination of impact on operations. Simple yet effective
approach that is the basis of the MOTIVA training in this specific QMS discipline. Meets
all the requirements of ISO/IEC 17025 clauses 4.7 and 4.8 and the applicable clauses in the
other technical competence standards. Based, in part, on ISO/PAS 17003 - Conformity
assessment — Complaints and appeals — Principles and requirements.
 MOTIVA Sample Conflict of Interest and Confidendiality Guideline. This document
contains a comprehensive committment that has been referenced in the Sample Code of
Ethics regarding integrity. This guideline is separate from both the Sample Quality

3. Manual and theSample Code of Ethics, but is refered to in the Sample Code of Ethics
which allows for committment to both the Code and these Guidelines by staff signature.
This approach meets all the requirements of ISO/IEC 17025 clauses 4.1 and 4.2, and the
applicable clauses of the other technical competence standards regarding these issues.
Based, in part, on ISO/PAS 17001 - Conformity assessment — Impartiality — Principles
and requirements and ISO/PAS 17002 - Conformity assessment — Confidentiality —
Principles and requirements.
 MOTIVA Sample Internal Audit and Management Review Procedure. This document
forms the basis of the MOTIVA Internal Audit Course and provides a straighforward
approach that meets or exceeds the requirements provided in applicable clauses of all the
technical competence standards, ISO/IEC 17025. The procedure describes a process based
approach for internal auditing and contains reference to the forms needed for such activity.
 MOTIVA Sample Job Hazard Assessment Procedure. This document forms the basis of
the MOTIVA Job Hazard Assessment Course and provides a straighforward approach that
meets or exceeds current OHSA guidelines regarding the need to inform laboratory staff of
potential hazards and have them participate in appropriate solutions.
Sample Forms
Laboratories may make use of these forms in their own QMS by simply downloading them and
implementing them directly. These approaches represent best practice in laboratory QMS
implementation.

 Continual Improvement Form. This form tracks an identified departure from the quality
system (whether or not it has already occured) or a potential improvement, from
identification through assessment of impact, root cause analysis (where necessary),
development and implementation of solution, to follow up (again, where necessary). This
form is used in the MOTIVA Internal Audit Course. It is also available in Word
format here.
 Feedback Form - This form tracks both positive and negative feedback from any person or
organisation with respect to any aspect of the operation of the laboratory. It is also available
in Word format here.
 Internal Audit Forms. This set of forms allows for a laboratory to conduct an internal
audit conformant to ISO/IEC 17025, clause 4.14, using a process based approach. These
forms are used in the MOTIVA Internal Audit Course. It is also available in Word
format here.
 MasterDocument List. While this appraoch to controlling documents has been phased out
of the newest versions of standards such as ISO/IEC 17020 and 17065, it still lives in 17025
and it provides a very simple approach to controlling the documents within a management
system. If we understand that the entire aim of document control is to ensure that only the
most appropriate version of a document is available to everyone who may need it, then the
Master Document List approach described in 4.3.2 of ISO/IEC 17025 is a pretty
straightforward way to do that. No thinking required, just list them and track them. This
form allows a laboratory to list the status and location of all internally-generated documents
and all externally-generated documents, including the formats of laboratory forms. It also

4. allows the laboratory quality system personnel to record the authorities for creating and
authorizing documents, tracking review dates, and indicating when a document needs to be
reviewed again to determine its continued suitability in the laboratory management system.
It is also available in its Excel format here.
==================
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)
 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

5. 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.
3. Pareto chart

6. 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
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

7. 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
exactly.

8. 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

9. 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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