The document discusses product quality management systems. It provides an overview of quality management tools and strategies that can be used, including check sheets, control charts, Pareto charts, scatter plots, Ishikawa diagrams, histograms, and more. It also discusses quality management challenges and solutions for different consumer product industries like food and beverage, personal care, tobacco, and how an enterprise quality management software system can help automate processes and ensure compliance.

1. Product qualitymanagement system
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I. Contents of product quality management system
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Companies operating in the consumer products industry are playing in one of the most highly
competitive industries today. Constantly changing consumer tastes and preferences, pending
product market regulation, new packaging technologies, new formulations, expanding
geographical markets and the ever present need to achieve profitable growth are all top of mind
for executives. Although successfully positioning your brand in front of consumers is an
important first step, the ensuing relationship with your product is paramount to success. For this
reason, maintaining a long-term trusted status with customers is driven by product quality. With
an enormous amount of choices available to consumers in almost every market globally, if their
needs are not met, then companies are quickly overlooked and fall behind.
Quality is Critical to Compete
Regardless of the goods sold, Food and Beverage (F&B), Personal Care & Cosmetics, or other
consumer packaged goods, these organizations all face similar issues with managing compliance,
quality, operating margins, traceability, risk, changing demand and supply-chain visibility.
Leading companies in the consumer products industry are some of the largest companies in the
world. Over the past ten years there has been significant consolidation at the top of the market,
with the remaining companies looking for additional growth by expanding into new product
segments and geographies. For these large consumer products companies, a consumer

2. goods quality management system is not only necessary for seamless global operations, it is
becoming mandatory business application for offering high quality products at low costs.
Food & Beverage (F&B)
Food and beverage companies are faced with the difficulties of balancing rigorous food safety
standards and compliance processes – including the Food Safety Modernization Act (FSMA) and
Hazard Analysis and Critical Control Points (HACCP) – while striving for cost efficiencies in
production to remain competitive. With product recalls becoming more common and highly
publicized, brand protection is more important than ever before. Leading F&B companies that
adopt food safety and quality solutions, such as an Enterprise Quality Management Software
(EQMS) system, are able to increase the effectiveness of operational excellence initiatives
through improved supplier quality performance as well as improved internal process
performance that can support current GMP, ISO and HACCP requirements. These improvements
help reduce costs while also assist in creating a quality-centric culture and a compliance platform
that will support the organization with impending food safety regulations.
Personal Care and Cosmetics
The need for constantly introducing innovative new products to stay current with changing
customer tastes and preferences places a huge burden on personal care and cosmetics companies.
Product quality is a major point of marketing and brand differentiation for these companies, and
it takes a proactive approach to succeed in acquiring that new customer and developing that
brand loyalty. Engineering has to be in close communication with manufacturing, while the field
must have an open channel to relay defect information back to support and product development.
Furthermore, there is a growing complex network of suppliers and other third party vendors that,
for example, prepare surface treatments to enhance the quality of a finished good or prepare
treatment to modify powders and pigments to maximize water repellency or extended-wear
cosmetics. A consumer products quality management system can help personal care companies
seamlessly share quality information across the organization and their suppliers as well as
automate the process for managing deviations.
Tobacco
Managing the complexities of a global tobacco supply-chain creates many opportunities for
quality issues to arise. There are also diverse regulations around tobacco and smokeless tobacco
production based on the country for which the products are intended. Issues around traceability,
counterfeit products, black market products, for example, all put product quality issues into the
forefront. Because of these issues, the assessment and mitigation of risk is imperative for leading
tobacco companies today. Tobacco production software solutions can mitigate the risk of defects
or quality related issues that have legal and financial implications leading to brand erosion. Since
defects emerge as quality issues, success lies within having an internal and external quality
system in place to effectively manage processes and ensure deviations are promptly addressed.

3. TrackWise EQMS Solutions for Consumer Products
TrackWise is a web-based Enterprise Quality Management Solution (EQMS) that allows
consumer products companies to automate quality processes, compiling all information in one
central location. Providing unparalleled visibility across your organization, processes and metrics
are used to ensure compliance, risk management, and to operate in the most cost efficient way.
Highly configurable, TrackWise offers point-and-click configurability to define, track, manage,
and report on processes specific to your needs. Electronic signatures and records management
creates a comprehensive audit trail, eliminating the inefficiencies of paperwork. As TrackWise
streamlines workflow processes, integral consumer product quality management capabilities
such as traceability are leveraged through on-demand reporting, using the TrackWise and Crystal
Reports report generator.
TrackWise Seamless Integration with Other Applications
Quality is not a department or an isolated function; it is a shared corporate responsibility. Best in
class Consumer Products companies that integrate TrackWise EQMS with other enterprise
applications like Enterprise Resource Planning (ERP), Product Lifecycle Management (PLM),
Manufacturing Execution Systems (MES), and Supply Chain Management (SCM) to optimize its
capabilities and fully close the loop on quality management. Implementing applications such as
supplier quality management, audit management, complaint handling, deviation management and
CAPA solutions, adds visibility throughout your enterprise and are only strengthened when
connected to other processes and job roles in the organization.
==================
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.

4. 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
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

5. 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
algorithm for producing statistically based acceptance
limits (similar to confidence intervals) for each bar in
the Pareto chart.
4. Scatter plot Method

6. 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

7. 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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