1. Quality management system requirements
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I. Contents of quality management system requirements
==================
After outsourcing manufacturing to lower cost countries, many companies find new demands
imposed on their quality management processes. As a result, many companies find their existing
quality management systems to be no longer effective. This is especially true if their existing
quality system is either paper-based or implemented using a PC-based point solution. This article
identifies the quality management requirements for an outsourced manufacturing environment
and explains how companies that outsource manufacturing can still maintain a clear visibility
into their outsourcer's process capability and their overall product quality.
As companies shift manufacturing and assembly operations offshore to low cost countries, a
large number of deliveries to US based customers suddenly become cross-border transactions.
Sometimes shipments can take weeks to be delivered to distribution sites within the United
States, resulting in long supply chains. It is very important for such companies to gain visibility
into process capability and quality issues within their outsourcer's manufacturing sites so that
they can prevent any unacceptable quality products from entering the inbound supply chain. If
these unacceptable quality products enter the supply chain, the company has to wait to scrap a
part of the shipment at the point of destination, weeks after it was shipped from the outsourcer's
manufacturing site. If a company is forced to scrap products with quality issues this late in the
delivery chain, it may encounter shortages if the inventory in the distribution system is already
too lean. The company may also see disruption in fulfillment of orders to their customers - a very
high opportunity cost. Carrying high inventory at distribution centers to buffer against disruption
from quality issues is an expensive alternative, especially in an industry such as high technology
or consumer electronics with short product life-cycles. The additional transportation and
handling incurred due to poor quality products being rejected at the point of destination, instead
of the point of manufacture, also leads to increased cost of inventory write-offs. As a result, it is

2. critical that the company's quality management systems are able to provide timely and clear
visibility into any quality issues at their outsourcer's manufacturing site.
With their existing quality management systems (especially if they are paper-based systems or
point solutions), the company is more than likely to get aggregated quality data from the
outsourcer via excel spreadsheets once every week or every 15 days. By the time the quality
managers at the company can analyze the data to get visibility into process capability and
inspection results, it is too late to proactively address quality issues. In addition, the scope of data
obtained is normally too limited to do any trend analysis and understand the root cause of
process capability issues. As a result, many companies are seeking to get clear and timely
visibility into process capability and quality data, so they can proactively minimize quality issues
at their outsourcers manufacturing plants. Industry data shows that by gaining clear visibility into
quality data, a company can reduce the costs of quality by 5-10% due to reduced inventory write-
offs or and lower inventory carrying costs, and increase their revenues by 1-2% by reducing
missed market opportunities from poor quality shipments within a long supply chain, resulting in
huge impact on the bottom line.
Such companies need to deploy systems that enable them to:
 Define inspection points in the manufacturing process of the outsourcer's line; and define
quality attributes to be collected at those inspection points, so that the outsourcer can collect
data using a web-based quality system at those inspection points. The system then
automatically calculates process capability and makes it available to quality managers at
corporate - all within hours. As a result quality managers at corporate have real-time
visibility into process capability issues at their outsourcer.
 Enable the company's quality engineers to inspect finished goods at delivery points, collect
that data, identify issues, automatically correlate the collected data to process capability
information from that manufacturing batch and then deliver the aggregate information to
corporate quality managers.
 Ability for corporate quality managers to trend all collected data, identify issues, create
corrective actions to be implemented at outsourcer's plants, ensure that outsourcer has instant
visibility into corrective actions so the outsourcer's engineers can identify root cause and
resolution, enable company's quality managers to track the progress of corrective actions and
ensure that they have been successfully closed.

3.  Enable corporate to audit the outsourcer's processes on a frequent basis and easily correlate
the detailed audit data and results against previously identified corrective actions to ensure
the corrective actions were successfully implemented.
Using these capabilities, the company's quality managers not only can prevent a poor quality
shipment from entering the supply chain in a timely manner, they can also use quality issues to
create appropriate corrective actions and systematically prevent such problems from occurring
again
Many companies also find that their contract manufacturer may be using the same plant to
manufacture products for multiple customers and hence can not be forced to install different
systems for different customers at the same plant to support their respective quality needs. As a
result, the company has to rely on process and product quality information from the contract
manufacturer's quality system. That information usually does not integrate well with the
company's internal systems and is frequently not available in a timely manner.
Hence, a new breed of quality management systems is needed to support long supply chains.
These systems must be web-based, so a company can extend its internal quality system to its
contract manufacturer, where they can enter the required quality information for their customer's
products. As a result, the company gets instant access to quality information without requiring
the contract manufacturer to install a dedicated system at their plant. These systems must also
support an extraprise data and security model, so a contract manufacturer can not see the quality
issues that the company faces at a competing outsourcer or their internal plants. The company
should also be able to configure the system easily to allow them to simultaneously deploy
different quality processes at different outsourced or offshore sites to accommodate varying
process maturity levels at each of such sites. The system must also support an integrated
inspection/audit, non-conformance tracking, corrective action, change control, document
management, and user certification capabilities, so the company can implement an end-to-end
closed loop quality process for an outsourced supplier. A traditional point solution does not meet
these requirements and increases a company's risk of high reject costs and disruption of supply of
finished goods for their customer orders.
==================
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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