1. Holistic quality management
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I. Contents of holistic quality management
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Do all the situations below sound just too familiar to you when you quality management
environment?
 Many different processes?
 Numerous hardcopies of specification sheets to manage?
 Tonnes of hardcopies of equipment-printed data or manually recorded data?
 Many stand-alone measurement equipments?
 Need to compile fragmented data for sharing with other departments?
 Need to consolidated fragmented data for reporting to bosses?
 Get too bogged down with all these mundane processes and no time for your core duties?
If you look forwards to a simpler life and simpler way to manage all this, our Holistic Quality
Management Suite will work for you. Just leave us your contact information. we will get in touch
with you.
HQM Suite is a trusted quality system trusted by multinational companies for their 24/7
production line.
Apart from being well known for its stability, it is also an environmental friendly system
whereby it reduces greatly the use of papers.
Data is greatly secured as user access is fully customizable and super admin can have full control
over who should have access to quality and specification data.
It is also capable of integrating with other ERP/MRP to maximize information exchange.

2. Modules in HQM
1. Specification Management
This is the module that Users capture detailed
· Material specification
· Bills of Materials
· Process Parameters
It is also capable of automate material usage calculation. Bills of Material information can be
sent to different ERP to minimise double entry and improves efficiency.
Information is securely control so that only key personnel has access to specification
information.
It is enabled with
· electronic approval function for specifications
· electronic versioning of specifications
· automation of material usage calculation

3. · promote paperless specification management environment which enhances
information security management
2. Data Integration Module
To maximise cross-system information exchange without human-intervention which is both
error-prone and time consuming, our Data Integration Module leverages on the start-of-art
technology to enable data interchange (e.g. Bills of Materials, Materials usage, Waste Level)
between different systems that already existing in your business environment with our HQM, e.g.
SAP, Oracle, etc.
Data Integration Module is also capable of integrating with various test equipments in your
production floor or laboratory to promote sharing of near-live quality information.
In most cases of production environment, there are many different types of measurement
equipment with different types of communication port (e.g. RS232, TCPIP) and comminucation
protocol.
Our breath of experiences enables us to successfully perform seamless data collection from
different equipments/production machines to provide up-to-date information/analysis to the
production personnel and management.
With our robust system architecture, you can rest assured that our Data Integration Module will
run 24/7/365 smoothly for your quality-centric business. This modules will
· minimise double entry of data by human
· improve data integrity of your quality data
· improve your speed of response to area of concerns in quality perspective
3. On-line Specification Module
If you want to improve the timeliness and accurate dissemination of specification information to
production floor, this module will fit you perfectly.
As soon as specifications are approved electronically, specification information can be
disseminated to operators by respective authority with high level of security access control.
And you can rest assured that the version displayed to operators are always the LATEST
APPROVED version.
Therefore, you can cascade specification information
· - timely
· - correctly
· - securely
4. Statistical Process Control (SPC) Module
Our SPC, in summary, is a user-friendly and easy-to-use tool for monitoring and control of
production process to ensure that it operates at its full potential to produce conforming product. It
provides much needed information for production personnel to examine a process and the
sources of variation.

4. Product quality variation in any production processes may affect the quality of the end product or
service and can be corrected if detected during production process. Thus our SPC which is
capable of alerting management of out-of-norm product quality information, is able to reduce
waste as well as the likelihood that problems will be passed on to the customer. With its
emphasis on early detection and prevention of problems, our SPC has a distinct advantage over
other quality methods, such as inspection, that apply resources to detecting and correcting
problems after they have occurred.
In addition to reducing waste, SPC can lead to a reduction in the time needed to produce a
product from end to end. This is partially due to a reduction in likelihood that the final product
will have to be reworked, but it may also result from using SPC data to identify bottlenecks, wait
times, and other sources of delays within the process.
5. Material Tracking Module
Be it your raw material is bar-coded or RFID-tagged, this module is capable of ensuring the right
materials (as compared to material data captured in Specification Module) are loaded for your
manufacturing used. Gone are the days using manual visual verification which is highly prone to
error due to human-fatigue.
It is also very important to tag or barcode your final product or work-in-progress (WIP), so as to
avoid the use of wrong WIP or the delivery of final product to the wrong customer. With this
tracking module, errors can be minimised.
Once all materials are tracked, doing product traceability can never be easier. This will also
provide vital information should there be any case of product recall, you can zoom-in to specific
batch for recall instead of using a very wide spectrum for recall, which can be extremely costly.
6. Business Analytics Module
InnoArk Analytics is a complete end-to-end solution that includes business intelligence, data
integration and data mining capabilities – providing power for technologists and rapid insight for
users. IA enables business users to intuitively access, discover and analyze their data,
empowering them to make information-driven decisions that positively impact their
organization's performance. And IT becomes heroes to their business, because they can rapidly
deliver a secure, scalable, flexible, and easy to manage business analytics platform that users
love
Business Analytics provides a web-based interface for business users to access any data they
wish to use in reporting, analysis and dashboards. With a simple wizard-based approach,
business users can turn their data into insight and make information-driven decisions in minutes.
7. Data Security Module
All transactions can only be performed if users have the required access right. This access right
profile can be set at higher level for secure information distribution.
End result? Intact and secured information control. This is to protect one of your company
greatest asset, information.

5. ==================
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

6. 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

7. 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

8. 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.

9. 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

10. 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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