cqep project report
TRANSCRIPT
Certified Quality Engineering Professional Course
Conducted By
PIQC Institute of Quality Control Islamabad
Project
Application of Statistical Quality Control Tools
Project Supervisor: Maj Gen® Salim-uddin
Submitted by: Engr. Farhan Saleem
Dated: 19th Jun 2010
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Declaration
It is hereby declared that this project report is original and prepared by the
undersigned.
Prepared by:
Engr. Farhan Saleem
CQEP course participant
PIQC, Islamabad.
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Table of Contents
1. Abstract 4
2. Introduction 5
3. Quality control tools 6
a.Flowcharts 6
b.Check sheets 8
c.Pareto charts 9
d.Histograms 11
e.Cause-and-effect diagrams 12
f. Control charts 14
g.Scatter diagrams 15
4. Process improvement 16
5. Recommendations & Conclusion 18
6. References 19
List of Figures & Tables:
Table 1 Check sheet 9
Figure 1 Process flow chart 7
Figure 2 Pareto chart 10
Figure 3 Histogram 12
Figure 4 Cause-and-effect diagram 13
Figure 5 Control chart 15
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Abstract:
Statistical quality control tools are aimed to identify and solve process
problems. These tools have proved to be extremely effective instruments for
data collection, analysis, process control and quality improvement. By
systematically identifying process variation and potential nonconformance with
design expectations early in the production or service environment, managers
can proactively make corrections before the process variation negatively
impacts quality and customer perceptions.
The objective of this study is to apply the seven quality control tools namely
process flow charts, check sheets, pareto charts, histograms, cause-and-effect
diagrams, control charts & scatter diagrams on the inspection process of a
product which is developed through vendor for identifying & improving the
quality problems through monitoring the process variation and taking action to
eliminate the root causes.
The QA department collects, summarizes & analyzes the inspection results for
the improvement of product quality. They apply seven quality control tools for
the identification of non-conformances/defects not meeting the acceptance
criteria/specifications. The results are analyzed for taking corrective action
against the quality problems to bring improvement in results. All the defects
are identified through check sheet and the major defects are categorized by
pareto chart, their causes identified for taking corrective action.
The data of the critical dimension contributing to non-conformance is taken
and accordingly histograms, control charts & scatter diagram are drawn for
identifying the process variations and relationship among variables. The root
causes of the variations are analyzed and corrective action are taken for quality
improvement. Ultimately the variation is minimized and the results got
improved by the application of seven quality control tools.
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Introduction:
The project study is based upon the application of seven quality control tools
on the product development & inspection process which is developed through a
vendor. There has been lot of problems being faced during inspection of the
items and therefore 20-25 % of the lots get rejected due to poor quality of the
finished product.
In order to improve the quality of product, the project team along with Quality
assurance department planned to improve the product quality to reduce the
rejection level through application of statistical quality control tools. Although
the individual defect in the product are contributing less in the overall
defectives but their combined effect results in more rejection of the products.
The product is designed by the project department along with drawings &
specifications. All the documentation along with the acceptance criterion is
forwarded to the vendor for the product development. The finished product is
received by the ware house department on the delivery challan provided by the
vendor. The ware house team verifies the product quantity and give their
remarks on the delivery challan in case of any shortfall in quantity for feedback
to the vendor and maintaining own record.
Afterwards, the product is forwarded to the Quality assurance department for
inspection. They inspect the product as per specifications and acceptance
criteria. The rejected items are returned to vendor and inspection note prepared
for finally handing over the items to ware house department and onward
delivery to the customer.
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Statistical Quality Control Tools
The quality control tools are commonly referred to as the seven basic tools.
Kaoru Ishikawa (1985) is credited with making the following statement with
respect to these tools, “As much as 95 percent of all quality-related problems in
the factory can be solved with seven fundamental quantitative tools.”
Flowcharts
Check sheets
Pareto charts
Histograms
Cause-and-effect diagrams
Control charts
Scatter diagrams
They are simple but powerful data analysis tools, and help to solve the majority
of quality problems. These tools have been used worldwide by companies,
managers of all levels and employees. They provide the means for making
quality management decisions based on facts. No particular tool is mandatory,
any one may be helpful, depending on circumstances.
Flowcharts:
Flowcharts depict the progress of work through a series of defined steps. It
promotes a common understanding of process steps and the
relationships/dependencies among those process steps. They can be used to
communicate a process to employees who are being trained for the work, and
management can use them to evaluate process flows, constraints, and gaps.
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Figure 1. Process flow chart
Quantity conformance by WH
Delivered to QAD for Inspection with Inspection performa
Items received by WH from Supplier on Delivery challan
Yes
Passed
Issue of Inspection note by QAD
Yes
Products handed over to WH
Feedback to Supplier No
No
Inspection of Items by QAD as per Criteria
Product developed by Vendor as per requirementsDesign specs provided to Vendor by PM
Process Flowchart
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The symbols used in flowcharting are standardized; some of the more
commonly used are rectangles (activities and tasks), diamonds (decision
points), rectangles with a wavy base (documents), cylinders (files), and arrows
(linkages). The flowchart in Figure 1 demonstrates the process flow of a
quality assurance department starting from project launching to the delivery to
customer.
Check Sheets:
The purpose of a check sheet is to summarize, and in some cases graphically
depict, a tally count of event occurrences. They are a simple way of gathering
data so that decisions can be based on facts, rather than anecdotal evidence.
Checklist items should be selected to be mutually exclusive and to cover all
reasonable categories.
A check sheet is used for counting the number of occurrences of an event, such
as defects. In many instances, a check sheet will summarize count data related
to certain types of defects and will provide a rough graphical representation of
where in a part or process defects occur. It is also used to develop Pareto charts
& Histograms.
The check sheet in Table 1 below shows different types of defects found during
the inspection of product which is developed from the vendor. The
categorization is mentioned in decreasing order of occurrence of the defects.
Both the visual & measurement techniques are used in the inspection of the
developed product. This data obtained from the above check sheet will be used
in plotting the pareto chart.
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S/N Defects Frequency
1 Req Pressure not sustained 328
2 Poor finishing of Lid 293
3 Spidle / rod rusty 113
4 Bottle leakage during oper 47
5 PRV not released at 4.9 Bar 25
6 Nozzle malfunctioning 13
7 Cylinder O Ring malfunctioning 5
8 Pump Washer malfunctioning 4
9 Handle broken 2
10 Press knob malfunctioning 2
11 Lock broken 2
Table 1. Check sheet
Pareto Charts:
The purpose of a Pareto chart is to identify those “vital few” areas that account
for the largest frequency or relative frequency in a data set and separate those
vital few areas from the “trivial many.” These are graphical demonstrations of
occurrences, with the most frequently occurring event to the left and less
frequent occurrences to the right. Pareto charts are named after Vilfredo Pareto,
an Italian economist who identified that 80% of the wealth is held by a
relatively small share of the population. This has been translated into the Pareto
principle, which says that about 80% of outcomes are typically created by
about 20% of causes. By constructing a Pareto chart, managers can quickly see
what problems are most prevalent in their organizations.
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The Pareto chart in Figure 2 shows the occurrences & contribution of various
defects. 39.3% of the defects are due to pressure instability, followed by poor
finishing of lid at 35.1%.
Figure 2. Pareto chart
The chart depicts that pressure instability in the product is the relatively largest
frequency occurring defect while poor finishing of lid is the second most
frequent occurring defect contributor to the overall defects. Therefore by
addressing / eliminating these two vital few causes can remove the 80% of
failures. The other trivial many causes only contribute to the 20% of failures
which may not necessarily be need to addressed on priority.
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Histograms:
The purpose of a histogram is to graphically depict the frequency of occurrence
of events, where event occurrences are sorted into categories of a defined range
along a continuous scale. They are another form of bar chart in which
measurements are grouped into bins, each bin representing a range of values of
some parameter. It provides a quick representation of the “spread” and
“centering” of a process.
Histograms are used when it is important to see and understand how a
particular set of data are distributed relative to each other, and possibly relative
to a target or tolerance. Besides the central tendency and spread of the data, the
shape of the histogram can also be of interest. If it a bi-modal distribution (two
peaks) means that the measurements are not from a homogeneous process,
indicating two central tendencies.
Based on the above analysis of Pareto chart, it is evident that pressure
instability is the major critical factor causing the product failure. Therefore, by
digging in the detail of this parameter can lead to some conclusion. Foregoing
in view, the data of this critical dimension is plotted for Histogram to
investigate further.
Figure 3 shows the Histogram for the pressure parameter which could not
sustained at the specified/required level. As per the design specification
criteria, the air pressure inside the product should sustained within 3±0.5 limits
ranging from 2.5-3.5 bar. The inspection data shown below depicts that the
frequency distribution is normally distributed & mean almost lies in the centre
but there are some data points going beyond the design limits. This shows
some process variation and need to be controlled to bring the data within
specification limits. Action is required to further dig down the root causes of
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this process variation so that corrective action may be taken for process
improvement.
Figure 3. Histogram
Cause-and-Effect Diagrams:
They are also known as Ishikawa diagrams, named after their inventor, Kaoru
Ishikawa or fishbone diagrams, after their appearance, or cause and effect
diagrams after their function. These diagrams depict an array of potential
causes of quality problems. The problem (the head of the fish) is displayed on
the right, and the bones of the fish representing the potential causes of the
problem are drawn to the left. Potential causes are often categorized as
materials, equipment, people, environment, and management. Other categories
may be included as appropriate. It is useful in brainstorming the causes of
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problems (including potential problems) from multiple perspectives, these
diagrams should include all possible reasons for a problem. When completed,
further analysis is done to identify the root cause.
Figure 4 is an Ishikawa diagram to evaluate the possible causes resulting in
pressure drop in the spray gun bottle based on 4M’s (man, machine, material,
method) & environment. Brainstorming technique is applied keeping in view
all of the above factors by a group of people and the relevant causes are plotted
against each factors contributing to product failure. The most critical & related
factor caused by the material of the product & ineffective process. Therefore
the material of the product is standardized including its composition, grade,
etc. The process is improved as well to fulfill the design specifications.
Figure 4. Cause-and-Effect diagram
Control Charts:
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Control charts combine expanded run chart information with statistical control
data to help identify process variation over a period of time that is not likely
due to random chance.
Control charts are used to show when a process is in, or out of, statistical
control. Statistical control does not imply zero variation—some degree of
variation is normal and it is unrealistic to expect zero variation.
However, the control chart is able to demonstrate data patterns that indicate
that a process is out of control, and it is useful as a tool for making continuous
improvement by reducing variability. The most commonly employed control
charts are the mean chart and the range chart, often referred to as X-bar and R
chart.
From the mean and variance, control limits can be established. Control limits
are values that sample measurements are not expected to exceed unless some
special cause changes the process. A sample measurement outside the control
limits therefore indicates that the process is no longer stable, and is usually
reason for corrective action.
These limiting bounds (upper & lower control limits) are each three-sigma
limits, meaning that almost all (99.73%) of the variation in the process is
expected to fall within a six-sigma limit. The range chart (R-chart) shows
variation within each sample.
Figure 5 above illustrates an Xbar-R Control chart of variable pressure to find
out the variation in the process due to common & assignable causes. In both
mean & R chart, there are only common causes and no special causes
identified. It means that the process is stable & within control as there is only
random variations. Further process improvement can be done to bring the data
values closer to the mean for more smoothing process.
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Figure 5. Xbar-R Chart
Scatter Diagrams:
The purpose of a scatter diagram is to graphically display indications of a
relationship between two variables. They are a graphical, rather than statistical,
means of examining whether or not two parameters are related to each other. It
is simply the plotting of each point of data on a chart with one parameter as the
x-axis and the other as the y-axis. If the points form a narrow "cloud" the
parameters are closely related and one may be used as a predictor of
the other. A wide "cloud" indicates poor correlation. The relationship being
investigated is called a correlation, and it identifies three possible relationships
as positive correlation, no correlation, and negative correlation.
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Process Capability Analysis:
Process capability analysis is a technique that is used to determine the ability of
a process to meet product or service specifications. It is a useful tool to
evaluate variation within a process and whether improvements can be made to
process control. Although a process may be within control limits as determined
by control chart data, capability analysis takes things a step further by
evaluating the amount of variation in process outcomes (the product or service)
compared to the capability of the process.
Capability analysis is based on measures of process capability (Cp) and process
control (Cpk). These measures are based on the means and standard deviations
of a process variable and are indicators of the aptitude, or capability, of the
process to perform. Similarly, measures of actual process performance (Pp)
and process control (Ppk) demonstrate how a process is actually performing. A
comparison of the actual process control data (Ppk) with the process capability
data (Cpk) helps managers to numerically evaluate how much variation there is
in an in-control (within control limits) process, and whether modifications of
the process will reduce variation.
Process Improvement:
After the identification of critical problems contributing towards the
product failures and to reduce the process variation, various steps are
taken to improve the processes as mentioned above.
Figure 6. below depicts the improvement in results after the application
of statistical quality control tools on the processes. There is a significant
improvement seen as shown below in the Histogram. The process
variation has reduced to a greater extent and the data values are more
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centred as well as the spread of the data is reduced. Therefore we have
been able to achieve the desired results effectively.
Figure 6. Histogram
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Recommendations & Conclusion:
Statistical quality control benefits organizations by providing a systematic
method for the monitoring and evaluation of process variation. Too often,
managers do not notice changes and problems in processes until either the
output is inspected or customers make complaints.
By proactively identifying potential process problems and using SQC tools to
evaluate process outcomes and improve process control, organizations are able
to direct their resources more efficiently and can focus management time and
attention on the most pressing problems.
By application of statistical quality control tools, it was evaluated that the
process variation in above case was due to insufficient quality of the material
and ineffective processes. Therefore, the material of the product is standardized
including its composition, grade, etc. and the processes were improved to
fulfill the design specifications. As a result of this rigorous effort by the team,
the level of lots rejection decreased from 20% to 5-7%.
Therefore SQC supported in evaluating the process variation through
monitoring the processes, find out the root causes of the problems &
accordingly lead to effective corrective actions which eventually resulted in
improvement of the process variation hence ultimately improved the product
quality.
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References:
Certified Quality Engineer Handbook, Third edition.
System Reliability Centre, RAC Publication, QKIT, Quality Toolkit, 2001.
http://www.qfinance.com/performance-management-best-practice/statistical-
process-control-for-quality-improvement.
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