CQEP Project Report

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

Page 1 of 19CQEP – Application of SQC Tools

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.


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


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.


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.


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.


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


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.


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.


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.


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


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


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:


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.


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.


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


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


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.


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.


http://www.qfinance.com/performance-management-best-practice/statistical-process-control-for-quality-improvement

http://www.qfinance.com/performance-management-best-practice/statistical-process-control-for-quality-improvement

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