chap 6 - quantitative method for quality management

8/9/2019 Chap 6 - Quantitative Method for Quality Management

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Quantitative methods for quality management

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Objective

To understand the various methods for

quantitative measurement of quality.


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Scope

Quality management by measurement

Management tools for Quality Improvement

The Seven QC Tools

Problem identification tools

Problem analysis tools

Statistical Process Control

Software Quality Measurements Productivity measures

Complexity measures

Baselining


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Measurement strongly advocated as a crucial factor on the

road to quality improvement.

Measurement of quality is key to improvement of quality.

Quality of the product as well as the process has to bemeasured and monitored.

The initial step in quality improvement is

To measure the product or process and find the baseline or

the current situation. Plans for improvement are then based on this.

To monitor by keeping track of the progress, measurement

has to be iterated.


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Dr. Deming emphasized the use of statistical methods to find

out the sources of defects, in his 14 quality principles.

No exact way for determining that quality is being delivered.

Measurement provides a quantified record of quality. Tells the organization performance.

This information essential to manage quality.

Measurement must be an integral part of the organizations

work process.

There are seven basic QC tools for problem identification and

analysis

Seven management tools for quality improvement are also

there.


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Statistical process control makes use of statistical methods to

monitor process performance.

Statistical methods are used.

Determining whether or not the process is under control

To help identify root causes that are causing defects.

These tools can be used individually as well as in

combination.


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Management Tools for Quality Improvement

Relations Diagram

Identifies relationships between specific items, factors orideas related to a central issue to be addressed.

Provides clarity to intertwined relationships in a complex

situation in order to find an appropriate solution.

Affinity Diagram


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A network of cause-effect relations.

Often used to trace through answers to why is it

happening? questions. Useful in designing steps to counter market complaints,

planning to eliminate latent defects in products etc.

Relation Diagram


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Tree Diagram Enables the development of specific action items to

address a subjective goal.

Often used to relate means to ends, which in turn are

means to more general ends. Helps answer how? questions. How do we do this?

Tree Diagram


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Matrix Diagram

Allows a logical look at the relationships between a group

of items.

Items might represent a cause-effect relationship forsolving problems or what-how relationship for attaining

goals and objectives.

Method identifies corresponding elements involved in a

problem situation or event. Elements are arranged in rows and columns on a chart that

shows the presence or absence of relationships among

collected pairs of elements.


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Popularly used for quality function deployment to transformthe desires of the customer into the language required to

implement a product.

Relates multiple alternatives to multiple consequences of

each. Often used to answer which? questions.

Which things to do to satisfy customersrequirements?

Matrix Diagram


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Matrix data analysis

Quantifies and arranges matrix diagram data and

mathematically analyzes it.

Makes information easy to visualize and comprehend too. The only numerical analysis method.

Matrix Data Analysis Diagram


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Canbe used to analyze

Production processes where factors are complexly

intertwined

Causes of non conformities that involve a large volume ofdata.

Process decision program chart

Enables potential problems to be identified during the

planning phase of a complex and critical project Helps to develop contingencies and preventive measures.

A diagram of the flow of alternative possibilities and

counter measures for each.


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Used to design responses to possible setbacks, answers to

whatif? questions.

Can be used to implement countermeasures to minimize non

conformities in the manufacturing process etc.

Simple graphical tool that can be used to mitigate risk in

virtually any undertaking.

Process Decision

Program Chart


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Activity network diagram

Called the arrow diagram

Utilized by PERT or CPM methods.

Very useful for scheduling events and identifying

bottlenecks.

Typically used to answer when? question.

Activity Network

Diagram


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The Seven QC Tools Used for measurement and control. Meant to be used by everyone.

Strategy for mass movement.

The seven QC tools used for problem identification and

analysis are:

Problem identification

Pareto Analysis

Cause and effect diagram

Problem analysis

Check sheet

Control charts

Histograms

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The Seven QC Tools Scatter diagram Stratification

Tools introduced in the 1950s by Dr. Deming and Dr. Juran.

Problem Identification Tools Pareto Analysis

In problem solving, the first task is to identify the problem

areas.

There will be numerous problems.

Impractical to attack all these problems at once.

Common question that arises is Which problem do I

tackle first?

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The Seven QC Tools

Rule of statistics called the 80-20 rule states that 20percent of any activity will account for 80 percent of the

frequency.

Pareto analysis one of the most powerful quality tools.

Four steps to be followed in preparing a Pareto analysisare as follows:

Step 1Identify problem area.

Step 2Identify and name causes of the problem.

Step 3 Document the occurrence of the cause of theproblem.

Step 4Rank the causes by frequency using the Paretochart.

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The Seven QC Tools

The Pareto chart is a bar chart.

After Pareto Analysis, then action can be taken to firstaddress the largest cause of undesirable activity.

Once that is solved the next biggest cause can be handled and

so on.

0

20

40

60

80

100

1 2 3 4 5 6 7 8

Series1Frequency of

Occurrence

Category of Errors

Pareto Analysis

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The Seven QC Tools Cause and effect diagram

A problem identification tool.

Helps to know the real causes and the interrelations amongcauses.

A cause and effect diagram shows an effect at the right andmain causes of the effects off the horizontal axis.

Main causes are in turn having effects that have sub causesand so on down many levels.

Enumerates the variety of causes rather than the frequencyof events.

To solve a problem involves construction of a cause andeffect diagram.

Also called a fishbone diagram or Ishikawa diagram

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The Seven QC Tools

Named after the champion of this tool, Kaoru Ishikawaa quality expert from Japan.

MAN MACHINE

Cause and Effect Diagram

Q tit ti th d f lit t


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The Seven QC Tools Problem Analysis Tools

Check Sheet

Basic data collection mechanisms.

Forms to gather and record data in an organized manner.

Helps to determine the occurrence of an event or cause.

Often referred to as checklist or tally sheet of events.

AX XB XCX X X XDX X X

Check Sheet



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The Seven QC Tools Control Charts

A statistical technique.

Assesses, monitors and maintains the stability of a process.

Developed in 1924 by Control Walter Shewhart.

Objective to monitor a continuous repeatable process and

the process variation from specifications or limits.

A graph with limit lines to show acceptable range of quality

production.

Helpful for spotting abnormal situations in standard

processes.

Used to plot over time (left to right) the observed output or

status of a process.



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The Seven QC Tools

In the figure the circled point is outside the control limits

and indicates that action should be taken to control such

variations.

Does not actually control anything.

A decision support mechanism that provides a basis for

action.

Extensively used in Statistical Process Control.

Upper Limit

Lower Limit

TIME

Control Chart



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The Seven QC Tools Histograms

Also called frequency distribution chart

A bar graph of measured values (data sets) to display the

frequency of occurrences of the measured data.

Provides a way to measure and analyze data collected abouta process or problem.

0

5

10

15

20

25

1 2 3 4

Histogram

CPU sec. between

Frequency ofOccurrence



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The Seven QC Tools Scatter diagrams

Also called as plot.

Shows the relationship that might exist between two

variables / factors.

Can test for possible causeeffect relationships.

Often referred to as correlation diagrams.

Scatter

Diagram

Inspection Mistakes

Illuminat

ion Level

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The Seven QC Tools Used to display what happens to one variable when another

variable changes, in order to test a theory or make forecasts.

Stratification

A technique used to analyze / divide a universe of data into

homogenous groups.

Plots many data points, typically with a measure of quality

on one axis and a variable that influence quality on the other

axis.

The Japanese work force mastered these elementary toolsfor quality control and got a head start in producing quality

products.



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Statistical Process Control Key element of the quality process.

Fundamental reality that the outputs of all processes vary

over time.

According to Dr.Deming- the differences in the results of

processes are due to special causes or common causes ofvariation.

Special causes represent forces outside the process, acting

with an adverse effect.

These are variations caused by special circumstances.

Can be pinpointed to a specific location or time.

If special causes are numerous then it follows that the process

is unstable and out of control.



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Statistical Process Control Common causes lead to variations.

These are the net effect of the numerous sources of variation

inherent in the current system.

Determination of the type of cause is key to effective

intervention.

The type of cause designates the responsibility for action.

Correction of special causes requires action on the process.

Common causes are inherent in the system.

Must be resolved by those who create the system within

which the operations are performed.

Hence SPC- a scientific measurement and decision-making

approach



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Decisions and plans are formulated on the basis of actual

collection and evaluation of the correct measurement data.

For repetitive processes subject to some kind of variability:

Statistical process control keeps track of how the process isperforming.

A sophisticated technique takes time, expertise and money to

introduce properly.

Cannot be used as a casual way of monitoring process. Most effective method for determining cause is the control chart.

Signals the existence of special causes, when they appear.

Indicates the extent of variation resulting from common causes.



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In the model given, the control limits serve as decision lines.

These are limits derived from past experience.

Denotes the range within which fluctuations are expected tofall.

Special causes are denoted by instances (circled) that fall

outside of control limits (Fig.A) or special patterns in the

plotted points (Fig.B).

special pattern

lower

limit

upper

limit

Fig. A Fig. B



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Statistical Process Control SPC is a highly defined technique.

Has a fundamental effect on the way in which the organization

operates.

A process in statistical control enables prediction of future

performance.

Brought huge advances in reliability of both machines and

processes, helping to control their inherent variability.

In software development field, application of statistical process

control has found scope particularly in the following:



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Studying previous programs in order to find the deficiencies

and improve the process that produced them.

Real time monitoring of the present program to enable

signaling potential problems and institute appropriate

countermeasures.

Assessment of schedule, cost and human resources of

previous projects in order to develop realistic schedules andestimates.

SPC methods facilitate process improvement by disclosing

the causes of variation.



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Software Quality Measurement Measurement of software quality has faced difficulty since itis not based on quantitative laws.

Software metrics is still a complex field continuouslyevolving.

Software quality is a complex mix of various factors that vary

across different applications and projects.

Need for quality measurement has given rise to a number ofmetrics that would measure different aspects of the software.

Measurement gives a tool to communicate to your customers

the size of their request and extrapolate productivity, qualityand estimating accuracy.



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Software Quality Measurement Quality characteristics desired in software products defined in the

ISO standard guidelines for Information Technology Software

Product Evaluation Quality Characteristics are:

Functionality

Suitability Accuracy

Interoperability

Compliance

Security

Reliability



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Software Quality Measurement

Maturity

Fault tolerance

Recoverability

Usability Understandability

Learnability

Operability

Efficiency Time behavior

Resource behavior

Maintainability



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Analyzability

Changeability

Stability

Testability Portability

Adaptability

Installability

Conformance

Replaceability



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Metrics based on different attributes has been devised to measure

quality.

Can be used to help estimate projects, measure project progress

and performance and quantify product attributes.

Also required to compare softwares or projects.

Examples of metrics include

Product metrics, e.g. productivity measures and complexity

measures. Software development resource metrics, no. of people working

on a project.

Software development process metrics, e.g. no. of lines of code

inspected.



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Some of the metrics used commonly in projects are:

Attributes Metrics

Size SLOC (sourcelines of code)

Effort person hoursCost currency

Complexity software (system software, embedded, GUI, datacategory communications etc)

Productivity Measures.

Two popular productivity measures are:

Size-oriented measures.



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Software Quality Measurement Function- oriented measures.

Productivity defined as the amount of work that can be done in

a unit of time.

Step 1

Each function is classified as one of the following function types.

External input type

External output type

Logical internal file type External interface file type



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Step 2

The system being measured is looked at in terms of 14 general

system characteristics.

The degree of each characteristics influence on the system isestimated.

The 14 characteristics are:

Data communications

Distributed functions

Performance

Heavily used configuration



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Software Quality Measurement Transaction rate On-line data entry

End user efficiency

On-line update

Complex processing

Reusability

Installation ease

Operation ease Multiple sites

Facilitate change



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Software Quality Measurement The degree of influence is assigned to each of the above

characteristics from the following rating.

0 = no influence 1 = insignificant 2 = moderate 3 = average

4 = significant 5 = strong influence throughout.

Step 3

After quantifying the function and system characteristics, an

unadjusted function count is produced by :

The number of functions in each of the 15 categories is

multiplied by a predefined Weighting Factor.



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The weighting figures from each category are added together

to produce an adjusted function count (A).

The degree of influence ratings (0-5) for the 14 general

characteristics are added together to produce a total. The total is multiplied by .01, then the result is added to .65

(B).

The actual function point metric is now calculated by

multiplying the adjusted function count A with B.



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Complexity Measures

Software quality is measured by looking at it from various

attributes points of view, like reliability, stability,

maintainability etc. Another good indicator of quality is correctness.

This is looked at by determining the number of defects per size

unit of code.

Referred to as the defect rate. Operational quality of the software is determined by measuring

the characteristics that define its performance.



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These are:

Processor utilization

Memory utilization

I/O performance Complexity.

An appropriate measure of software complexity should cover

measurements of cost, time and reliability.

McCabes complexity metric: Count of the number of decision statements in a program.



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Gives a fairly good correlation between complexity to bugs anddebugging difficulty.

Advises partitioning routines whose metric exceeds ten.

Good aid to productivity and complexity evaluation and should

be used as a rule of thumb measure. Some of the aspects, which are not taken into account for

measurements are:

Severity of a bug is not taken into account.

Some bugs would appear only under stress testing and theseare not identified in the present scenario.

The impact of maintenance on the number of bugs is ignored.

The number of bugs does not remain constant.



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Baselining Method used to determine the current performance, accuracy

or efficiency of products or processes.

Used for the purpose of measuring progress or variances

from past performances. Baseline is a product or performance specification.

Been formally reviewed and agreed upon, and documented.

Forms the basis for any further development.



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Summary Measurement is a crucial factor on the road to quality

improvement. Measurement provides a quantified record of

quality.

The seven management tools being widely used for quality

improvement are: Affinity diagram

Relations diagram

Tree diagram

Matrix diagram Matrix data analysis

Process decision program chart

Activity network diagram



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Summary

The seven QC tools used for problem identification andanalysis are:

Problem identification

Pareto AnalysisCause and effect diagram

Problem analysis

Check sheet

Control charts

Histograms

Scatter diagram

Stratification



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Summary Statistical Process Control is a scientific measurement and

decision-making approach.

The quality characteristics that is desired in software products

are: Functionality, Reliability, Usability, Efficiency,

Maintainability and Portability. Two popular productivity measures are. Size-oriented

measures and Function- oriented measures.

McCabes complexity metric is a count of the number of

decision statements in a program. Baselining is the method used to determine the current

performance, accuracy or efficiency of products or processes

for the purpose of measuring progress or variances from past

chap 6 - quantitative method for quality management

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