Quality Management Tools

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Quality Management Tools

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1 Modern Quality Management

Modern quality management requires customer satisfaction prefers prevention to inspection recognizes management responsibility for quality

Noteworthy quality experts include Deming, Juran, Crosby, Ishikawa, Taguchi, and Feigenbaum

Quality; Who’s who

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Quality Experts

Deming was famous for his work in rebuilding Japan and his 14 points

Juran wrote the Quality Control Handbook and 10 steps to quality improvement

Crosby wrote Quality is Free and suggested that organizations strive for zero defects

Ishikawa developed the concept of quality circles and using fishbone diagrams

Taguchi developed methods for optimizing the process of engineering experimentation

Feigenbaum developed the concept of total quality control

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Fishbone or Ishikawa Diagram

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Malcolm Baldrige Award and ISO 9000

The Malcolm Baldrige Quality Award was started in 1987 to recognize companies with world-class quality

ISO 9000 provides minimum requirements for an organization to meet their quality certification standards

HKMA Quality Award

http://www.hkma.org.hk/qa/award.htm

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2. Quality Planning

It is important to design in quality and communicate important factors that directly contribute to meeting the customer’s requirements

Design of experiments helps identify which variables have the most influence on the overall outcome of a process

Many scope aspects of IT projects affect quality like functionality, features, system outputs, performance, reliability, and maintainability

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Quality Assurance

Quality assurance includes all the activities related to satisfying the relevant quality standards for a project

Another goal of quality assurance is continuous quality improvement

Benchmarking can be used to generate ideas for quality improvements

Quality audits help identify lessons learned that can improve performance on current or future projects

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Quality Control What it is:

*** The main outputs of quality control are

acceptance decisions rework process adjustments

Some tools and techniques include pareto analysis statistical sampling quality control charts testing

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3. Pareto Analysis

Pareto analysis involves identifying the vital few contributors that account for the most quality problems in a system

Also called the 80-20 rule, meaning that 80% of problems are often due to 20% of the causes. It is the fundamental postulates underlie the rational for the Statistical SW Quality Assurance.

Pareto diagrams are histograms that help identify and prioritize problem areas.

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80% of the contribution comes from 20% of the contributors:- 80% of the engineering is consumed by 20% of

the requirements 80% of the software cost is consumed by 20% of

the components 80% of the errors are caused by 20% of the

components 80% of software scrap and rework is caused by

20% of the errors 80% of the progress is made by 20% of the

people. …

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Sample Pareto Diagram

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Statistical SQA

SQA & Traceability as example on SQA & FTR notes L14

p209, Pressman.

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Statistical Sampling and Standard Deviation

Statistical sampling involves choosing part of a population of interest for inspection

The size of a sample depends on how representative you want the sample to be

Sample size formula:

Sample size = .25 X (certainty Factor/acceptable error)2

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Commonly Used Certainty Factors

Desired Certainty Certainty Factor

95% 1.960

90% 1.645

80% 1.281

95% certainty: Sample size = 0.25 X (1.960/.05)2 = 38490% certainty: Sample size = 0.25 X (1.645/.10)2= 6880% certainty: Sample size = 0.25 X (1.281/.20)2 = 10

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Standard Deviation

Standard deviation measures how much variation exists in a distribution of data

A small standard deviation means that data cluster closely around the middle of a distribution and there is little variability among the data

A normal distribution is a bell-shaped curve that is symmetrical about the mean or average value of a population

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Normal Distribution and Standard Deviation

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Sigma and Defective UnitsSpecification Range

(in +/- Sigmas)

Percent ofPopulation

Within Range

Defective Units

Per Billion

1 68.27 317,300,000

2 95.45 45,400,000

3 99.73 2,700,000

4 99.9937 63,000

5 99.999943 57

6 99.9999998 2

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Quality Control Charts and the Seven Run Rule A control chart is a graphic display of data that

illustrates the results of a process over time. It helps prevent defects and allows you to determine whether a process is in control or out of control

The seven run rule states that if seven data points in a row are all below the mean, above the mean, or increasing or decreasing, then the process needs to be examined for non-random problems

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Control Chart of 12” ruler

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Control Chart contiu.

The output of a production process will fluctuate. The causes of fluctuation can just be random or non-random due to desirable/undesirable process change. Control charts graph and measure process data against control limits. Control charts can distinguish the random variation from assignable causes or non-random causes.

We cannot adjust random variation out of a process. Process adjustments for random variation are neither necessary nor desirable. This is over-adjustment or tempering, and it makes the process worse.

We can and must investigate assignable causes (or non-random causes). Points outside the control limits are evidence of process problems. Analyst must investigate every out of control point for an assignable cause. They must record their findings and any corrective actions. For example, a tool adjustment, or change in Formal Technical Review format or worn tooling, may correct the problem.

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Pattern analyzing of Control Chart 7-Run rule

7-run-rule is used to filter out the random variation in a production process. shows the ‘trends’ that are caused by the ‘assignable causes’ or non-random causes that required investigation and possible corrective action to be taken.

7-run-rule pattern: seven points above mean value; seven points below mean value; seven points or all increasing ; or seven points all decreasingthe patterns are indicators of non-random problems which

can be symptom of process out of control.

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To develop a Control Chart to determine project

stability

Plot individual metric values on a chart. Compute the mean value for the metrics value and

plot the line. Plot the Upper Control Limit and Lower Control

Limit. Compute a standard deviation as (Upper-control-

limit - mean)/3. Plot lines one and two standard deviation above and below Am. If any of the standard deviation lines is less than 0.0, it need not be plotted unless the metric being evaluated takes on values that are less than 0.0.

The Std Dev.# is then plotted on the control chart.

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Statistical Testing

Statistical testing is a system testing process in which the objective is to measure the reliability of a system rather than to discover faults. Statistical testing can be combined with reliability growth modeling. Predictions of the final system reliability and when that will be achieved can be made. As failures are discovered, the underlying faults causing these failures are repaired so that the reliability of the system can improve in the course of the testing processA lot of airlines manage dispatch reliability with statistical testing approach.

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The Cost of Quality

The cost of quality is the cost of conformance or delivering products

that meet requirements and fitness for use the cost of nonconformance or taking

responsibility for failures or not meeting quality expectations

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Total Quality Cost

Price of nonconformance PONC Price of Conformance (POC)

peer walkthroughs, inspections development & impl. QMS, standards, training setting up & running of quality program

Cost of Quality (COQ) = POC + PONC

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Costs Per Hour of Downtime Caused by Software Defects

Business Cost per Hour Downtime

Automated teller machines (medium-sized bank) $14,500

Package shipping service $28,250

Telephone ticket sales $69,000

Catalog sales center $90,000

Airline reservation center (small airline) $89,500

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Five Cost Categories Related to Quality

Prevention cost: the cost of planning and executing a project so it is error-free or within an acceptable error range

Appraisal cost: the cost of evaluating processes and their outputs to ensure quality

Internal failure cost: cost incurred to correct an identified defect before the customer receives the product

External failure cost: cost that relates to all errors not detected and corrected before delivery to the customer

Measurement and test equipment costs: capital cost of equipment used to perform prevention and appraisal activities

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Organization Influences, Workplace Factors, and Quality Study by DeMarco and Lister showed that organizational

issues had a much greater influence on programmer productivity than the technical environment or programming languages

Programmer productivity varied by a factor of one to ten across organizations, but only by 21% within the same organization

Study found no correlation between productivity and programming language, years of experience, or salary

A dedicated workspace and a quiet work environment were key factors to improving programmer productivity