The Management and Control of Quality (5th Edition). South Western 2001.
Amitava Mitra. Fundamentals of Quality Control and Improvement (2nd Edition).
Quality Engineering Grahame Baker Medway School of Engineering Pembroke 237 Telephone 01634 883302
[email protected]
QUALITY ENGINEERING 1
Quality Concepts Statistical Concepts Process Improvement. Variation: Common and Special Causes of Variation. Prevention and Detection Systems. Graphical Techniques for Quality
2
Statistical Process Control Interpretation of SPC Information, Implementation, Advanced Techniques Attribute Data, Acceptance Sampling
3
Experimental Design Off-Line Quality Control, Factorial Designs. Analysis of Variance. Taguchi Methods, Quality Function Deployment.
QUALITY ENGINEERING 4
Reliability Engineering Life Cycle Curve and Probability Distributions for Modelling Reliability. System Reliabity. System Reliability. Operating Characteristic Curves. Life Testing Plans
5.
Quality Management Systems Standards and Standardisation. ISO 9000 Series Quality Management System. IS0 14000 Series Environmental Management System.
6.
Total Quality Management Concepts and Implementation of Total Quality Management. Integration with Design, Manufacture and Operations Management.
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REFERENCES Douglas Montgomery An Introduction to Statistical Quality Control (4th Edition) John Wiley, New York, 2000 James Evans and William Lindsay The Management and Control of Quality (5th Edition) South Western 2001 Amitava Mitra Fundamentals of Quality Control and Improvement (2nd Edition) Prentice Hall 1998
REFERENCES John Oakland Total Organisational Excellence Butterworth-Heinemann 1999 William Kolarik Creating Quality : Process Design for Results McGraw Hill 1999
QUALITY CONCEPTS
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WILLIAM EDWARDS DEMING (1900 - 1994)
WILLIAM EDWARDS DEMING u American
Statistician who worked with Shewart to the American war effort by teaching statistics and quality control techniques u 1947 sent to Japan (by the American Government) to help in the reconstruction of industry u 1950 invited back to Japan u Contributed
u 1960
Japanese create Deming Award for quality in his honour u 1980 “Discovered” in the USA (through a TV documentary) u 1980 -1994 Total Quality Guru giving seminars internationally
Deming's philosophy of management is so different from historic precedents that many managers are unable to make the transition. joy in work
understand variability
There are several underlying assumptions which are unacceptable to them.......... profit not enough
usually systems fault
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u
u
Most people really want to do a job. The fraction of people who do not want to take pride in their work is very small and are easily recognisable.
When things go wrong, the odds are at least 5 to 1 that the difficulty is in the system and not the people.
uWhen
variability is reduced, costs do go down, errors and mistakes become fewer, quality goes up, customers are happier, market share rises. Reduction in variability can be achieved with the assistance of the workers. The purposes of an enterprise matter. It is impossible to have everyone working harmoniously together for the same purpose if that purpose does not appeal to their hearts and minds. Simply making profit is not enough. u
Management by Positive Cooperation
not Management by Conflict
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The workers work in the system; the manager’s job is to work on the system and improve it with the workers help
We are here to come alive
thoughts concerning joy in work
Management's job is to create an environment where everybody may take joy in his work What is needed is profound knowledge People get rewarded for conforming, no wonder we are on the decline I used to say that people are assets, not commodities. But they are not just assets they are jewels
Management by Positive Cooperation
not Management by Conflict
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The workers work in the system; the manager’s job is to work on the system and improve it with the workers help
"There are no bad sailors, only bad officers." Earl Mountbatten of Burma
PLAN-DO-CHECK-ACT CYCLE
Plan Act Do Check for continuous improvement
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CORRECTIVE ACTION
Write down what you did
ACT
PLAN Justify what you do
Revise what you will do
CHECK
DO Do what you say
Review what you did
Record what you did (audit)
THE JOINER TRIANGLE Obsession with Quality
Scientific Approach
All one Team
THE TQM MODEL Management Commitment
Quality System (ISO 9000)
Teamwork
Tools (SPC etc)
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TOTAL QUALITY MANAGEMENT
TOTAL QUALITY
commitment
team work
tools
Conflict must be eliminated
TQM requires total integration, everyone working together for quality improvement
TQM IS PEOPLE DRIVEN
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The term Total Quality Control (TQC) was originally coined by A.V. Feigenbaum who defined it as: "Total Quality Control is an effective system for integrating the quality development , quality maintenance , and quality improvement efforts of the various groups in an organisation so as to enable marketing, engineering production, and service at the most economical levels which allow for full customer satisfaction."
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TQC is best described as an operating philosophy that is totally committed to quality. It focuses on continuous improvement through the participation of everyone in the company.
Total Quality Management is a management technique developed from Feigenbaum's original ideas and based on the teachings of the so called American quality gurus such as William Conway, Philip Crosby, Edwards Deming, and Joseph Juran.
I need TQM
They have formed consultancy type operations which use their philosophies and methods and have acted as consultants to some very large organisations.
The Customer is the next stage in the process
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Total Quality Management (TQM) is an approach to improving the effectiveness and flexibility of a business as a whole. It is essentially a way of organising and involving the whole organisation; every department, every activity, every single person at every level. For an organisation to be truly effective, each part of it must work properly together, recognising that every person and every activity affects, and in turn is affected by others.
Each part of an organisation has custumers, whether within or without and the need to identify what the customer requirements are, and then set about meeting them, forms the core of the total quality management approach.
Hope she can identify my customer needs
The three major components are: 1.
SYSTEMS
(based on a good international standard)
2.
TEAMS
(the councils, quality improvement teams, quality circles, corrective action teams etc)
3.
TOOLS
(for analysis, correlations, and predictions for action for continuous improvement to be taken, SPC etc)
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THE TQM MODEL Management Commitment
Quality System (ISO 9000)
Tools (SPC etc)
Teamwork
TEAMWORK
Several facets of TQM are summarised below: •
recognising customers and discovering their needs;
•
setting standards which are consistent with customers requirements;
•
controlling process and improving their capability;
•
establishing systems for quality;
•
management's responsibility for setting quality policy, providing motivation through leadership, and equipping people to achieve quality;
•
empowerment of people at all levels in the organisation to act for quality improvement.
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THE JOINER TRIANGLE Obsession with Quality
Scientific Approach
All one Team
DEMING'S FOURTEEN POINTS 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14.
Constancy of Purpose The New Philosophy Cease Dependence on Inspection End “lowest Tender” Contracts Improve Every Process Institute Training On The Job Institute Leadership Drive Out Fear Break Down Barriers Eliminate Exhortations Eliminate Targets Permit Pride of Workmanship Encourage Education Top Management's Commitment
DEMING’S TRIANGLE Management Commitment to Improvement (points 2, 14, 1)
Improve Interrelationships (points 9, 8, 10, 11, 12, 7, 4)
Apply the Statistical Methodology (points 6, 13, 5, 3)
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Applying the Statistical Methodology u We
live in a world of variation. We need to understand its sources and we need to understand a scientific method of predicting it, for reducing it and for controlling it. u Statistical thinking is an essential part of this scientific method. Statistical theory is the only way to deal with variation. u Continuous improvement means continuously solving the variation problem. but this relies on a successful marriage of theory and practice; experience is insufficient without theory. This theory needs to be taught. u There is no substitute for knowledge.
Applying the Statistical Methodology u Initial
training (point 6) and subsequent retraining and education (point 13) on statistical theories and techniques is the best investment for the future.
u Only
statistical methods can guarantee at all times, the necessary ongoing improvement in quality and productivity (point 5).
u Only
through these techniques can one build quality into the product and the process at the earliest stage possible, thus preventing subsequent errors, minimising costs and eliminating the need for mass inspection (point 3).
DEMING'S CONTRIBUTION TO QUALITY • • • • • • • • •
Understanding Variation - identification of special causes and common causes of variation Scientific approach to quality - using statistical tools etc Use of Joiner triangle - model for TQM Plan - Do - Check - Act cycle - feedback system basis of many quality assurance programmes including ISO 9000 Focus on people as the most important part of the system 14 Points for Management Management Commitment Education, Commitment and Obsession Joy in Work
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STATISTICAL TECHNIQUES FOR QUALITY CONTROL AND IMPROVEMENT
PROCESS CONTROL AND IMPROVEMENT
E M TI
IN CONTROL (variation from causes reduced)
IN CONTROL (special causes eliminated)
OUT OF CONTROL (special causes present)
EVERYTHING VARIES
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SOME BASIC STATISTICS
mean normal distribution standard deviation central limit theorem
Statistics u EVERYTHING
u Statistics
VARIES
can be defined as the study of variation
u Statistics
uses observations of real events to characterize and predict the variation of naturally occurring processes
u Statistical
Process Control is needed to predict or analyse the output of a combination of naturally occurring processes
Discrete and Continuous Distributions Data is observed as discrete data and can then be formed into a histogram (discrete distribution) A closely fitting theoretical distribution may then be used to model the histogram. This may be a discrete distribution such as the Binomial, or Poisson or more usually a continuous distribution such as the Normal, Beta or Exponential etc
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All processes involve some form of variation, for example the time taken to process a loan varies for each application or the diameters of manufactured components will vary. The variation may be large or very small but it will always be present. Statistics is the science of measuring and characterizing variation and Statistical Process Control (SPC) offers a method of identifying and controlling the variation within a process.
A BASIC STATISTICAL TOOLKIT
STATISTICAL VARIATION To describe the pattern of variation three simple measures are usually used: Location Spread Shape
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LOCATION
A measure of the “centre” or typical value of a distribution, usually the mean is used (but sometimes the mode or median)
MEASURES OF LOCATION The mean of a set of numerical observations is the sum of the set divided by the number of observations x = Σ xi /n
SPREAD A measure of the dispersion or scale of variation in a distribution, the range is the simplest measure but the standard deviation is a better estimate and is therefore preferred in most statistical applications.
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MEASURES OF SPREAD The most common measure of spread is the standard deviation:
(
∑ xi − x s= ( n−1)
)2
The variance is the square of the standard deviation however standard deviation is usually used in preference as it is in terms of unit measure.
SHAPE A measure of the pattern of variation (whether it is symmetric, peaked etc.). Measures include skewness and kurtosis. Often a certain type of distribution is associated with a certain shape - for example the Normal distribution is bell shaped, The Uniform distribution is rectangular, etc.
THE NORMAL DISTRIBUTION The normal distribution is a continuous distribution which is unimodal and symmetrical (sometimes described as bell shaped) and theoretically extends in both directions to infinity. Its properties are well known and is perhaps the most important of distributions because it may be used to characterize many naturally occurring processes.
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99.8 % of variation
-3sd
3sd mean
Using the mean and standard deviation of the data a normal distribution can be fitted. From the theory of the normal distribution it is possible to predict the proportion of variation that will fall between two limits . 95% of all occurrences from a normal distribution fall between +- 1.96 standard deviations of the mean and 99.8 of occurrences between +-3.09 (usually approximated to 3) standard deviations of the mean.
THE NORMAL DISTRIBUTION the normal law of error stands out in the experience of mankind as one of the broadest generalizations of natural philosophy. it serves as the guiding instrument in researches in the physical and social sciences and in medicine agriculture and engineering. it is an indispensable tool for the analysis and the interpretation of the basic data obtained by observation and experiment
THE CENTRAL LIMIT THEOREM uA
distribution of sample means tends to be normally distributed regardless of the shape of the population from which it is drawn. This is known as the CENTRAL LIMIT THEOREM and is of great importance in statistics.
u The
mean of the sample means will be equal to the mean of the population and the standard deviation (usually referred to as the standard error) will be equal to the standard deviation of the population divided by the square root of the sample size.
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CENTRAL LIMIT THEOREM Any Distribution Samples Distribution 1
Samples Distribution 2
Mean of Samples
Distribution 3
Mean of Samples Normal Distribution
Let’s see a Simulation of the Central Limit Theorem
THE CENTRAL LIMIT THEOREM u If
the population is normally distributed these relationships hold regardless of the sample size. For non-normal populations the approximation improves as the sample size increases.
u In
simulation studies a simulation is run several times and means are used so that use can be made of the central limit theorem. Properties of the normal distribution can then be used to analyse the results from the simulation irrespective of the original pattern of variation. Confidence limits for the normal distribution may be used to predict the percentage of the distribution which will fall between the limits
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THE HISTOGRAM uA
histogram is useful to see what distributional assumptions are reasonable for each variable and whether any outliers, groupings or other peculiarities are present.
u The
histogram is used to display continuous data. They are particularly useful when a large amount of data needs to be visulualised.
u Frequencies
are formed by grouping the data into
classes.
STATISTICAL PROCESS CONTROL •The concept of SPC was introduced by Shewhart in the 1920’s. •Deming developed Shewhart's ideas to address managements ultimate responsibility for quality. •The main objectives of SPC are to improve and ensure quality and to adopt a preventative rather than detective philosophy to reduce (and ultimately eliminate) waste as a result of rejections or other causes.
STATISTICAL PROCESS CONTROL
•All manufacturing processes exhibit variation due to special natural, inherent (common causes of ) variation and/or assignable (special causes of) variation •The power of the Shewhart technique lies in its ability to separate and identify the special causes from the common causes of variation.
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STATISTICAL CONTROL The goal of a process control system is to make economically sound decisions about actions affecting the process. This means balancing the risks of taking actions when no action is necessary against failing to take action when action is necessary. These risks must be handled in the context of the special causes and common causes of variation. A process is said to be operating in statistical control when th e only source of variation is common causes. Deming says: "A state of statistical control is not a natural state for a manufacturing process. It is instead an achievement, arrived at by elimination, one by one, by determined effort, of special causes of excessive variation."
Sources of Variation
Variation is usually separated into two types: Common Causes of Variation are inherent in the process and will occur in the same random pattern over a long period in time. Special Causes of Variation (sometimes called assignable causes) will change over time and are usually non-random in nature
Local Actions and Actions on the System Local Actions u Are
usually required to eliminate special causes of variation u Can usually be taken by people close to the process u Can correct about 15% of process problems
Actions on the System u Are
usually required to reduce the variation due to common causes u Almost always require management action for correction u Are needed to correct about 85% of process problems
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If variation is to be controlled it is important to be able to distinguish between common and special causes of variation. Usually special causes are easily rectified by the operator. eg replacing or sharpening a cutting tool. Common causes of variation are more difficult to rectify and usually require an improvement of some kind to the manufacturing process. This is the responsibility of management. The object of statistical process control is to eliminate all special causes of variation so that only common causes exits. When this is the case the process is said to be in a state of statistical control.
The Need for Process Control
Detection
- Tolerates Waste
Prevention - Avoids Waste
Prevention versus Detection In a traditional manufacturing environment quality control is used to inspect (or detect) the quality of the component after production. This is obviously a waste of recourses. It is far more effective to prevent unusable components from being made in the first place. This can be achieved by using the quality of the output as an indication of how when the manufacturing process is performing. If the sources of variation have been identified then the process may be adjusted using the quality output as a feedback control.
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People
Method
Equipment Environment
Material Information about Performance
O U T P U T
Action on the Output
A DETECTION BASED SYSTEM
Design of Product and Process
Action on the Process
People
Method
Equipment Environment
Material
O U T P U T
Information about Performance
Action on the Output
A PREVENTION BASED SYSTEM
CONTROL CHARTS Several types of control charts have been developed to analyze both variables and attributes. However all control charts have the same two basic uses. Using Shewhart's terms, they are: u
As a judgement, to give evidence whether a process has been operating in a state of statistical control, and to signal the presence of special causes of variation so that corrective action can be taken.
u
As an operation, to maintain the state of statistical control by extending the control limits as a basis for real time decisions.
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Deming’s Definition of the Two Causes of Variation Special Cause: detected by simple statistical test (example X & R charts), a failure of the specific production-worker, and to be corrected by HIM. Special causes afflict a specific worker or machine. They come and go.
Deming’s Definition of the Two Causes of Variation Common or environmental causes, faults of the system, common to all production workers, affecting them equally: There are usually a number of common causes in any production line. Common causes stay on the job until corrected or reduced. Simple statistical tests detect their existence and measure the magnitude of their combined effect on rejections. Elimination or reduction of common causes can be affected ONLY BY ACTION OF MANAGEMENT.
Construction of a Control Chart 3sd
process mean
-3sd
distribution of sample means should be normal (based on Central Limit Theorem)
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BENEFITS OF CONTROL CHARTS
uControl
charts are simple and effective tools to achieve statistical control. They lend themselves to being maintained at the job station by the operator. They give people closest to the operation reliable information on when action should be taken - and on when action should not be taken.
BENEFITS OF CONTROL CHARTS
uWhen
a process is in statistical control, its performance to specification will be predictable. Thus both producer and customer can rely on consistent quality levels, and both can rely on stable costs of achieving that quality level
BENEFITS OF CONTROL CHARTS After a process is in statistical control, its performance can be further improved to reduce variation. u
uThe
expected effects of proposed improvements in the system can be anticipated, and the actual effects of even relatively subtle changes can be identified through the control chart. uSuch
process improvements will:
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BENEFITS OF CONTROL CHARTS
Increase the percentage of output that meets customer expectations (improve quality), u
Decrease the output requiring scrap or rework (improve cost per good unit produced), and u
Increase the total yield of acceptable output through the process (improve effective capacity). u
BENEFITS OF CONTROL CHARTS Control charts provide a common language for communications about the performance of a process - between the two or three shifts that operate a process; between line production (operator, supervisor) and support activities (maintenance, material control, process engineering, quality control); between different stations in the process; between supplier and user; between the manufacturing/assembly plant and the design engineering activity. u
BENEFITS OF CONTROL CHARTS
uControl
charts, be distinguishing special from common causes of variation, give a good indication of whether any problems are likely to be correctable locally or will require management action. This minimises the confusion, frustration, and excessive cost of misdirecting problem solving efforts.
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CONTROL CHARTS FOR PROCESS DISPERSION It is recommended that control charts for dispersion should always be run, concurrent with the averages chart, to detect any changes in dispersion. The reasons for this are twofold:
CONTROL CHARTS FOR PROCESS DISPERSION Changes in dispersion may be due to assignable causes that need to be eliminated. In particular an increase in dispersion of a medium relative capability process will cause product to be outside specification limits. u
The control limits for the averages chart are calculated using a value for the true standard deviation. If the standard deviation changes these limits no longer apply. u
Check Sheet, Control Chart
Pareto Analysis Process Flow Diagram
Select Problem
Process Control Monitoring
Identify Process
SPC METHODOLOGY
Implement Corrective Action
Analyse Data Control Chart, Histogram
Analyse Problem Causes
Data Collection
Cause & Effect, Scatter Diagram
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CHARACTERIZATION OF STATISTICAL CONTROL
STATISTICS
SPC
The Past Predicts the Future Tomorrow
Today Yesterday When a process displays a reasonable degree of statistical control the output will be predictable. The variation in the product will be essentially the same day after day
When a process displays a reasonable degree of statistical control virtually all the product will be within the natural process limits. These limits may be considered to be the voice of the process
variation due to common causes lower natural process limits
upper natural process limits
The Natural Process Limits
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If the voice of the process is not properly aligned with the voice of the customer, then some fraction nonconforming may change from day to day, the basic fraction nonconforming will persist day after day.
Tomorrow In-Spec
Today
Out-of-Spec In-Spec Out-of-Spec
Yesterday In-Spec Out-of-Spec
Misalignment between Voice of Process and Voice of Customer
When a process displays a degree of statistical control both the conforming product and nonconforming product are consequences of the same set of common causes. Seeking a special explanation for the existence of the nonconforming product will be a waste of time variation due to common causes upper specification limit in spec material
out of spec
Common Causes Can Generate both Good and Bad Products
When a process displays a reasonable degree of statistical control, the only way to tackle the problem of nonconforming products is to work to bring the voices into alignment. Either the process will have to be modified, or the specifications will have to be changed. either shift the mean upper spec or reduce the variation upper spec or change the specs
Aligning the Process
upper spec
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When a process displays a reasonable degree of statistical control the product will vary within natural process limits without regard for the specification limits
spec limit
spec limit
natural process variation natural process limits
No Matter What the Specification - the Process can do no better then the Voice of the Process
CHARACTERIZATION OF A LACK OF STATISTICAL CONTROL Call for Super-Statistics Man Quick !
When a process displays a lack of statistical control the pattern of variation will be inconsistent day to day. the variation in the process, and the variation in the product, are said to be due both to common causes and assignable causes. Such a process will be unpredictable
An Unstable Process variation due to common causes excessive variation due to assignable causes
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Even if the past production has been 100 percent conforming, a lack of statistical control puts a question mark on all predictions. the data shown came from a production process which was out of control
Monday’s Production
Tuesday’s Production
Wednesday’s Production
?
?
?
?
?
Thursday’s and Friday’s Production
When a process displays a lack of statistical control the control chart will detect the presence of the assignable causes. Each and every signal on a control chart represents an opportunity to gain more insight into the process Evidence of assignable cause
Out of Control Points are Signals
Shewhart constructed the control chart in such a way that it will almost always be economical to spend the time to identify the assignable causes associated with out of control points Process is different and it is will be worthwhile to find out why!
Is Anybody Listening ?
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When a process displays a lack of statistical control, the product will vary without regard for either thenatural process limits - or the specification limits
spec limit spec limit
what was made today natural process limits
When a process is unstable about the best you can say is “ Tomorrow is Another Day”
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