quality management

Quality ManagementQuality Management
““It costs a lot to produce a bad product.It costs a lot to produce a bad product.””
Norman AugustineNorman Augustine

Syllabus
• Evolution of quality Management;
• Concepts and objectives of quality management;
• Zero defects; Acceptance sampling; Quality inspection; Quality
assurance;
• Quality circles; training for quality; concept and tools of Six Sigma.
• Statistical quality control: basic concepts; product control; process
control; variations in quality;
• Control charts: types of control charts- mean charts, range charts, P-
chart, n p chart, C charts;
• Applications of control charts.

What is Quality?
• Meeting specificationsMeeting specifications
• Meeting customer needs /Meeting customer needs /
expectationexpectation
• Transparency of service deliveryTransparency of service delivery
• Process controlProcess control
• Achieving desired resultsAchieving desired results
• Continuous ImprovementContinuous Improvement
• Competitive advantageCompetitive advantage
• Added value for societyAdded value for society
• Best value for priceBest value for price
• Cost effectivenessCost effectiveness
• Performance measurementPerformance measurement
• More for lessMore for less
• Satisfaction of stakeholdersSatisfaction of stakeholders
• Doing the right thingsDoing the right things
• Doing things rightDoing things right
• Doing the right things rightDoing the right things right

Formal Definitions of Quality
• Transcendent definition: excellence
• Product-based definition: quantities of product
attributes
• User-based definition: fitness for intended use
• Value-based definition: quality vs. price
• Manufacturing-based definition: conformance
to specifications

Cost of quality
It is not the price of creating a quality product/service. It's the cost of
NOT creating a quality product/service.
1. Prevention costs: The costs of all activities specifically designed to
prevent poor quality in products or services
2. Appraisal costs: The costs associated with measuring, evaluating or
auditing products or services to assure conformance to quality
standards and performance requirements.
3. Internal failure costs: The costs occurring prior to delivery or
shipment of the product, or the furnishing of a service, to the
customer.
4. External failure costs: The costs occurring after delivery or shipment
of the product — and during or after furnishing of a service — to the
customer.
5. Opportunity costs: It is the cost of a missed opportunity

Evolution of Quality
• Dr Walter A Shewhart (1891 – 1967)
– Worked in Western Electric Company and AT&T, USA
– Advocated SQC and Acceptable Quality Level(AQL)
– AQL is the foundation of Six Sigma
– Developed control charts for quality assessment and improvement
– Also developed PDCA cycle for continuous improvement
• Deming W.Edwards (1900 -1993)
– Associate of Shewhart & a statistician
– Modified PDCA cycle of Shewhart to PDSA cycle
– Focused on product improvement and service conformance by reducing
variations in the process
– Stressed on the importance of suppliers and customers for the business
development and improvement
– Believed that people do their best and it is the system that must change to
improve quality
– 14 point principles for management to increase quality and productivity

Deming’s 14 Points
1. Create constancy of purpose for improving products and services.
2. Adopt the new philosophy.
3. Cease dependence on inspection to achieve quality.
4. End the practice of awarding business on price alone; instead, minimize total cost
by working with a single supplier.
5. Improve constantly and forever every process for planning, production and service.
6. Institute training on the job.
7. Adopt and institute leadership.
8. Drive out fear.
9. Break down barriers between staff areas.
10. Eliminate slogans, exhortations and targets for the workforce.
11. Eliminate numerical quotas for the workforce and numerical goals for
management.
12. Remove barriers that rob people of pride of workmanship, and eliminate the annual
rating or merit system.
13. Institute a vigorous program of education and self-improvement for everyone.
14. Put everybody in the company to work accomplishing the transformation.

Evolution of Quality (Contd)
• Joseph M.Juran (1904 – 2008): developed Western Electric SQC
Handbook. JUSE invited him to Japan in 1954
• Juran’s fitness of quality
– Quality of design – through market research, product and concept
– Quality of conformance – through management, manpower and technology
– Availability – through reliability, maintainability and logistic support
– Full service – through promptness, competence and integrity
• Juran’s Quality Planning Roadmap
– Identify your customers
– Determine their needs
– Translate them into your language
– Develop a product that can respond to the needs
– Develop processes, which are able to produce those product features
– Prove that the process can produce the product
– Transfer the resulting plans to the operating forces

• Philip B.Crosby (1926 – 2001) : 4 absolutes of quality
1. Quality is conformance to requirements, nothing more or nothing less and certainly not
goodness or elegance
2. Quality has to be achieved by prevention and not by appraisal
3. The performance standard must be zero defect and not something close to it
4. The measurement of quality is the price of non conformance, i.e how much the defects
in design, manufacture, installation and service cost the company. It is not indexes,
grade one or grade two.
• Armand V.Feigenbaum (1922 – 2014)
– Quality is in its essence a way of managing the organization
– Suggested methodology for cycle time reduction
• Feigenbaum’s cycle time reduction methodology
– Define process
– List all activities
– Flowchart the process
– List the elapsed time for each activity
– Identify non value adding tasks
– Eliminate all possible non value adding tasks

• Kaoru Ishikawa (1915 – 1989)
– Quality guru from Japan
– Advocated the use of cause and effect diagrams to provide a true
representation of the organizational impacts and procedures
– Developed fishbone diagram for cause and effect analysis

1980–TotalQuality1980–TotalQuality
1970–QualityManagementPrograms1970–QualityManagementPrograms
1960–QualityWarranty1960–QualityWarranty
1930–StatisticalControl1930–StatisticalControl
1920–Inspection1920–Inspection
1900–Supervision1900–Supervision
1900–Manpowerpredominance1900–Manpowerpredominance
1990-…-ExcellenceModels1990-…-ExcellenceModels
The century of Quality
2000-...2000-...

 Industrial Revolution
 mass production
 unit verification
 defective product
 Taylor's conception of work
 Measurement, comparison and
verification activities
 Focus on the quantity produced
1920 - Inspection

 Sampling inspection
 Use of statistical tools
 First concerns regarding
prevention:
 identification of causes for
defective products
 Focus on the finished product
1930 – Statistical Control

 Seven Basic Quality Tools:
 Flowcharts and Process Maps
 Check lists
 Cause-effect diagrams
 Pareto diagrams
 Histograms
 Scatter diagrams
 Control charts
1930 – Statistical Control

 First quality standards
 Customers’ specifications
 Preventive actions
 System’s approach
 Started the concern about
involving everyone in the
organization
 Focus on the manufacturing
process
1960 – Quality Warranty

 Evolution from the Quality
Warranty phase
 Integration of quality on global
management
 Quality Circles
 Audit
 Focus on the work process
1970 – Quality Management Programs

 Management Principles:
 Responsibility delegation
 Staff autonomy
 Satisfaction of needs and
expectations
 Struggle for improvement
 Adaptation needs
 Change management
 Focus on the organizational
process
1980 – Total Quality

 Quality Management System:
a set of organisational measures which transmit maximum
confidence that a given quality level is being achieved with the
adequate resource consumption
 Characteristics:
 External focus: at the client
 Global approach and as an integral component
of the organization strategy
 Horizontal vision within the organization,
from top management to staff
 Includes all the concerned parts
 Continuous learning and adaptation to change

Tools and methodologies:
 Re-engineering
 QFD – Quality Function Deployment
 Benchmarking
 Inquiries: clients and staff
 Brainstorming
 Balanced Scorecard

 Orientation guide
 Flexible and adaptable instrument
 Self-assessment and continuous
improvement models
 Support on the pathway to
excellence
 Focus on customer
1990 - ... – Excellence Models

History: how did we get here…
• Deming and Juran outlined the principles of Quality
Management.
• Taiichi Ohno applies them in Toyota Motors Corp.
• Japan has its National Quality Award (1951).
• U.S. and European firms begin to implement Quality
Management programs (1980’s).
• U.S. establishes the Malcolm Baldridge National
Quality Award (1987).
• Today, quality is an imperative for any business.

What is quality management all about?
Try to manage all aspects of the organization
in order to excel in all dimensions that are
important to “customers”
Two aspects of quality:
features: more features that meet customer needs
= higher quality
freedom from trouble: fewer defects = higher
quality

Quality Planning
Establish quality goals, identify customers, discover customer needs,
develop product features, develop process features, establish process
controls and transfer to operations
Quality Control
Choose control subjects, choose units to measure, set goals, create a
sensor, measure actual performance, interpret the difference and take
action on the difference.
Quality Assurance
It is another important factor. If the right plans are put into place and
then implemented in a timely manner, team leaders will be able to
ensure the quality that is expected is reached and sustained.
Quality Improvement
Prove the need, identify projects, organize project teams, diagnose
the causes, provide remedies which prove that the remedies are
effective, deal with resistance, change and control, and hold the
gains.

Zero Defects – a term coined by Mr. Philip Crosby in his book
“Absolutes of Quality Management” has emerged as a trending
concept in quality management; Six Sigma adopting it as one of the
major theories.
Zero defects in quality management don’t mean so in its literary term
but it refers to a state where waste is eliminated and defects are
reduced. It means ensuring quality standards and reducing defects to
the level of zero in projects.
Zero defects concept is a concept of quest for perfection in order to
improve quality. Though perfection might not be achievable but at
least the quest will lead towards improvement in quality.
Zero defects theory ensures that there is no waste existing in a
project. Waste here refers to all unproductive process, tools,
employee etc.

Acceptance Sampling
• Quality control is an activity in which measures are taken to control
quality of future output.
• Sampling refers to observation of a population or lot for the purpose
of obtaining some information about it.
• Acceptance sampling is a quality control technique.
• Acceptance sampling is defined as sampling inspection in which
decisions are made to accept or reject products or services.
• It is a decision making tool by which a conclusion is reached
regarding the acceptability of lot.

Acceptance Sampling : Advantages
• Acceptance sampling eliminates or rectifies poor lots & improve
overall quality of product.
• Reduces inspection costs & risk.
• In inspection of sample greater care will be taken so that results may
be more accurate.
• A rejected lot is frequently a signal to the manufacturer that the
process should be improved.
• It provides a no of alternative plans in which a single sample is taken,
two or indefinite no of samples may be taken from a single lot.

Quality Inspection
• Quality inspection are measures aimed at
checking, measuring, or testing of one or more
product characteristics and to relate the results to
the requirements to confirm compliance.
• This task is usually performed by specialized
personnel and does not fall within the
responsibility of production workers.
• Products that don't comply with the specifications
are rejected or returned to improve.

Importance of quality inspection
• Identification of the problem
• Preventing its occurrence
• Elimination of the problem

Types of quality inspections methods
• Quality inspection of product design - At the design stage,
verification or validation phase - refers to assessing the status of
compliance with the requirements enunciated by users or by the
designers. The resulting quality of the design is essentially
immeasurable and its evaluation is characterized by a considerable
objectivity.
• Quality inspection of the design process - At this stage the task of
inspection consists of checking whether accepted or held methods
and means of production, can produce quality performance in
accordance with the quality of design.
• Quality inspection in the production stage - Inspection used to
determine the compatibility of the resulting quality of the product
or fractional part of the documentation requirements contained in
the design or technology.

Types of quality inspections methods
• Thorough quality inspection - shall be carried out after completion
of all stages of the production process. Final product and its
compatibility with the standard design is subject of inspection.
• Inspection one hundred percent, - which consists of subjecting the
inspection of all units produced. Due to time-consuming, this
method is applied only to products manufactured individually or in
small series.
• Statistical inspection - a lot of statistical inspection is assessed on
the basis taken in a random sample. Therefore, this form of control
is called a sample inspection.
– Statistical inspection of the products
– Statistical process control (SPC)

Quality Assurance
• QA is a way of preventing mistakes or defects in manufactured
products and avoiding problems when delivering solutions or
services to customers.
• ISO 9000 defines quality assurance as "A part of quality
management focused on providing confidence that quality
requirements will be fulfilled"
• Quality Assurance refers to administrative and procedural activities
implemented in a quality system so that requirements and goals for
a product, service or activity will be fulfilled. It is the systematic
measurement, comparison with a standard, monitoring of processes
and an associated feedback loop that confers error prevention. This
can be contrasted with quality control, which is focused on process
output.

Quality Assurance
• Two principles included in Quality Assurance are: "Fit for
purpose", the product should be suitable for the intended purpose;
and "Right first time", mistakes should be eliminated. QA includes
management of the quality of raw materials, assemblies, products
and components, services related to production, and management,
production and inspection processes.
• Suitable quality is determined by product users, clients or
customers, not by society in general. It is not related to cost, and
adjectives or descriptors such as "high" and "poor" are not
applicable. For example, a low priced product may be viewed as
having high quality because it is disposable, where another may be
viewed as having poor quality because it is not disposable.

Quality Circle
• The term quality circles was defined by Professor Kaoru Ishikawa
• A quality circle is a group of workers who do the same or similar
work, who meet regularly to identify, analyze and solve work-related
problems.
• Normally small in size, the group is usually led by a supervisor or
manager and presents its solutions to management; where possible,
workers implement the solutions themselves in order to improve the
performance of the organization and motivate employees.
• Quality circles are generally free to select any topic they wish.

Statistica1 Quality Control (SQC)
• Statistica1 Quality Control (SQC) is the term used to describe the set
of statistical tools used by quality professionals to evaluate
organizational quality. SQC can be divided into three broad
categories:
– Descriptive statistics are used to describe quality characteristics and
relationships. Included are statistics such as the mean, standard deviation, the
range and a measure of the distribution of data.
– Statistical process control (SPC) involves inspecting a random sample of the
output from a process and deciding whether the process is producing products
with characteristics that fall within a predetermined range. SPC answers the
question of whether the process is functioning properly or not.
– Acceptance sampling is the process of randomly inspecting a sample of goods
and deciding whether to accept the entire lot based on the results. Acceptance
sampling determines whether a batch of goods should be accepted or rejected.

Variations in Quality
• No two products are exactly alike because of slight differences in
materials, workers, machines, tools, and other factors. These are
called common, or random causes of variation.
• Common causes of variation are based on random causes that we
cannot identify. These types of variation are unavoidable and are
due to slight differences in processing.
• Another type of variation that can be observed involves variations
where the causes can be precisely identified and eliminated. These
are called assignable causes of variation.
• Examples of this type of variation are poor quality in raw materials,
an employee who needs more training, or a machine in need of
repair.
• In each of these examples the problem can be identified and
corrected. Also, if the problem is allowed to persist, it will continue
to create a problem in the quality of the product.

Control Chart
• A control chart (process chart or quality control chart) is a graph
that shows whether a sample of data falls within the common or
normal range of variation.
• A control chart has upper and lower control limits that separate
common from assignable causes of variation.
• The common range of variation is defined by the use of control
chart limits.
• We say that a process is out of control when a plot of data reveals
that one or more samples fall outside the control limits.

Process Control Charts
• Control Charts show sample data plotted on a graph with
Center Line (CL), Upper Control Limit (UCL), and Lower
Control Limit (LCL).

Types of Control Charts
• Control chart for variables are used to monitor characteristics
that can be measured.
• e.g. length, weight, diameter, time, etc.
• Control charts for attributes are used to monitor characteristics
that have discrete values and can be counted.
• e.g. % defective, number of flaws in a shirt, number of broken
eggs in a box, etc.

Control Charts for Variables
• Mean (x-bar) charts
– Tracks the central tendency (the average value
observed) over time
• Range (R) charts:
– Tracks the spread of the distribution over time
(estimates the observed variation)

x-bar and R charts
monitor different parameters!

Constructing a X-bar Chart:
A quality control inspector at the Cocoa Fizz soft drink company has taken
three samples with four observations each of the volume of bottles filled.
If the standard deviation of the bottling operation is .2 ounces, use the
data below to develop control charts with limits of 3 standard deviations for
the 16 oz. bottling operation.
Time 1 Time 2 Time 3
Observation 1 15.8 16.1 16.0
Observation 2 16.0 16.0 15.9
Observation 3 15.8 15.8 15.9
Observation 4 15.9 15.9 15.8

Step 1:
Calculate the Mean of Each Sample
Observation 1 15.8 16.1 16.0
Observation 2 16.0 16.0 15.9
Observation 3 15.8 15.8 15.9
Observation 4 15.9 15.9 15.8
Sample means
(X-bar)
15.875 15.975 15.9

Step 2: Calculate the Standard Deviation of
the Sample Mean
x
σ .2
σ .1
n 4
 
= = = ÷
 

Step 3: Calculate CL, UCL, LCL
 Center line (x-double bar):
 Control limits for ±3σ limits (z = 3):
15.875 15.975 15.9
x 15.92
3
+ +
= =
( )
( )
x x
x x
UCL x zσ 15.92 3 .1 16.22
LCL x zσ 15.92 3 .1 15.62
= + = + =
= − = − =

An Alternative Method for the X-bar
Chart Using R-bar and the A2 Factor
Use this method when
sigma for the process
distribution is not known.
Use factor A2 from Table
Factor for x-Chart
A2 D3 D4
2 1.88 0.00 3.27
3 1.02 0.00 2.57
4 0.73 0.00 2.28
5 0.58 0.00 2.11
6 0.48 0.00 2.00
7 0.42 0.08 1.92
8 0.37 0.14 1.86
9 0.34 0.18 1.82
10 0.31 0.22 1.78
11 0.29 0.26 1.74
12 0.27 0.28 1.72
13 0.25 0.31 1.69
14 0.24 0.33 1.67
15 0.22 0.35 1.65
Factors for R-Chart
Sample Size
(n)

Step 1: Calculate the Range of Each
Sample and Average Range
Observation 1 15.8 16.1 16.0
Observation 2 16.0 16.0 15.9
Observation 3 15.8 15.8 15.9
Observation 4 15.9 15.9 15.8
Sample ranges
(R)
0.2 0.3 0.2
0.2 0.3 0.2
R .233
3
+ +
= =

 Center line:
 Control limits for ±3σ limits:
( )
( )
2x
2x
15.875 15.975 15.9
CL x 15.92
3
UCL x A R 15.92 0.73 .233 16.09
LCL x A R 15.92 0.73 .233 15.75
+ +
= = =
= + = + =
= − = − =

Control Chart for Range (R-Chart)
Center Line and Control Limit
calculations:
4
3
0.2 0.3 0.2
CL R .233
3
UCL D R 2.28(.233) .53
LCL D R 0.0(.233) 0.0
+ +
= = =
= = =
= = =
Factor for x-Chart
A2 D3 D4
2 1.88 0.00 3.27
3 1.02 0.00 2.57
4 0.73 0.00 2.28
5 0.58 0.00 2.11
6 0.48 0.00 2.00
7 0.42 0.08 1.92
8 0.37 0.14 1.86
9 0.34 0.18 1.82
10 0.31 0.22 1.78
11 0.29 0.26 1.74
12 0.27 0.28 1.72
13 0.25 0.31 1.69
14 0.24 0.33 1.67
15 0.22 0.35 1.65
Factors for R-Chart
Sample Size
(n)

Control Charts for Attributes: P-Charts & C-Charts
• Use P-Charts for quality characteristics that are discrete
and involve yes/no or good/bad decisions
– Percent of leaking caulking tubes in a box of 48
– Percent of broken eggs in a carton
• Use C-Charts for discrete defects when there can be more
than one defect per unit
– Number of flaws or stains in a carpet sample cut from a production run
– Number of complaints per customer at a hotel

Constructing a P-Chart:
A Production manager for a tire company has inspected the number
of defective tires in five random samples with 20 tires in each
sample. The table below shows the number of defective tires in
each sample of 20 tires.
Sample Sample
Size (n)
Number
Defective
1 20 3
2 20 2
3 20 1
4 20 2
5 20 1

Step 1:
Calculate the Percent defective of Each Sample and
the Overall Percent Defective (P-Bar)
Sample Number
Defective
Sample
Size
Percent
Defective
1 3 20 .15
2 2 20 .10
3 1 20 .05
4 2 20 .10
5 1 20 .05
Total 9 100 .09

Step 2: Calculate the Standard Deviation
of P.
p
p(1-p) (.09)(.91)
σ = = =0.064
n 20

CL p .09= =
 Center line (p bar):
( )
( )
p
p
UCL p zσ .09 3(.064) .282
LCL p zσ .09 3(.064) .102 0
= + = + =
= − = − = − =

Constructing a C-Chart:
The number of
weekly customer
complaints are
monitored in a large
hotel. Develop a
three sigma control
limits For a C-Chart
using the data table
On the right.
Week Number of
Complaints
1 3
2 2
3 3
4 1
5 3
6 3
7 2
8 1
9 3
10 1
Total 22

Calculate CL, UCL, LCL
 Center line (c bar):
UCL c c 2.2 3 2.2 6.65
LCL c c 2.2 3 2.2 2.25 0
z
z
= + = + =
= − = − = − =
#complaints 22
CL 2.2
# of samples 10
= = =

Six Sigma Quality
• A philosophy and set of methods companies use to
eliminate defects in their products and processes
• Seeks to reduce variation in the processes that lead to
product defects
• The name “six sigma” refers to the variation that
exists within plus or minus six standard deviations of
the process outputs
• A statistical concept that measures a process in terms
of defects – at the six sigma level, there 3.4 defects
per million opportunities.
σ6±

SIGMA LEVELS
Sigma Level ( Process
Capability)
Defects per Million
Opportunities
2 308,537
3 66,807
4 6,210
5 233
6 3.4

Six Sigma Roadmap (DMAIC)
Next Project Define
Customers, Value, Problem Statement
Scope, Timeline, Team
Primary/Secondary & OpEx Metrics
Current Value Stream Map
Voice Of Customer (QFD)
Measure
Assess specification / Demand
Measurement Capability (Gage R&R)
Correct the measurement system
Process map, Spaghetti, Time obs.
Measure OVs & IVs / Queues
Analyze (and fix the obvious)
Root Cause (Pareto, C&E, brainstorm)
Find all KPOVs & KPIVs
FMEA, DOE, critical Xs, VA/NVA
Graphical Analysis, ANOVA
Future Value Stream Map
Improve
Optimize KPOVs & test the KPIVs
Redesign process, set pacemaker
5S, Cell design, MRS
Visual controls
Value Stream Plan
Control
Document process (WIs, Std Work)
Mistake proof, TT sheet, CI List
Analyze change in metrics
Value Stream Review
Prepare final report
Validate
Project $
Validate
Project $
Validate
Project $
Validate
Project $
Celebrate
Project $

Tools used for continuous improvement
1. Process flowchart

2. Run Chart
Performance
Time

3. Control Charts
Performance Metric
Time

4. Cause and effect diagram (fishbone)
Environment
Machine Man
Method Material

5. Check sheet
Item A B C D E F G
-------
-------
-------
√ √ √
√ √
√ √
√
√
√ √
√ √ √
√
√
√
√
√ √

6. Histogram
Frequency

7. Pareto Analysis
A B C D E F
Frequency
Percentage
50%
100%
0%
75%
25%
10
20
30
40
50
60

Summary of Tools
1. Process flow chart
2. Run diagram
3. Control charts
4. Fishbone
5. Check sheet
6. Histogram
7. Pareto analysis

Case: shortening telephone waiting time…
• A bank is employing a call answering service
• The main goal in terms of quality is “zero waiting time”
- customers get a bad impression
- company vision to be friendly and easy access
• The question is how to analyze the situation and improve quality

The current process
Custome
r B
OperatorCustome
r A
Receiving
Party
How can we reduce
waiting time?

Makes
custome
r wait
Absent receiving
party
Working system of
operators
Customer Operator
Fishbone diagram analysis
Absent
Out of office
Not at desk
Lunchtime
Too many phone calls
Absent
Not giving receiving
party’s coordinates
Complaining
Leaving a
message
Lengthy talk
Does not know
organization well
Takes too much time to
explain
Does not
understand
customer

Daily
average
Total
number
A One operator (partner out of office) 14.3 172
B Receiving party not present 6.1 73
C No one present in the section receiving call 5.1 61
D Section and name of the party not given 1.6 19
E Inquiry about branch office locations 1.3 16
F Other reasons 0.8 10
29.2 351
Reasons why customers have to wait
(12-day analysis with check sheet)

Pareto Analysis: reasons why customers have to wait
A B C D E F
Frequency Percentage
0%
49%
71.2%
100
200
300 87.1%
150
250

Ideas for improvement
1. Taking lunches on three different shifts
2. Ask all employees to leave messages when leaving desks
3. Compiling a directory where next to personnel’s name
appears her/his title

Results of implementing the recommendations
A B C D E F
100%
0%
49%
71.2%
100
200
300 87.1%
100%
B C A D E F
0%
100
200
300
Before… …After
Improvement

In general, how can we monitor quality…?
1. Assignable variation: we can assess the cause
2. Common variation: variation that may not be possible to
correct (random variation, random noise)
By observing
variation in
output measures!

quality management

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