Module - 4 Continuous Process Improvement: process, the Juran trilogy, improvement strategies, types of problems, the PDSA Cycle, problem-solving methods, Kaizen, reengineering, six sigma, case studies. Statistical Process Control : Pareto diagram, process flow diagram, cause and effect diagram, check sheets, histograms, statistical fundamentals, Control charts, state of control, out of control process, control charts for variables, control charts for attributes, scatter diagrams, case studies

1. Total Quality Management
(18ME734)
Module - 4
1. Continuous Process Improvement: process, the Juran trilogy,
improvement strategies, types of problems, the PDSA Cycle, problem-
solving methods, Kaizen, reengineering, six sigma, case studies.
2. Statistical Process Control : Pareto diagram, process flow diagram,
cause and effect diagram, check sheets, histograms, statistical
fundamentals, Control charts, state of control, out of control process,
control charts for variables, control charts for attributes, scatter
diagrams, case studies
By:
D N Roopa
Assistant Professor
Department of Mechanical Engineering
JSSATE, Bangalore

2. Continuous Process Improvement
CONTENTS
1. Process
2. The Juran trilogy
3. Improvement strategies
4. Types of problems
5. The PDSA Cycle
6. Problem-solving methods
7. Kaizen
8. Reengineering
9. Six sigma
10. Case studies.

3. PROCESS
Input and Output Process Model

4. • Process: - Set of interrelated or interacting activities
that use inputs to deliver an intended result.
• Intended result: - output i.e., product or service
• Output: - Result of a process. The output is product or
service depending on the characteristic involved.
Example: Paining for sale in a gallery is a product
whereas supply of a commissioned paining is service.
Hamburger bought in retail store is a product where
as receiving an order and serving a hamburger
ordered in a restaurant is part of service.
• A process where the conformity (Conformity means
fulfillment of requirements) of the resulting output
cannot be readily or economically validated is
frequently referred to a a “Special process”

5. Improvement
An activity to enhance performance. The
activity may be recurring or singular.
Recurring activity to enhance performance
is Continual Improvement.
Can be achieved through:
• Eliminating waste
• Using benchmarking to improve
competitive advantage
• Use of SPC, QFD, Experimental design,
Quality by design, FMEA, TPM, TQE

6. Improvement is made by eliminating WASTE:
D Defects
O Overproduction
W Waiting
N Non utilized talent
T Transportation
I Inventory
M Motion
E Extra processing

7. D
Efforts caused by rework, scrap and incorrect
information
O
Production that is more than needed or before it is
needed
W Wasted time waiting for the next step in a process
N Underutilized peoples talent, skills & knowledge
T Unnecessary movement of products & materials
I Excess products and material not being processed
M Unnecessary movement by people (ex. Walking)
E Non value added processing

8. Approaches towards CPI
• Juran Trilogy – Quality improvement from
COST ORIENTED PERSPECTIVE
• Shewharts PDSA (PLAN-DO-STUDY-ACT) Cycle.
• KAIZEN – Focuses on making small
incremental improvements to the individual.
This is more behavioral approach.
• Re-engineering and six sigma.

9. The Juran Trilogy
QP –Quality
planning
QC - Quality Control
QI – Quality
Improvement

10. Juran developed the quality trilogy-- QUALITY PLANNING, QUALITY CONTROL AND QUALITY
IMPROVEMENT.
The Juran Trilogy
The Juran Trilogy – CONTD…

12. Quality planning

13. House of quality
Step1 – List customer requirements (WHATS)
Step 2 – List the technical descriptors (HOWS)
Step 3 – Develop a relationship matrix between
WHATS and HOWS
Step 4 – Develop an interrelationship matrix
between HOWs.
Step 5 – Competitive Assessment
Step 6 Develop prioritized customer requirement
Step 7 Develop prioritized technical descriptors

16. Quality by Design

17. Quality Control

19. Cost of quality
• It is used to present the performance
measure.
• It is an approach to measure and track
financial impact of various quality activities
• Reporting cost of quality can help in
prioritizing appropriate improvement
activities to minimize the over all cost

20. Cost of poor quality

21. Categories of quality cost
1. Internal failure cost – rework, fixing of bugs detected in
internal testing of software, premium freight due to late delivery, engg
drawings changes to correct errors, etc.
2. External failure cost – complaints, warranty claims, recall
costs, Liabilities and penalties, Allowances and customer goodwill, lost
sales, lost goodwill etc.
3. Appraisal cost – these are costs incurred while conducting
inspection, evaluations with the purpose of determining whether
product conforms to its stated requirements
4. Prevention cost – These are costs of all activities undertaken
to prevent defect in design, development, purchase, labor,
Cost of quality = 1+2+3+4

22. Relationship between Prevention +appraisal and failure
costs

23. Improvement Strategies
1. Repair
2. Refinement
3. Renovation
4. Reinvention
The Juran Trilogy – CONTD…

24. REPAIR
• Anything Broken must be fixed
Two levels of application
1. If a customer receives damaged product: FIX
IT (Temporary solution)
2. Eliminate the root cause of the problem (long
term solution)
The Juran Trilogy – CONTD…

25. Refinement
• Continually improve the process that is not
broken
• Incremental improvements in products,
processes, services
• A strategy for both individuals & teams
• Doing things just a bit quicker, better,easier or
with less waste
• Benefit: Little resistance from employees
• Drawback: Gradual change might not be
recognized or rewarded by
The Juran Trilogy – CONTD…

26. Renovation
• Major break through improvements
• Output may appear to be the same
• Innovation & technology advancements are
key factors in this approach
• More expensive
The Juran Trilogy – CONTD…

27. Reinvention
• Most demanding strategy
• Thinking process: Current approach will never
satisfy customer requirements
• A new output (product, service or activity) might
be developed
• Start with the imagination that previous system
does not exist
• Benefit: Potential competitive advantage
• Drawback: Potential resistance from employees
The Juran Trilogy – CONTD…

28. TYPES OF PROBLEMS
• Compliance (Specified by standards)
• Unstructured (Not specified by standards)
• Efficiency (from operations viewpoint)
• Process Design
• Product Design

29. PDSA /PDCA CYCLE
• Plan a change aimed at improvement
• Do – Carry out the change
• Check/Study the results
• Act - Adopt, adapt, or abandon

31. Plan: (1) Select Improvement
Opportunity
• Generate list and select
• Redefine team
• Write problem/opportunity/aim statement
• Management review and support

32. Plan: (1) Select Improvement Opportunity Common
Selection Criteria

33. Plan: (2) Analyze Current Situation
• Define process/problem to be solved – Identify the
customer(s).
• Baseline data
• Performance gaps? – Look at benchmarks, standards,
regulatory requirements
• Composition of team?
• Validate problem and statement
• Management review

34. Plan: (3) Identify Root Causes
• Very important step
• Analyze cause and effect relationships –
Fishbone diagrams
• Select root cause – Shared decision making
• Unbiased and reliable data to verify – Baseline
data
• Management review

35. Plan: (4) Generate and Choose
Solutions
• Generate list and select solutions
– Directly linked to root cause and supported by data
– Team brainstorming and shared decision making
– Consider best practices
– Be honest about barriers
– Change is hard!!
• Choose best solution based on criteria
– Shared decision making is key to buy-in!
• Define and map out solution
– Plan to measure (SMART-Specific, Measurable, Achievable,
Relevant, Timely objectives)
–Handoffs, Resources, outputs, accountabilities
• Management review

36. Do: (5) Map Out and Implement a Trial
Run
• Map out a trial run
– Communication and education/training are key
– Be specific
– New forms, handoffs, data etc.
• Implement trial run
– Small scale but representative
– Tests the intervention on a small scale to ensure
change will produce desired output

37. Check: (6) Analyze the Results
• Collect and evaluate results
– Team-based analysis and beyond
– Flexible and inclusive
– Objective and subjective data
– Revisit process as it was mapped out
– Be honest!

38. Check: (7) Draw Conclusions
• Team-based discussion and beyond
• Did the desired change occur?
– Did the intervention go as planned?
– Was the root cause eliminated?
– Are outcomes generalizable?
• What worked?
• What didn’t work?
• What could be improved/changed?
• What did we learn?

39. Act: (8) Adopt, Adapt, or Abandon the
Intervention
• Team-based discussion and beyond
• Adopt
– Test again on a larger scale?
– Communication, education, and training
– Plan to measure
• Adapt
– Revise plan and repeat trial
– Communication, education, and training
• Abandon
– Revisit root cause analysis and/or list of solutions
– Need additional/new members on the team?

40. Problem Solving: A Continuous Effort
1. Identify problems as an opportunity
2. Analyze the problem: to find root causes
3. Develop optimal and cost effective solutions
4. Implement changes: system wide
5. Study the results: worked or not? Need
adjustment?
6. Standardize solution: Keep problems from
reoccurring.

41. KAIZEN
• Japanese philosophy that defines role of
management in continuous improvement.
• Process of continuous improvement in small
increments that make process more efficient,
effective, under control and adaptable.
• Accomplished with little or no expense and
without using sophisticated techniques or
expensive equipment.

42. Kaizen improvement can be accomplished by:
• Elimination of all non value added work activities.
• Elimination of all MUDA (refers to eight wastes – over production,
delay, transportation, processing, inventory, wasted motion,
defective parts, non used talent).
• Use of principles of motion study, use of cell technology (group
technology).
• Principles of materials handling and use of one piece flow.
• Standardizing and Documentation
• 5-S in workplace
• Visual management
• Just in time principles
• Poka-Yoke to prevent or detect errors
• Team dynamics

43. Six sigma
• 1999, M Harry and R. Schroeder published
Six sigma: The breakthrough management
strategy revolutionizing the worlds top
corporations

44. Six sigma – A techque for measuring the PROCESS CAPABILITY of a process
Central tendency - to determine the center of a distribution of a data set. it is the single
value which is most representative of the entire data set.
Mean, Median, Mode
Measurement scale Best central tendency method
Interval & Ratio Mean for symmetrical data
Median for skewed data
Ordinal Median
Categorical data (Nominal) Mode

45. Measures of dispersion - Items in a data set tend to differ from each other and from the mean.
So, dispersion measures the extent to which different items tend to disperse away from the
mean.
•Range
•Variance
•Standard deviation - Standard deviation is the most popular measure of dispersion.
The symbol for the measurement of dispersion in a population is denoted by Greek letter
sigma σ.
•Coefficient of variation
•Inter Quartile range

46. A process that is normally distributed and centered with upper and lower
specification limits (USL & LSL) established at 6 sigma. 99.9999998% of the
products are in the limits and non conformance rate is 2 per billion. At this
level of 6 sigma Cp = 2

47. • Cp = (USL - LSL/ 6σ)
• Cpk = min(USL−mean / 3σ, mean−LSL/3σ)
• Cpk is a standard index to estimate the
capability of one process, the higher the Cpk
value the better the process is.
• For example, Machine 1 has a Cpk of 1.5 and
machine 2 has a Cpk of 1.2. From the Cpk value,
one can say that machine 1 is better than machine 2.
Cp and Cpk - To verify if the process can meet to meet Customer CTQs
(requirements).

49. We need to provide hands-on training for students. In the future, jobs will
become more data-driven. Mechanical engineers also need to know the data
generated by the processes. So that would be one additional skin that will go a
long way in giving a certain kind of job security for the students. Industries are
looking for smart people who are very flexible.

50. It is tool for quality control merely a scientific, data-driven
methodology for quality analysis and improvement.
It’s also termed as an industry-standard methodology for
measuring and controlling quality during the manufacturing
process.
Quality data in the form of Product or Process measurements
are obtained in real-time during manufacturing.
Graphical explanation helps to determine control limits. Control
limits are determined by the capability of the process, whereas
specification limits are determined by the client's needs.
51

51. Pareto analysis is a formal technique useful where many
possible courses of action are competing for attention.
Pareto analysis is a creative way of looking at causes of
problems because it helps stimulate thinking and organize
thoughts.
The value of the Pareto Principle for a project manager is that it
reminds you to focus on the 20% of things that matter. Of the
things you do during your project, only 20% are really
important. Those 20% produce 80% of your results. Identify
and focus on those things first, but don't totally ignore the
remaining 80% ofcauses.
52

52. There is need to classify the data according to root cause
problem
There is need to rank characteristics the financials or other
variable ifrequired
Collect the appropriate data for particular time frame
Summarize the data & rank the order for categories in
descending order
Construct the diagram &find the vital view
This is powerful quality management tool, it helps for
problem solving, identification and progress measurement
as well.
53

53. Purpose
:
Visual illustration of the sequence of operations required to
complete a task
 Schematic drawing of the process to measure or improve.
 Starting point for process improvement
 Potential weakness in the process are made visual.
 Picture of process as it shouldbe.
Benefits:
 Identify process improvements
 Understand the process
 Shows duplicated effort and other non-value-added steps
 Clarify working relationships between people and organizations
 Target specific steps in the process for improvement.
54

54. Benefits
• Simplest of all flowcharts
• Used for planning new processes or examining existing
one
• Keep people focused on the whole process
How is it done?
• List major steps
• Write them across top of the chart
• List sub-steps under each in order they occur
55

55. 56

56. Cause and Effect Analysis is a technique for identifying all the possible
causes (inputs) associated with a particular problem / effect (output)
before narrowing down to the small number of main, root causes which
need to be addressed.
Breaks problems down into bite-size pieces to find root cause
Fosters team work
Common understanding of factors causing the problem
Road map to verify picture of the process
Follows brainstorming relationship
57

57. Focusing on causes not symptoms capturing the collective knowledge
and experience of a group
Providing a picture of why an effect is happening
Establishing a sound basis for further data gathering and action
Cause and Effect Analysis can also be used to identify all of the areas that
need to be tackled to generate a positive effect.
It is also known as a Fishbone or Ishikawa diagram) graphically illustrates
the results of the analysis and is constructed in steps.
It is usually carried out by a group who all have experience and knowledge
of the cause to be analyzed.
It graphically display potential causes of a problem & relationship
between potentialcauses
58

58. 59

59. Use the Check Sheet to distinguish between opinions
and
facts
Use it to gather data about how often a problem is occurring.
Use it to gather data about the type of problem occurring.
Record types of writing errors on student writing samples
Examples
Excuses for late homework
Observations of the weather
Items found in a backpack
Amount of minutes practiced studying math facts
Amount of minutes spent reading each night
Amount of time spent completing homework
Prepared By: Muhammad Salman
Jamil 11

60. 61

61. 62

62. Purpose:
To determine the spread or
variation of a set of data points in
a graphical form
Stable process, exhibiting bell shape
How is it done?:
Collect data, 50-100 data point
Determine the range of the data
Calculate the size of the class
interval
Divide data points into classes
Determine theclass boundary
Count # of data points in each class
Draw the histogram
63

63. Histograms are a useful way to illustrate
the
frequency distribution of continuous data. For
example, the data in the table below show the lung
volume of a group of students.
15
Lung
volume
(litres)
Frequency
2.5–2.9 2
3.0–3.4 5
3.5–3.9 8
4.0–4.4 11
4.5–4.9 9
5.0–5.4 4
5.5–5.9 1

64. For some data sets the number of distinct values is too large to
utilize.
In such cases, we divide the values into groupings, or class
intervals.
The number of class intervals chosen should be a trade-off
between
(1)choosing too few classes at a cost of losing too much
information about the actual data values in a class and
(2)choosing too many classes, which will result in the
frequencies of each class being too small for a pattern to be
discernible.
Generally, 5 to 10 class intervals are typical.

67. Acollection of quantitative data pertaining to a subject or group.
Examples are blood pressure statistics etc.
The science that deals with the collection, tabulation, analysis,
interpretation, and presentation of quantitative data
Frequency Distribution
Measures of Central Tendency
Measures of Dispersion

68. The three measures in common use are the:
 Average
 Median
 Mode
• Average
• There are three different techniques available for
calculating the average three measures in common use are
the:
 Ungrouped data
 Grouped data
 Weighted average

69. Average Un-Grouped Data
Average Grouped Data

70.  Range
 Standard Deviation
 Variance
The range is the simplest and easiest to calculate of the measures of
dispersion. Range = R = XH – Xl (Largest value - Smallest value in data
set).
These tools are used to determine the dispersion in data, the smaller
the value of standard deviation the better the quality as distribution is
expected around central value. Quality control is one of the important
tool determine through principle control charts. The benefit of standard
deviation is required when there is need to have precise measurement.

71.  Population: Set of all items that possess a characteristic of
interest
 Sample
Parameter
: Subset of a population
is a characteristic of a population, it describes a
population. Example: Average weight of the population, e.g.
50,000 cans made in a month.
Statistic is a characteristic of a sample, used to make inferences
on the population parameters that are typically unknown,
called an estimator. Example: average weight of a sample of 500
cans from that month’s output, an estimate of the average
weight of the 50,000 cans.

72.  It is symmetrical -- Half the cases are to
one side of the center; the other half is
on the other side.
 The distribution is single peaked, not
bimodal or multi-modal also known as
the Gaussiandistribution
 Most of the cases will fall in the center
portion of the curve and as values of the
variable become more extreme they
become less frequent, with "outliers" at
the "tail" of the distribution few in
number. It is one of many frequency
distributions.

74. The control chart is a graph used to study how a process
changes over time. Data are plotted in time order.
A control chart always has a central line for the average, an
upper line for the upper control limit and a lower line for the
lower control limit.
Lines are determined from historical data. By comparing current
data to these lines, you can draw conclusions about whether the
process variation is consistent (in control) or is unpredictable
(out of control, affected by special causes of variation).

75. A run chart, also known as a run-sequence plot is a graph that
displays observed data in a time sequence. Often, the data displayed
represent some aspect of the output or performance of a
manufacturing or other business process.

76. It helps in determining the trend of data & indicate the variation of
quality.
The variation helps to understand central tendency and set of
observation related to central tendency and dispersion in data.
It help in assigning limits at different level of quality adjustments.

77. Following are the most commonly used variable control charts:
To track the accuracy of the process
Mean control chart or x-bar
chart
To track the precision of the process
Range control chart – R control
chart

78. The quality can be expressed in multiple basic units or
derived units of a particular product.
It relates to performance of a particular product & multiple
functions are involved in it such raw material, components or
finished goods etc.
There is need to prioritize theselection criteria in relation to
the product.
Sometimes the decision for cost saving opportunities reduce
the cost but it spoil rework cost.
Pareto Analysis would be effective tool for testing &product
inspection.

79. As discussed control chart are present to show multiple
subgroup in random manner, it need to limits within the group as
well. It would help to ensure the stability within the group. The
decision on particular sample size are considered as empirical
judgment.
1. As subgroup size increased it gets closer to central tendency.
2. When the size of subgroup increased it would increase
inspection cost.
3. It increase the cost of testing &item become expensive.
4. Due to computation the sample size with common features
within the industry are selected.
5. By using statistical distribution of subgroup averages taken
from non-normal population already proven by central limit
theorem.

81. Control limits, also known as natural process limits, are horizontal lines
drawn on a statistical process control chart, usually at a distance of ±3
standard deviations of the plotted statistic from the statistic's mean.

84. There is need to have amendments in regards when some points out-
of-control that needs to recalculate central lines &control limits.

86.  A process is considered to be in a state of control, or under
control, when the performance of the process falls within the
statistically calculated control limits and exhibits only chance, or
common, causes.
 When special causes have been eliminated from the process to the
extent that the points plotted on the control chart remain within
the control limits, the process is in a state of control cause a
natural pattern ofvariation.
 Type I, occurs when looking for a special cause of variation when
in reality a common cause is present
 Type II, occurs when assuming that a common cause of variation
is present when in reality there is a special cause
Prepared By: D N Roopa

87. 1. Individual units of the product or service will be more uniform
2. Since the product is more uniform, fewer samples are needed
to judge the quality
3. The process capability or spread of the process is easily
attained from 6ơ
4. Trouble can be anticipated before it occurs
5. The % of product that falls within any pair of values is more
predictable
6. It allows the consumer to use the producer’s data
7. It is an indication that the operator is performing satisfactorily

88. Natural pattern of variation
Common Cause
Special Cause

89. The term out of control is
considered when condition
arises for
considered when data
undesirable. It
lies
between 3∂. Below are some
conditions arises for out of
control processes.
1. Change or jump in level.
2. Trend or steady change in
level
3. Recurring cycles
4. Two populations
5. Mistakes

90.  The process spread will be referred to as the process capability and is
equal to 6σ i.e. +3σ & -3σ. The difference between specifications is
termed as tolerance
 When the tolerance is established by the design engineer without regard
to the spread of the process, undesirable situations can result
 Case I: When the process capability is less than the tolerance
6σ<USL-LSL
 Case II: When the process capability is equal to the tolerance
6σ=USL-LSL
 Case III: When the process capability is greater than the tolerance
6σ >USL-LSL

91.  The range over which the natural variation of a process occurs as
determined by the system of common causes measured by the
proportion of output that can be produced within design specifications.
 Following method of calculating the process capability assumes that
the process is stable or in statistical control:
 Take 25 (g) subgroups of size 4 for total of 100 measurements
 Calculate the range, R, for each subgroup
 Calculate the average range, RBar= ΣR/g
 Calculate the estimate of the population standard deviation
 Process capability will equal 6σ0

92. Case I: 6σ<USL-LSL
Case II: 6σ = USL-LSL
Case III : 6σ > USL-LSL

93.  Process capability and tolerance are combined to form the capability
index.
 The capability index does not measure process performance in terms
of the nominal or target value. This measure is accomplished by Cpk
 The Capability Index does not measure process performance interms
of the nominal or target

94. 1. The Cp value does not change as the process center changes
2. Cp= Cpk when the process iscentered
3. Cpk is always equal to or less than Cp
4. A Cpk = 1 indicates that the process is producing product that
conforms to specifications
5. ACpk < 1 indicates that the process is producing product that does
not conform tospecifications
6. ACp < 1 indicates that the process is not capable
7. ACp =0 indicates the average is equal to one of the specification
limits
8. Anegative Cpk value indicates that the average is outside the
specifications

95. Cpk = negative
number
Cpk = zero
Cpk = between 0
and 1
Cpk = 1
Cpk > 1

96. Used when the sample size is not the same
 Different controllimits for each
subgroup
 As n increases, limits become narrower
 As n decreases, limits become wider
apart
 Difficult to interpret andexplain
 To be avoided
Chart for Trends:
Used when the plotted points have an upward or
downward trend that can be attributed to an
unnatural pattern of variation or a natural pattern
such as tool wear. The central line is on a slope,
therefore its equation must be determined.

97. Used when we cannot have multiple observations per
time period. Extreme readings have a greater effect than
in conventional charts. An extreme value is used several
times in the calculations, the number of times depends
on the averaging period.
This is a simplified variable control chart.
 Minimizes calculations
 Easier to understand
 Can beeasily maintained by operators
 Recommended to use a subgroup of 3, then all data is used.

98. Formulae for Median & Range
Chart for Individual values (Moving)
Used when only one measurement
 Too expensive
 Time consuming
 Destructive
 Very few items

99. Six Sigma
• Six Sigma is a disciplined, statistical-based, data-driven
approach and continuous improvement methodology for
eliminating defects in a product, process or service.
• It was developed by Motorola and Bill Smith in the early
1980’s based on quality management fundamentals, then
became a popular management approach at General
Electric (GE) with Jack Welch in the early 1990’s.
• The approach was based on the methods taught by W.
Edwards Deming, Walter Shewhart and Ronald
Fisher among many others.
• Hundreds of companies around the world have adopted Six
Sigma as a way of doing business.

100. Six sigma ---contd..
• σ is the measurement of process dispersion called
standard deviation.
• A process established at 6 σ
99.9999998% of product or
service will be between specification.
• Nonconformance rate will be
0.002 per per million (2 per billion)

101. A defect refers to a flaw or discrepancy on an item
where more than one flaw (defect) can be
found.
For example, a hospital admission form contains
several fields of information that can be missing
or incorrect, so a given form can have more
than one defect. This means that a sample of 10
forms can show more than 10 defects.
• An item is said to be defective when the
decision is made that the item is not acceptable,
based either on one characteristic or the
accumulation of multiple defects.
This means that a sample of 10 items can show
a maximum 10 defective units.

102. Sigma calculation

103. 1. Cp value does not change as process center changes
2. Cp=Cpk when process is centered
3. Cpk is always equal to or less than Cp
4. A Cpk value greater than 1 indicates the process conforms
to specifications
5. A Cpk value less than 1 indicate process doesnot conforms
to specification
6. A Cp less than 1 indicate the process is not capable.
7. A Cpk value of zero indicate the average is equal to one of
the specification limits
8. A negetive Cpk value indicates that the average is outside
the specifications.