Total Quality Management.ppt

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MBA
Semester III
MB303
Unit 4
Total Quality Management

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 TQM Technologies and TQM Practices: Policy Deployment, Benchmarking,
Backtracking, Cross-Functional Teams, QFD, Taguchi Method, Failure-Mode
and Effect Analysis, The Poka-Yoke Concept,
 Concurrent Engineering, Malcolm Baldrige Award Model, TQM
Implementation Process, Introduction to World Class Manufacturing.
Unit 4: Total Quality Management (TQM) Practices

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Objectives
 Defining benchmarking and understanding its need
 Understanding the process of benchmarking
 Identifying the critical factors to success
 Understanding the current performance
 Planning the benchmarking exercise
 Studying and analyzing the results
 Understanding limitations and pitfalls

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Benchmarking
Benchmarking is the systematic search for best practices,
innovative ideas, and highly effective operating procedures.
Benchmarking considers the experience of others and uses it.

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Reasons to Benchmark
Benchmarking is a tool to achieve business and competitive
objectives. It is powerful and extremely effective when used for
the right reasons and aligned with organization strategy. Some of
the reasons are:
 Benchmarking can inspire managers (and organizations) to compete.
 Benchmarking allows goals to be set objectively, based on external
information.
 Benchmarking partners provide a working model of an improved
process, which reduces some of the planning, testing, and
prototyping effort. As the old saying goes, Why reinvent the wheel?
 Benchmarking enhances innovation by requiring organizations to
constantly scan the external environment and to use the information
obtained to improve the process.

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Process
Organizations that benchmark, adapt the process to best fit their
own needs and culture. Although the number of steps in the
process may vary from organization to organization, the following
six steps contain the core techniques.
 Decide what to benchmark.
 Understand current performance.
 Plan.
 Study others.
 Learn from the data.
 Use the findings

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Deciding What to Benchmark
Can be applied to virtually any business or production process. Improvement to best-in-
class levels in some areas will contribute greatly to market and financial success,
whereas improvement in other areas will have no significant impact.
Posers to decide high impact areas to benchmark are:
1. Which processes are causing the most trouble?
2. Which processes contribute most to customer satisfaction and which are not
performing up to expectations?
3. What are the competitive pressures impacting the organization the most?
4. What processes or functions have the most potential for differentiating our
organization from the competition?
 In deciding what to benchmark, it is best not to choose too large a scope
 Pareto analysis can be a helpful technique for deciding what processes to investigate.

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Understanding Current Performance
To compare practices to outside benchmarks, it is first necessary to
thoroughly understand and document the current process. It is
essential that the organization’s performance is well understood
 Several techniques, such as flow diagrams and cause-and-effect
diagrams can be used
 When documenting the process, it is important to quantify it
 Special care should be taken when using accounting
information. Most accounting systems were developed to satisfy
external reporting requirements to the tax and regulatory
authorities

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Planning
 The Benchmarking Team should do planning considering
following:
– what type of benchmarking to perform: internal,
competitive, and process.
– what type of data are to be collected
– the method of data collection.
– candidates to serve as the benchmark to be identified.
Identifying the best firms to find a benchmark is a research
project.
– timetables for each of the benchmarking tasks : Techniques
like Gant Chart, PERT, etc. can be effectively used
– The desired output from the study.

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Studying Others
Benchmarking studies look for either description of how best-in-class processes
are practiced or the measurable results of these practices. For this purpose,
internal sources, data in the public domain, original research, or—most likely—a
combination of sources are used.
 Three techniques for conducting original research are:
– Questionnaires: Questionnaires are particularly useful to ensure
respondent anonymity and confidentiality, when data are desired from
many external organizations and when using a third party to collect
information.
– Site visits: Site visits provide the opportunity to see processes in action
and for face-to-face contact with best-in-class operators. Site visits
usually involve a tour of the operation or plant followed by a discussion
period.
– Focus groups: Focus groups are simply panels of benchmarking partners
brought together to discuss areas of mutual interest. Most often the panels
are comprised of people who have some previous joint benchmarking
activity.

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Learning from the Data
Benchmarking studies can reveal three different outcomes.
 External processes may be significantly better than internal processes (a
negative gap). Negative gaps call for a major improvement effort.
 Process performance may be approximately equal (parity).
 The internal process may be better than that found in external organizations
(positive gap). The finding of a positive gap should result in recognition for
the internal process.
Following steps should be taken in in case of negative gaps
 Identifiable benchmark gaps must be described and quantified.
 Once best-in-class practices are described and understood, key process
measures should be quantified.
 When best-in-class processes have been described and quantified, additional
analysis is necessary to determine the root causes of the gaps

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Using the Findings
When a benchmarking study reveals a negative gap in performance, the objective is to change
the process to close the gap.
 The findings must translate to goals and objectives, and action plans must be developed to
implement new processes.
 Process changes are likely to affect upstream and downstream operations as well as
suppliers and customers. Therefore, senior management has to know the basis for and
payoff of new goals and objectives in order to support the change.
 These changes have to be considered and incorporated into the strategic planning process.
 When acceptance is gained, new goals and objectives are set based on the benchmark
findings
 The generic steps for the development and execution of action plans are:
1. Specify tasks.
2. Sequence tasks.
3. Determine resource needs.
4. Establish task schedule.
5. Assign responsibility for each task.
6. Describe expected results.
7. Specify methods for monitoring results.

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Benchmarking for Performance Evaluation
 Benchmarking is “the continual process of measuring products, services, and practices against
the toughest competitors or those companies recognized as industry leaders.” - Xerox
Corporation It involves openly learning how others do something better than one’s own
company so that the company not only can imitate, but perhaps even improve on its techniques.
 1. Identify the area or process - an activity to determine a business unit’s competitive
advantage. 2. Find behavioral and output measures of the area or process and obtain
measurements. 3. Select an accessible set of competitors and best-in-class companies against
which to benchmark, within or beyond completely different industries, but perform similar
activities. To improve its order fulfillment, Xerox went to L. L. Bean, the successful mail order
firm, to learn how it achieved excellence in this area. 4. Calculate the differences among the
company’s performance measurements and those of the best-in-class and determine why the
differences exist. 5. Develop tactical programs for closing performance gaps. 6. Implement the
programs and then compare the resulting new measurements with those of the best-in-class
companies.
 A survey by Bain & Company of 460 companies of various sizes across all U.S. industries
indicated that more than 70% were using benchmarking in either a major or limited manner.
Cost reductions range from 15% to 45%. Benchmarking can also increase sales, improve goal
setting, and boost employee motivation.


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Benchmarking for Performance Evaluation
contd…
 The average cost of a benchmarking study is around $100,000 and involves
30 weeks of effort.
 Manco, Inc., a small Cleveland-area producer of duct tape regularly
benchmarks itself against Wal-Mart, Rubbermaid, and Pepsico to enable it to
better compete with giant 3M.
 APQC (American Productivity & Quality Center), a Houston research group,
established the Open Standards Benchmarking Collaborative database,
composed of more than 1,200 commonly used measures and individual
benchmarks, to track the performance of core operational functions. Firms
can submit their performance data to this online database to learn how they
compare to top performers and industry peers (see www.apqc.org).

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Where to look at for inspirations
 Not necessary to confine oneself to own industry,
 Toyota adopted new inventory system benchmarking against a US supermarket
systems, Taichi Ohno, an official, learnt about inventory replenishment there,
and developed JIT (Just in Time), sometimes the copy is better than the
original!!!
 Xerox another contributor in Benchmarking. It bridged the gap (quality, costs,
time to market) with Canon, the respectable Japanese brand.

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Where to look at for inspirations
 The hardest part of benchmarking can be gaining access to other firms’ value chain
activities with associated costs. Typical sources of benchmarking information,
however, include published reports, trade publications, suppliers, distributors,
customers, partners, creditors, shareholders, lobbyists, and willing rival firms. Some
rival firms share benchmarking data. However, the International Benchmarking
Clearinghouse provides guidelines to help ensure that restraint of trade, price fixing,
bid rigging, bribery, and other improper business conduct do not arise between
partic- ipating firms.
 Due to the popularity of benchmarking today, numerous consulting firms such as
Accenture, AT Kearney, Best Practices Benchmarking & Consulting, as well as the
Strategic Planning Institute’s Council on Benchmarking, gather benchmarking data,
conduct benchmarking studies, and distribute benchmark information without
identifying the sources.

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Pitfalls and Criticisms of Benchmarking
 The most persistent criticism of benchmarking comes
from the idea of copying others.
 Benchmarking isn’t very helpful if it is used for processes
that don’t offer much opportunity for improvement.
 Benchmarking is also not a substitute for innovation

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Summary for Benchmarking
 The organizations which have intentions to grow and perform well should
measure themselves against the best industry practices. Benchmarking
provides a systematic approach to achieve this purpose.
 It primarily contains two elements, first, doing comparative performance
measure on the basis of well-established metrics and second, understanding
why their own performance differs from the targeted values.
 Benchmarking can be adapted to any business or production process. The
organization must indentify critical processes or business measures, which it
wants to benchmark and at the end achieve it.
 Several techniques are available to carry out the benchmark studies.
 Organizations must ensure that business ethics are maintained in obtaining
such data and should avoid copying the processes blindly.

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 QFD is linking the design of products or services to the processes that
produce them. QFD is a means of translating customer requirements
into appropriate technical requirements for each stage of product or
service development and production.
 Early industrial pioneers - Bridgestone Tyre and Mitsubishi Heavy
Industries (late 1960s and early 1970s ), they used quality charts that
take customer requirements into account in the product design process.
 In 1978 Yoji Akao and Shigeru Mizuno – early authors.
 House of quality - a conceptual map for inter-functional planning and
communications. “method to transform user demands into design
quality, to deploy the functions forming quality, and to deploy methods
for achieving the design quality into subsystems and component parts,
and ultimately to specific elements of the manufacturing process”- Dr.
Yoji Akao, combined his work in quality assurance and quality
control points with function deployment used in value engineering.
Quality Function Deployment (QFD)

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 QFD is designed to help planners focus on characteristics of a new or existing
product or service from the viewpoints of market segments, company, or
technology-development needs. The technique yields charts and matrices.
 QFD helps transform customer needs (the voice of the customer [VOC])
into engineering characteristics (and appropriate test methods) for a product or
service, prioritizing each product or service characteristic while simultaneously
setting development targets for product or service.
Quality Function Deployment (QFD) contd…

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23

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1. Operation Management for Competitive Advantage,Richard B Chase
F Robert Jacobs, Nicholas J Aquilano, Nitin K Agarwal, 11th Ed, Mc Graw
Hill
2. http://en.wikipedia.org/wiki/Quality_function_deployment, accessed on
18_08_2013
Acknowledgements:

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Taguchi Methods
 Dr. Genichi Taguchi of Nippon Telephones and Telegraph Company,
Japan has been identified with the advent of what has come to be
termed quality engineering.
 The Taguchi Loss Function is graphical depiction of loss developed
by the Japanese business statistician Genichi Taguchi to describe a
phenomenon affecting the value of products produced by a company. it
made clear the concept that quality does not suddenly plummet when,
for instance, a machinist exceeds a rigid blueprint tolerance. Instead
"loss" in value progressively increases as variation increases from the
intended condition. This was considered a breakthrough in describing
quality, and helped fuel the continuous improvement (Kaizen)
movement that since has become known as lean manufacturing.
 The goal of quality engineering is to move quality improvement efforts
upstream from the production phase to the product/process design
stage (off-line).

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Taguchi Methods contd…
 Taguchi has developed a method based on " ORTHOGONAL ARRAY "
experiments which gives much reduced " variance " for the experiment
with " optimum settings " of control parameters. Thus the marriage of
Design of Experiments with optimization of control parameters to obtain
BEST results is achieved in the Taguchi Method. "Orthogonal Arrays"
(OA) provide a set of well balanced (minimum) experiments and Dr.
Taguchi's Signal-to-Noise ratios (S/N), which are log functions of
desired output, serve as objective functions for optimization, help in
data analysis and prediction of optimum results.
 As his loss function demonstrates, his main concern is deviation of a
characteristic from its nominal value. Uncontrollable factors (noise) are
often responsible for this deviation and, therefore, Taguchi’s approach
to experimental design has as its goal the design of products/process
that are robust to these noise factors

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Traditional Quality Metric
Traditional Metric
 All products within specifications equality
good.
 All products outside specifications equally bad.
 USL (All products equally good)
Equallyunacceptable LSL

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L(y) = k(y-m) 2, y is data, m is target value,
L(y) is loss(consumer’s loss, producer’s
loss)

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 Taguchi has developed a method based on “ORTHOGONAL ARRAY "
experiments which gives much reduced " variance " for the experiment
with "optimum settings " of control parameters. Thus the marriage of
Design of Experiments with optimization of control parameters to obtain
BEST results is achieved in the Taguchi Method. "Orthogonal Arrays"
(OA) provide a set of well balanced (minimum) experiments and Dr.
Taguchi's Signal-to-Noise ratios (S/N), which are log functions of
desired output, serve as objective functions for optimization, help in
data analysis and prediction of optimum results.
 Optimization problems:
 [A] STATIC PROBLEMS : Generally, a process to be optimized has
several control factors which directly decide the target or desired value
of the output. The optimization then involves determining the best
control factor levels so that the output is at the the target value. Such a
problem is called as a "STATIC PROBLEM".

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 This is best explained using a P-Diagram which is shown
below ("P" stands for Process or Product). Noise is shown
to be present in the process but should have no effect on
the output! This is the primary aim of the Taguchi
experiments - to minimize variations in output even though
noise is present in the process. The process is then said to
have become ROBUST.

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 [B] DYNAMIC PROBLEMS : If the product to be optimized has a signal
input that directly decides the output, the optimization involves
determining the best control factor levels so that the "input signal /
output" ratio is closest to the desired relationship. Such a problem is
called as a "DYNAMIC PROBLEM".
This is best explained by a P-Diagram which is shown below. Again,
the primary aim of the Taguchi experiments - to minimize variations in
output even though noise is present in the process- is achieved by
getting improved linearity in the input/output relationship.

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 [A] STATIC PROBLEM (BATCH PROCESS OPTIMIZATION) : There
are 3 Signal-to-Noise ratios of common interest for optimization of
Static Problems;
 (I) SMALLER-THE-BETTER :
n = -10 Log10 [ mean of sum of squares of measured data ]
 This is usually the chosen S/N ratio for all undesirable characteristics
like " defects " etc. for which the ideal value is zero.
 Also, when an ideal value is finite and its maximum or minimum value
is defined (like maximum purity is 100% or maximum Temperature is
92K or minimum time for making a telephone connection is 1 sec) then
the difference between measured data and ideal value is expected to
be as small as possible. The generic form of S/N ratio then becomes,
 n = -10 Log10 [ mean of sum of squares of {measured - ideal} ]
 (II) LARGER-THE-BETTER :
 n = -10 Log10 [mean of sum squares of reciprocal of measured data]
 This case has been converted to SMALLER-THE-BETTER by taking
the reciprocals of measured data and then taking the S/N ratio as in the
smaller-the-better case.

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 (III) NOMINAL-THE-BEST :
square of mean
n = 10 Log10 -----------------
variance
 This case arises when a specified value is MOST desired, meaning that
neither a smaller nor a larger value is desirable.
 Examples are;
 (i) most parts in mechanical fittings have dimensions which are
nominal-the-best type.
 (ii) Ratios of chemicals or mixtures are nominally the best type.
 e.g. Aqua regia 1:3 of HNO3:HCL
Ratio of Sulphur, KNO3 and Carbon in gun-powder
 (iii) Thickness should be uniform in deposition /growth /plating /etching..

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Taguchi’s three stage design process
 System Design - create prototype product and process to produce it.
 Parameter Design - find settings of process and product parameters
which minimize variability.
 Tolerance Design - tradeoff between loss to consumer and
manufacturing costs

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Signal to Noise Ratios
 In the parameter design stage Taguchi makes use of designed experiments and signal to
noise ratios to determine the optimal parameter settings.
 The signal to noise ratios are derived from the Taguchi loss function.
 While Taguchi has proposed a large number of signal to noise ratios three are the most
widely used:
Nominal is Best:
Larger is Better:
Smaller is Better:








 2
2
log
10
s
y
SNN
















n
y
SN
n
i i
L
1
2
1
log
10
















n
y
SN
n
i
i
S
1
2
log
10

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Experimental Design
 Taguchi has designed a number of
orthogonal arrays to aid in the development
of experiments
 These arrays are essentially balanced
fractional factorial designs.

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Acknowledgements
All accessed on 30/08/2013.
 http://cs.anu.edu.au/courses/ENGN8101/Loss%20functions-
lecture%205.pdf
 http://www.ee.iitb.ac.in/~apte/CV_PRA_TAGUCHI_INTRO.htm
 http://www.ee.iitb.ac.in/~apte/CV_PRA_TAGUCHI.htm
 http://en.wikipedia.org/wiki/Taguchi_loss_function
 Wheelen Hunger Strategic Mgt - Pearson
 David Fred Strategic Mgt - PHI

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