This document provides an overview of Six Sigma in 10 steps. It discusses key Six Sigma concepts like the normal distribution and defects per million opportunities (DPMO). The document explains that (1) processes can be measured and improved, (2) measurements typically follow a normal distribution, (3) most outcomes fall within 3 sigmas of the mean, and (4) Six Sigma aims for less than 4 defects per million by requiring 6 sigmas between the mean and specification limits. This achieves over 99.99966% success rate in meeting customer requirements.

1. SIX SIGMA IN 10
EASY STEPS
Professor & Lawyer
Puttu Guru Prasad
VVIT-Nambur
9394969898

3. NEED AND SCOPE OF SIX SIGMA
 Every generation of business strives for a new level
of quality.
 The quality program that is currently in vogue
and being widely used and recognized by industry
is the Six Sigma program.
 Six Sigma is a relatively new program, and was
only started in 1986.
 It was first put into implementation at Motorola, but
is now in use by most large corporations.
 Some of these other large companies include GE,
Honeywell, and Bank of America.

4. SOME STATISTIC BACKGROUND BEFORE
LEARNING ABOUT SIX SIGMA
You are probably already familiar with the concepts
of average, standard deviation, and Gaussian
distribution.
However, they are very important concepts in Six
Sigma, so they are reviewed in the next section.

5. AVERAG
E
 The equation for calculating an average is shown
below.
where
average
xi = measurement for trial i
N = number of measurements
This equation relates to Six Sigma because it is
the value that you aim for when you are creating
your product.

6. SIGNIFICANCE OF AVERAGE IN SIX SIGMA
 The average is combined with the specification
limits, which are the limits that determine if your
product is in or out of spec.
 The wider the specification limits are, the more
room for deviation from the average there is for
your product.
 A product specification would be written like this:
10 ± 2 mm
 Where the first number (10) represents the average
and the second number (2) represents the amount
of error allowable from the average. Thus, your
product can range from 8 to 12 mm for this
example.

7. STANDARD
DEVIATION
 The equation for standard deviation is shown below.
 where σ = standard deviation, and the other variables
are as defined for the average.
 For each measurement, the difference between the
measured value and the average is calculated.
 This difference is called the residual. The sum of the
squared residuals is calculated and divided by the
number of samples minus 1. Finally, the square root
is taken.

8. SIGNIFICANCE OF STANDARD DEVIATION FOR
SIX SIGMA
The standard deviation is the basis of Six Sigma.
The number of standard deviations that can fit
within the boundaries set by your process represent
Six Sigma.
The number of errors that you can have for your
process as you move out each standard deviation
continues to decrease.

9. DEFECTS PER MILLION AND SIX SIGMA TABLE
 The table below shows the percentage of data that falls within
the standard deviations and the amount of defects per
sigma, in terms of "Defects Per Million Opportunities" or
DPMO.
Defects per
100
Defects per
10000
Defects per
million
Success
rate
Sigma
Value (σ)
93 9330 933000 7% 0.0
69 6910 691000 31% 1.0
31 3090 309000 69.1% 2.0
7 668 66800 93.32 3.0
1 62 6210 99.379 4.0
2 233 99.9767 5.0
3.4 99.99966% 6.0

10. GAUSSIAN OR NORMAL DISTRIBUTION
 The normal, or Gaussian, distribution is a family of
continuous probability distributions
 These distribution functions are
defined by twoparameters:
"mean",μ),
a location
and a scale
(most
(most
commonly the
commonly the
"variance", σ2).
 It is observed that most random variable
Gaussian
distributions takes the shape of
distribution over a fairly long time.

11. GAUSSIAN DISTRIBUTION EXPLAINED
FURTHER…
 Above are 4 examples of different distributions given different
values for mean and standard deviation. An important case is
the standard normal distribution shown as the red line. The
standard normal distribution is the normal distribution with a
mean of 0 and a variance of 1. It is symmetrical about the
mean, μ.

12.  Suppose we have a process where we make a product of a certain
concentration and we have good control over the process.
 After analyzing a set of data from a time period we see that we
have a standard deviation of only 0.01 and our product concentration
is required to be within 0.05. In order to say our product is essentially
defect-free, 4.5 standard deviations away from the average must be
less than our required product tolerance (± 0.05). In this case 4.5
standard deviations is equal to 0.045 and our product tolerance is
0.05.

13. SIX SIGMA IN 10
EASY STEPS

14. 1.EVERYTHING WE DO IS A
PROCESS Absolutely everything that we do, at work or at play,
is a process. Each process has a start, a stop(and
therefore a time taken), inputs in from suppliers
and outputs out to customers, and things that
happen during the process steps.
 Business processes usually perform an action on
the main entity passing through the process, to
physically change it and add value in the eyes of
the customer.
 Manufacturers use processes to add value to
products, and service industries use processes to
deliver value-added services.

15. 2. EVERY PROCESS HAS MEASURABLE
CHARACTERISTICS
 Measurements can be of
input or output characteristics such as number,
 All of these processes change entities and such
changes can be measured.
size, weight or type.
 Measurements can be of continuous data items
such as time, money, size, or they can be of
discrete data items such as integer counts.
 The process itself will have requirements for the
inputs, and the customers will have requirements of
the outputs. Even if we are interested in measuring
such intangible things as customer satisfaction, we
can still do this using customer surveys.

16. 3. Measurements follow a frequency
distribution
 Frequency distributions are histograms showing how
many measurements fall within a given range of data.
 The range of the data is divided into 'bins' (normally
equally sized), and each data point is allocated to the
corresponding 'bin'. By plotting the number in each bin
against the data range, a frequency histogram will be
produced. With a lot of data, the overall envelope shape
can be clearly shown as a nice smooth curve.

17. 4. THE MOST COMMON FREQUENCY
DISTRIBUTION IS NORMAL DISTRIBUTION
 Many different types of distribution have been observed and
investigated. However there is one distribution which occurs
so often naturally that is has been named the Normal
Distribution because it is the one you will normally meet.
 The characteristics of the Normal Distribution are well
understood. The centre point is the mean or average (half the
measurements are above, and half below). The curve is like a
'bell shape' getting closer and closer to zero but never quite
reaching the line.
 The 'fatness' (variation) of the curve is measured by the
standard deviation

18. 5. MOST OBSERVATIONS FALL WITHIN THREE
SIGMA
The interesting thing about the Normal Distribution is that 68% of
all measurements fall within one sigma either side of the mean.
This is both mathematically proven and a practically experienced
result.
In fact, if you take all measurements that fall within three sigma of
the mean - that is between (mean + 3 sigma) to (mean - 3 sigma),
you will have 99.74% of all outcomes.
In practical terms - if you measure the shoe sizes of the entire
population, the plotted measurements will look like the Normal
Distribution, with a mean (M) and a sigma (S). Almost 100% of all
people will have shoe sizes from M-3S to M+3S, so if you make
shoes you can satisfy 99.74% of all your customers with just this
range of shoe sizes.

19. 6. CUSTOMERS HAVE EXPECTATIONS OF PROCESS
PERFORMANCE
Measure any process and you will find that almost
everything you measure looks like the Normal Distribution
Each measurement has variation, and that is a fundamental
fact of our universe.
Customers will have expectations about the outcome - such
as how long it takes, and how well the output suits their needs.
For example Customers of a bank expect to queue to reach
the cashier in perhaps 3 minutes or less.
Customer expectation can be determined, and processes can
be measured. How well do they match up?

20. 7. THREE SIGMA IS THE STANDARD
 Since we only miss 0.26% of the time if we aim for +/- (plus or
minus) three sigma, this has become the accepted standard
for manufacturing quality since about 1920.
 Manufacturers look at what the requirements are, and set the
process up so that the outcome has a mean and sigma to fit
within these requirements.
 If the customer has an upper and a lower limit on their
requirements or expectations, then the best situation is where
the mean is exactly between the customer limits, and the
distance between the mean and either limit is three
sigma.

21. 8. THREE SIGMA IS FAILURE 7% OF THE TIME
 Unfortunately life is not quite that simple.
 The problem is that perfection only lasts a short while, and
machines often change as parts wear or shift and the variation
begins to increase, so less and less of the outcome meets
customer requirements.
 Today manufacturing and services are becoming more and more
complex with hundreds of process steps and thousands of parts.
Each bit of the process may deliver at 99% success, but as each
part relies on what has gone before the failures soon multiply, and
only a very small fraction of the final product gets through without
any failure at all.
 In reality, three sigma often fails customer requirements 7% of the
time. This is not 99.74% as we might think but just 93% customer
satisfaction.

22. 9. SIX SIGMA MEANS FAILURE LESS THAN 4 IN A MILLION
Six Sigma quality does three major things to shake up the status quo:
1. It measures quality in terms of the number of standard
deviations (Sigma) between the mean and limits for a process
measure.
2It focuses totally on the customer, and lets the customer decide
what matters and lets the customer determine the acceptable
limits.
3It moves the target from three sigma to six sigma. That is a shift
from 66,700 to under 4 Defects Per Million Opportunities.
With the limits set by the customer (and not the process owner), and
with six standard deviations between mean and limits, failure is
experienced by the customer only 3.4 times in every million
opportunities, even when process wear and change is accounted for.
Six Sigma quality is about measurable total customer satisfaction.

23. 10. SIX SIGMA IS A PHILOSOPHY,
METHODOLOGY AND A QUALITY METRIC
 Six Sigma stands for a measure of customer quality - and it stands for a
philosophy of giving customers what they want each and every time
(zero defects, or as close as you can get). It also stands for a
methodology that can be used to change processes and company
culture to enable companies to deliver Six Sigma quality.
 Six Sigma quality methodology uses the very best from existing Total
Quality Management together with Statistical Process Control and
Measurement, and strong Customer Focus, and therefore impacts on
three key areas: the process, the employee, and the customer.

24. HOW TO IMPLEMENT SIX SIGMA - METHODOLOGIES
Six Sigma projects follow two project
methodologies These methodologies, composed of
five phases each, bear the acronyms DMAIC and
DMADV.
DMAIC is used for projects aimed at improving an
existing business process.
DMADV is used for projects aimed at creating new
product or process designs.

25. DMAIC
The DMAIC project methodology has five phases:
Define the system, the voice of the customer and their
requirements, and the project goals, specifically.
Measure key aspects of the current process and collect
relevant data.
Analyze the data to investigate and verify cause-and-effect
relationships. Determine what the relationships are, and attempt
to ensure that all factors have been considered. Seek out root
cause of the defect under investigation.
Improve or optimize the current process based upon data
analysis using techniques such as design of experiments, poka
yoke or mistake proofing, and standard work to create a new,
future state process. Set up pilot runs to establish process
capability.
Control the future state process to ensure that any deviations
from target are corrected before they result in defects.
Implement control systems such as statistical process control,
production boards, visual workplaces, and continuously monitor
the process.

26. DMADV
The DMADV project methodology, known as DFSS
("Design For Six Sigma"), features five phases:
Define design goals that are consistent with customer
demands and the enterprise strategy.
Measure and identify CTQs (characteristics that are
Critical To Quality), product capabilities, production
process capability, and risks.
Analyze to develop and design alternatives
Design an improved alternative, best suited per analysis
in the previous step
Verify the design, set up pilot runs, implement the
production process and hand it over to the process
owner(s).

27. DMAIC VERSUS DMADV

28. SIX SIGMA IMPLEMENTATION ROLES
Six Sigma identifies several key roles for its successful implementation.
Executive Leadership includes the CEO and other members of top
management. They are responsible for setting up a vision for Six Sigma
implementation. They also empower the other role holders with the freedom
and resources to explore new ideas for breakthrough improvements.
Champions take responsibility for Six Sigma implementation across the
organization in an integrated manner. The Executive Leadership draws them
from upper management. Champions also act as mentors to Black Belts.
Master Black Belts, identified by champions, act as in-house coaches on
Six Sigma. They devote 100% of their time to Six Sigma. They assist
champions and guide Black Belts and Green Belts.
Black Belts operate under Master Black Belts to apply Six Sigma
methodology to specific projects. They devote 100% of their valued time to
Six Sigma. They primarily focus on Six Sigma project execution and special
leadership with special tasks, whereas Champions and Master Black Belts
focus on identifying projects/functions for Six Sigma.
Green Belts are the employees who take up Six Sigma implementation
along with their other job responsibilities, operating under the guidance of
Black Belts.

29. CRITICISMS OF SIX SIGMA
 Lack of originality
 Role of consultants
 Over-reliance on (statistical) tools
 Stifling creativity in research environments
 Lack of systematic documentation
 Criticism of the 1.5 sigma shift