3. 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.
4. Six Sigma strategies seek to improve the quality of the output of
a process by identifying and removing the causes of defects and
minimizing impact variability in manufacturing and business
processes.
It uses a set of quality management methods,
mainly empirical, statistical methods, and creates a special
infrastructure of people within the organization who are experts in
these methods.
Each Six Sigma project carried out within an organization follows a
defined sequence of steps and has specific value targets,
for example: reduce process cycle time, reduce pollution, reduce
costs, increase customer satisfaction, and increase profits.
5. The term Six Sigma (capitalized because it was written that way
when registered as a Motorola trademark on December 28, 1993)
originated from terminology associated with statistical modeling of
manufacturing processes.
The maturity of a manufacturing process can be described by
a sigma rating indicating its yield or the percentage of defect-free
products it creates—specifically, to within how many standard
deviations of a normal distribution the fraction of defect-free
outcomes corresponds.
Motorola set a goal of "six sigma" for all of its manufacturing.
6. WHO CAN IMPLEMENT SIX SIGMA ?
Pharmaceutical Companies, Airline Manufacturing Organizations,
Automobile Manufacturers, among others are bound to work at a
sigma level which is either 6σ or more than that. If they are not able
to perform at this efficiency, the organization cannot exist.
Think about it, you are in the air, 5000 feet above the ground,
flying in a Boeing 777 Aircraft and suddenly a nut-bolt in the wing
of the plane loosens (probably due to manufacturing defect) making
it difficult for the pilot to steer the flight!
This is the only reason why defects are not welcome and
organizations try to achieve higher Sigma levels.
7. The term "six sigma" comes from statistics and is used in statistical
quality control, which evaluates process capability.
Originally, it referred to the ability of manufacturing processes to
produce a very high proportion of output within specification.
Processes that operate with "six sigma quality" over the short
term are assumed to produce long-term defect levels below
3.4 defects per million opportunities (DPMO).
8. METRICS
The primary metrics used in the Six Sigma approach are
Defects per Unit (DPU), Defects per Opportunity (DPO) and
Defects per Million Opportunites and Parts per Million
Defectives.
‘Opportunity’ in the Six Sigma methodology refers to the
chances to commit errors. The chances are nothing but the
possibilities or probabilities
9. What is DPO?
So, what is DPO? It is the ratio of the number of defects in a
sample to the total number of defect opportunities.
As a result, the ratio here helps you count the average number
of defects which occur in the total number of opportunities in a
sample group.
10.
11. Do you know how to calculate DPO? Here is a quick
defect opportunity calculation example.
Mr. X has got the business of printing visiting cards. Each order is
considered as a unit. Each order has four defect opportunities i.e.
incorrect, typo, damaged or incomplete. Fifty orders have been
randomly selected and inspected. Out of the fifty orders, six orders
have a problem. Two orders are incomplete (two defects), one order is
both damaged and incorrect (two defects), and three orders have typos
(three defects. Therefore, there are a total seven defects out of fifty
orders that have been sampled.
12. The formula is the total number of defects divided by the total
number of units sampled or inspected multiplied by the number of
defect opportunities per unit.
Therefore; DPO is equal to seven divided by two hundred (fifty
times four). The answer is 0.035.
The conclusion is: On an average, each unit of a product contains
0.035 opportunities for committing defects or errors. In other
words, each set of hundred orders would contain 3.5 defect
opportunities.
13. Mean is the arithmetic average of a process data set.
Central tendency is the tendency of data to be around this mean.
Standard Deviation (also known as Sigma or σ) determines the
spread around this mean/central tendency.
The more number of standard deviations between process
average and acceptable process limits fits, the less likely that the
process performs beyond the acceptable process limits, and it
causes a defect.
This is the reason why a 6σ (Six Sigma) process performs
better than 1σ, 2σ, 3σ, 4σ, 5σ processes.
14. LSL AND USL
LSL and USL stand for “Lower Specification Limit” and “Upper
Specification Limit” respectively. Specification Limits are derived
from the customer requirements, and they specify the minimum and
maximum acceptable limits of a process.
For instance in a car manufacturing system the desired average
length (Mean length) of car door can be 1.37185 meter. In order to
smoothly assemble the door into the car, LSL can be 1.37179 meter,
and USL can be 1.37191 meter. To reach a 6σ quality level in such a
process, the standard deviation of car door length must be at most
0.00001 meter around the mean length.
Sigma is also the capability of the process to produce defect free
work. Higher the capability, lower the defects.
15.
16. In the above figure, the red curve indicates a 2σ level of
performance where we observe that its peak is very low (fewer
outputs are around the desired average) and the variation is from
extreme left to extreme right of the figure.
If the process improves from 2σ to 3σ (green curve), you will
observe that the process variation reduces and the process has a
larger peak (more outputs are around the desired average, but a
different average than red curve).
17.
18. As the process performance increases from 3σ to 6σ (blue
curve), the process becomes centered between the upper and
lower specification limits and does not have much variation.
Here with blue curve the majority of process outputs are
around the desired average. This is why it is good and it causes
less defects beyond the lower and upper specification limits.
19. In the above table, you will observe that as the Sigma level
increase the Defects decrease.
For example, for a 2σ process the Defects are as high as
308,537 in one million opportunities. Similarly, for a 6σ
process the Defects is as low as 3.4 in one million
opportunities.
The 2σ performance level will have more defects than a
system in 6σ performance level as the standard deviation for a
2σ process is much larger than the standard deviation for a 6σ
process.
