Even if using Total Quality
Management as the main quality program represents. Prevalent facts in nowadays
industry, many companies are extending this kind of initiatives to incorporate
strategic and financial issue. Among these initiatives, one such methodology is
known as Six Sigma, which originated
from the Motorola Corporation. The Six Sigma
strategy involves extensive use of statistical techniques such as control charts, design of experiments,
response surface methodology etc. in order to minimize process variations
and product / service defects. These techniques need to be applied in a
structured manner. While reporting the process
improvement, Six Sigma teams use certain numeric values, known as Six Sigma Metrics. The most common
metrics are 'Defects per Million
Opportunities (DPMO)', 'Sigma Quality Level', and 'Yield'.
INTRODUCTION
OF SIX SIGMA:
Total Quality Management has become
a buzzword in the business management field all over the world. Its philosophy
and approach seem to have caught the imagination of organizational managers to
find themselves in the midst of intense competition and are thus concerned with
their survival. The concept however has not appeared overnight but has evolved
over a period of time.
Six sigma can be defined as “a Business process that allows organization
to drastically improve the bottom line by designing and monitoring everyday
business activities in ways that minimize waste and resources while increasing
customers stratifications”. Sigma is a Greek
letter used to represent standard deviation in statistics and six sigma stands for six standard
deviations from mean. It provides the techniques and tools to improve the
capability and reduce the defects in any process.
THE
ORGIN OF SIX SIGMA:
Six sigma was born in the 1980’s at Motorola. Bill smith, an engineer
at Motorola’s communication sector, was quietly working began the scenes as
studying the correlation between a product field life or reliability and how
often that product had been repair to the manufacturing process, finally as a
result of, Motorola began to improve the quality and simultaneously reduce th
manufacturing cost and save the profit of $ 2.2 billion dollars within a 4
years.
SIX
SIGMA PROCESS MODELS:
Six sigma
facilitated pro active approach through rigorous measurement. Although PDCA could be used for process
improvement, to give a new thrust six sigma was introduced with modified models
for process improvement. Some six sigma models are given bellow. For DMAIC is used to improving existing
process and DMADV is used to
employed the new products,
1. DMAIC
(Define, Measure, Analyze, Improve, Control)
2. DMADV
(Define, Measure, Analyze. Design, Verify)
1. DMAIC:
I.
Define
This is the first phase of the
process improvement effort. It is similar to the plan phase of PDCA cycle. During this phase, the six
sigma project is defined. During this phase plan for the garment production and
collect the information pertaining to the customer requirements.
II.
Measure
The
team identifies the key internal processes that influence critical to quality (CTQ) and measure the garment defects.
III.
Analyze
The team discovers the causes for
defects, they identify the key variables which cause the defect or which are
most likely to cost are create a process variations. For this purpose, cause
and effect diagram can be used, that is shown in fig: 1,
IV.
Improve
The
team identifies maximum permissible ranges of the key variables and proposes a
system for measuring deviations of the variables. The team modifies the process
to stay within the maximum permissible range of the performance of the key
variables. The process performance has to be monitored and measured. If it is
satisfactory, then it can be institutionalized. The solution has to be implemented on a pilot basis before large
scale application in the plant.
V.
Control
In
this phase, tools are put in place to ensure that the key variables remain
within the maximum permissible ranges continuously.
2. DMADV:
I.
Define
This phase is similar to
DMAIC. Six sigma team gets a charter of new
designs of garments.
II.
Measure
During this step, the
organization may need to use Quality
Function Deployment (QFD). It
will enable the organization to convert the voice of customer prioritized
technical requirements. Six sigma identifies
measures for each of the technical requirements identify through Quality Function Deployment (QFD). They should also define the
performance standards, i.e. the expected performance of the new process or
products.
III.
Analyze
In
this phase the team has to be design the concepts or top level design for the
new projects. They should generate various designs options. They should
evaluate and finally select the right option.
IV.
Design
This
stage, the detailed design of the garments or process is carried out. Detailed
design involved identifying the final details and identifying all the required
steps. The six sigma team will
evaluate available option before finalize the most suitable process methods.
Finally it is the system integration that takes place.
V.
Verify
This
is last steps in DMADV is
verification. At this stage, the functionality of garments or process of the
garment will be verified. The full scale verification of the design of the
garment is verified.
IMPLEMENTING
SIX SIGMA IN GARMENT MANUFACTURING:
The prevailing motto for Six Sigma is “Perfection is Possible”.
The subtitle would be “Even when things go wrong, the product is still good”.
There are two major qualifiers to the concept of perfection. The major concept
is that perfection specifically means that the product meets the customer's
accurately determined specification. Therefore perfect production of a size
“large” shirt, which has a specification of
42 inches ± ¾ inch, does not mean that all shirts produced measure 42” but rather that all shirts measure between 41 ¼ inches and 42 ¾ inches. The
second major concept of Six Sigma is that the variation of the output is very
slight. Thus, when size “large” shirts are produced the measurements of
multiple shirts will be so closely gathered around 42 inches that when
something goes wrong, such as bad cutting
or a gauge being miss adjusted, the end result is that shirts still measure
within the tolerance.
The rest of the Six Sigma process understands the statistics behind the charts and
controls, learning the methods to determine what is working properly, and how
to identify and fix problems that do occur. The concept as a statistical term
has been frequently used since the 1930's and gained popularity in Japan and
the USA beginning in the 1950's with Crosby, Deming, and Juran. However, Motorola is credited, by most, for
bringing the term to the general public in the 1980's. Oh yes, the statistical
term. Six Sigma manufacturing says
that the variation in the product is so slight that 99.73 % of the output falls within the tolerances. Actually, the
quality is so good, and multiple measurements so closely centered on the
specified number, that even when the measurement is off by three times the
standard deviation, in either direction, it is still within tolerance. Thus the
measurements fall within Six Sigma or six standard deviations of the mean and
the quality yield is 99.73%.
Controlling the change is often a
matter of “What gets measured and posted
is What Improves”. While computer programs can calculate the control limits
required to know when a process is out of control, the limits have less value
in an environment utilizing human operators. Manual calculations and charting
will allow quick recognition of changes in the average apparel plant.
MESUREMENT
OF ANALYTICAL PROCESSES IN SIX SIGMA:
The measurement and analytical
processes use tools and charts that are already available in many plants. These
include some capabilities within basic spreadsheet computer programs such as Check Sheets, Histograms, Pareto Charts, and
Scatter Diagrams. Flowcharts and Fishbone diagrams are easily drawn by
hand.
The actual analysis and improvements
come through human interpretation of the
information and a logical thought process. There are two areas within this
section that can be greatly assisted by the newer computer programs available.
The first item is the determination of whether changes observed are
statistically relevant, thus avoiding unnecessary adjustments to an existing
process. The second area, with less use in the apparel industry, is the Design
of Experiments capability to see what combination of changes will give the best
end result. Experienced and motivated people given the time for thought and
experimentation are the critical resource for analysis and improvement. Controlling the change is often a matter of “What
gets measured and posted is What Improves”. While computer programs can
calculate the control limits required to know when a process is out of control,
the limits have less value in an environment utilizing human operators. Manual
calculations and charting will allow quick recognition of changes in the average
apparel plant.
Should a company have people
certified as Green Belts or Black Belts? The answer is “YES, IF”. If the company needs the computer program, needs a show
of management support for Quality Improvement, or lacks personnel with Engineering or Quality Management training.
However, any company can reap significant financial benefits from a focused
quality program utilizing the statistical tools that have been available for
years. The requirement is to provide a motivated team of people with a
structured process, an agreed upon set of standards, an expectation.
STATISTICAL
PROCESS CONTROL:
The first reports on statistical quality
control of yarn manufacturing products appeared during the late 1940’s
and 1950’s. These documents emphasized product quality and defect
detection rather than defect prevention. At that time, quality assurance was
very much a departmentalized function. Unfortunately for many companies in the
textile industry, this condition still exists.
In spun-yarn manufacturing, testing
focused on three areas: end-product testing of characteristics such as linear density, twist, strength and elongation,
short-term evenness, and count variation; inspection of defects such as thick
and thin places, slubs and neps, and repeating faults like mechanical
errors or drafting waves; and frequency checks for end breaks during spinning.
In recent years, companies have begun to focus on electronic monitoring of
processing weights, faults and running performance.
However, one of the problems many
companies using electronic monitoring and controls face is that the adjustments
are made on predetermined target values rather than through statistical control
limits. This often leads to far more variation when over control is added to
the process.
CONCLUSION
During
implementation of six sigma is in garment manufacturing, we can reduce the garment cost, wastage in the garment and
get higher production and good quality of the garment. Six Sigma initiatives
aim at reduction of process variations and defects. Statistical process control (SPC)
and Engineering process control (EPC) are two important techniques for
achieving these goals.
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