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| The Tools of Six SigmaMarch 31, 2004 Six Sigma is a buzzword that’s getting attention in all types of industries. The success of GE, Motorola, and others has prompted many an executive to declare that he/she wants “Six Sigma.” But what does that mean? What are the methods and tools of Six Sigma? Generally, Six Sigma is concerned with
reducing variability. The goal of Six Sigma is a six-sigma level of quality or 3.4
defects per million opportunities. To accomplish this, projects are identified
and prioritized. Then, project
teams attack the problems using the DMAIC method: Define, Measure, Analyze,
Improve, Control. Let’s talk
about each phase and the tools associated with each. Phase
1: Define
In this step, the problem is defined.
This includes identifying the CTQ’s, the critical-to-quality
characteristics as defined by the customer and identifying the key factors
affecting the CTQ’s. Phase
2: Measure
The measure phase consists of two major
steps. Step 1 is to identify the
internal processes that influence the CTQ measurements.
Step 2 is to conduct a measurement systems analysis, which includes gauge
studies and to perform a thorough process capability study. Phase
3: Analyze
In the analyze phase, the project team
determines which inputs are affecting the outputs.
This is done using the following three steps: Step 1: Develop and state a hypothesis
about the cause or causes. Step 2: Test the hypothesis through data
analysis Step 3: If your hypothesis is correct,
add your cause to a list of significant causes affecting the process. Phase
4: Improve
The improve phase involves quantifying
the effects of the key variables on the process and developing an improvement
plan that modifies the key factors to achieve the desired process improvement. Phase
5: Control
In the control phase, you put in place a
system to maintain the changes/improvements you developed in the Improve phase.
This phase involves documenting and monitoring processes. The
Tools
There are statistical methods as well as
qualitative tools that are used for process improvement using Six Sigma.
This article will explain some of the key tools in brief. 1.
Process Mapping or Value Stream Mapping
One of the most important steps in
process improvement is to map out the process in question. Process mapping is one way to accomplish this.
For the lean enterprise, Value Stream Mapping adds the information flow
dimension to this tool. In general,
this involves mapping your product and information flow throughout any given
process. This can be done in
manufacturing as well as administrative functions. 2. Basic Statistics These basic statistics tell the analyst
the central tendency and the dispersion of the data. They consist of the following for a given set of data: Mean: Sometimes
called the average or arithmetic mean, the mean is the sum of a
series of values divided by the number of values. Median:
The midpoint in a series of data points. Mode: The value that
occurs most often in a set of values. Range: The range
measures a spread of data points. Very
simply, it is equal to the largest value in a set of data minus the smallest
value. Variance:
The variance is the average squared
deviation of each number in a data set from the mean.
Standard Deviation:
The most common measure of spread, the standard deviation is the average
distance from the mean. It is equal
to the square root of the variance. Correlation: The
correlation is the degree to which two variables are related, measured in terms
of correlation coefficient. 3. Basic Graphical Analysis Histogram: The
histogram is a vertical bar graph representing the distribution of a given data
set. It is also known as a frequency
distribution bar graph. Scatter Plot: The
scatter plot is a graph in which individual data points are plotted in two
dimensions. Scatter plots
graphically show correlation. 4. Cause and Effect Diagram (Fishbone
Diagram) The Cause and Effect Diagram or Fishbone
Diagram is a graphical tool for identifying the relationship between a
problem and its potential causes. One
of the most effective ways of constructing such a diagram is to brainstorm
potential causes in a team environment. 5. XY Matrix The X-Y Matrix is used to link
customer CTQ’s to process inputs. It
is simply a group of rows and columns, with one set of increments marked along
the X axis and another set of increments marked along the Y axis. 6. Gauge R&R (Repeatability and
Reproducibility) A Gauge R&R ensures that
you’re measuring what you think you’re measuring.
IT looks at your units of measure and number of variables, calibrates the
measurement gauge, and then randomly selects samples to measure against
different operators. 7. Pareto Chart A Pareto Chart graphically
depicts the relative importance of causes, defects, and other aspects of a
process. It is used to identify factors in a process that have the greatest
cumulative effect. 8. Multivariate Analysis Multivariate Analysis offers
a way to reduce possible causes of variation in a process to a family of related
causes. Multivariate charts present
an analysis of three main sources of variation: intra-piece (within a single
piece or batch), inter-piece (piece-to-piece or batch-to-batch), and temporal
(time related). 9. Hypothesis Testing Hypothesis Testing
defines the problem, statistically tests data assumptions, selects samples, and
determines whether or not the probability of a defect is caused by random chance
or has a more tangible cause hidden in the process. 10. Failure Mode and Effects Analysis
(FMEA) FMEA is a
disciplined methodology that identifies potential failures and puts in place a
plan to prevent them from happening. There
are two types of FMEA’s that can be performed: Product or Design FMEA (DFMEA)
and process FMEA (PFMEA). DFMEA
relates to product failures and PFMEA relates to process failures. 11. Design of Experiments (DOE) DOE is a powerful
tool that systematically identifies and quantifies the effects of two or more
factors on the outcome of a process by experimenting with many factors and
variables simultaneously. 12. Control Chart The Control chart is the
fundamental tool of Statistical Process Control (SPC).
It determines whether a process is operating consistently (in control) or
if a special cause has occurred to change the process mean or variance. Click here to subscribe to our free e-newsletter Learning to Lean and receive three articles like this one each month. About the Author Darren Dolcemascolo is an internationally recognized lecturer, author, and consultant. As Sr. Partner and co-founder of EMS Consulting Group, he specializes in productivity and quality improvement through lean manufacturing. Mr. Dolcemascolo has written the book Improving the Extended Value Stream: Lean for the Entire Supply Chain, published by Productivity Press in 2006. He has also been published in several manufacturing publications and has spoken at such venues as the Lean Management Solutions Conference, Outsourcing World Summit, Biophex, APICS, and ASQ. He has a BS in Industrial Engineering from Columbia University and an MBA with Graduate Honors from San Diego State University. | ||||||||||||||
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