Choosing the right type of subgroup in a control chart is
crucial. In a rational subgroup, the variability within a subgroup
should encompass common causes, random, short-term variability and
represent “normal,” “typical,” natural process variations, whereas
differences between subgroups are useful to detect drifts in
variability over time (due to “special” or “assignable” causes).
Variation within... Continue Reading
Parts 1 and
2 of Gauging Gage we looked at the numbers of parts, operators,
and replicates used in a Gage R&R Study and how accurately we
could estimate %Contribution based on the choice for each. In
doing so, I hoped to provide you with valuable and interesting
information, but mostly I hoped to make you like me. I mean
like me so much that if I told you that you were doing... Continue Reading
You run a capability analysis
and your Cpk is bad. Now what?
First, let’s start by defining
what “bad” is. In simple terms, the smaller the Cpk, the more
defects you have. So the larger your Cpk is, the
practitioners use a Cpk of 1.33 as the gold standard, so we’ll
treat that as the gold standard here, too.
Suppose we collect some data and run a capability analysis using
In Part 1 of Gauging Gage, I looked at how adequate a
sampling of 10 parts is for a Gage R&R Study and providing
some advice based on the results.
Now I want to turn my attention to the other two factors in the
standard Gage experiment: 3 operators and 2 replicates.
Specifically, what if instead of increasing the number of parts in
the experiment (my previous post demonstrated you would need... Continue Reading
"You take 10 parts and have 3 operators measure each 2
This standard approach to a Gage R&R experiment is so
common, so accepted, so ubiquitous that few people ever question
whether it is effective. Obviously one could look at whether
3 is an adequate number of operators or 2 an adequate number of
replicates, but in this first of a series of posts about
"Gauging Gage," I want to look at... Continue Reading
In its industry guidance to companies that manufacture drugs and
biological products for people and animals,
the Food and Drug Administration (FDA) recommends three stages for
Process Qualification, and Continued Process Verification. In
this post, we we will focus on that third stage.
Stage 3: Continued Process Verification
Per the FDA guidelines, the goal of... Continue Reading
To make objective
decisions about the processes that are critical to your
organization, you often need to examine categorical data. You may
know how to use a t-test or ANOVA when you’re comparing measurement
data (like weight, length, revenue, and so on), but do you know how to compare
attribute or counts data? It easy to do with statistical software
One person may look at
this bar... Continue Reading
People frequently have different opinions. Usually that's
fine—if everybody thought the same way, life would be pretty
boring—but many business decisions are based on opinion. And when
different people in an organization reach different conclusions
about the same business situation, problems follow.
Inconsistency and poor quality result when people being asked to
make yes / no, pass / fail, and... Continue Reading
The line plot is an incredibly
agile but frequently overlooked tool in the quest to better
understand your processes.
In any process, whether it's baking a cake or processing loan
forms, many factors have the potential to affect the outcome.
Changing the source of raw
materials could affect the strength of plywood a factory produces.
Similarly, one method of gluing this plywood might be better... Continue Reading
The ultimate goal of most quality improvement projects is clear:
reducing the number of defects, improving a response, or making a
change that benefits your customers.
We often want to jump right in and start gathering and analyzing
data so we can solve the problems. Checking your measurement
systems first, with methods like attribute agreement analysis or
Gage R&R, may seem like a needless waste... Continue Reading
We’ve got a plethora of case studies showing how businesses from different
industries solve problems and implement solutions with data
analysis. Take a look for ideas about how you can use data analysis
to ensure excellence at your business!
Boston Scientific, one of the world’s leading developers of
medical devices, is just one organization who has shared their
story. A team at their Heredia,... Continue Reading
There may be huge potential benefits waiting in the data in your
servers. These data may be used for many different purposes. Better
data allows better decisions, of course. Banks, insurance firms,
and telecom companies already own a large amount of data about
their customers. These resources are useful for building a more
personal relationship with each customer.
Some organizations already use... Continue Reading
This is an era of massive data. A huge amount of data is being
generated from the web and from customer relations records, not to
mention also from sensors used in the manufacturing industry
(semiconductor, pharmaceutical, petrochemical companies and many
Univariate Control Charts
In the manufacturing industry, critical product characteristics
get routinely collected to ensure... Continue Reading
In my last post, I discussed how a DOE was
chosen to optimize a chemical-mechanical polishing process in
the microelectronics industry. This important process improved the
plant's final manufacturing yields. We selected an experimental
design that let us study the effects of six process parameters in
Analyzing the Design
Now we'll examine the analysis of the DOE results after the
actual... Continue Reading
I used to work
in the manufacturing industry. Some processes were so complex that
even a very experienced and competent engineer would not
necessarily know how to identify the best settings for the
You could make a guess using a general idea of what should be
done regarding the optimal settings, but that was not sufficient.
You need very precise indications of the correct... Continue Reading
the roots of Lean Six Sigma and other quality improvement
methodologies are in manufacturing, it’s interesting to see how
other organizational functions and industries apply LSS tools
successfully. Quality improvement certainly has moved far beyond
the walls of manufacturing plants!
For example, I recently had the opportunity to talk to Drew
Mohler, a Lean Six Sigma black belt and senior... Continue Reading
When I wrote
How to Calculate B10 Life with Statistical
Software, I promised a
follow-up blog post that would describe how to compute any “BX”
lifetime. In this post I’ll follow through on that promise, and in
a third blog post in this series, I will explain why BX life is one
of the best measures you can use in your reliability
As a refresher, B10 life refers to the time at which 10% of... Continue Reading
If you need to assess process
performance relative to some specification limit(s),
capability is the tool to use. You collect some accurate
data from a stable process, enter those measurements in Minitab,
and then choose Stat > Quality Tools >
Capability Analysis/Sixpack or Assistant
> Capability Analysis.
Now, what about sorting the data?
I’ve been asked “why does Cpk change when I... Continue Reading
If you're just getting started in the world of quality
improvement, or if you find yourself in a position where you
suddenly need to evaluate the quality of incoming or outgoing
products from your company, you may have encountered the term
"acceptance sampling." It's a statistical method for evaluating the
quality of a large batch of materials from a small sample of items,
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