Achieving Op Ex Michael Ray Fincher copyright 2016

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ACHIEVING OPERATIONAL
EXCELLENCE
BY BUILDING SUSTAINABLE FOUNDATIONS
The top ten most typical failures organizations experience when
building and sustaining a lean environment
Copyright © 2016 Michael Ray Fincher
All rights reserved
ISBN:4863425498

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MICHAEL RAY FINCHER
ACHIEVING OPERATIONAL
EXCELLENCE
BY BUILDING SUSTAINABLE FOUNDATIONS
The top ten most typical failures organizations experience when building
and sustaining a lean environment

3
Copyright © 2016 Michael Ray Fincher
All rights reserved
ISBN: 4863425498

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“Excellent environments need excellent
people to develop them and to sustain their
longevity.”
“In order for our organization to achieve
excellence, we must all make contributions.”
“Every employee matters, it’s our obligation
as part of the team to prove it.”
“First, everyone must contribute
(everyone!), secondly everyone is
accountable.”
Acknowledgments
Angela Staup – Thank you for editing this project!

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INTRODUCTION
Wouldn’t it be awesome if we could just flip a switch and create
a working environment of excellence where every system we have
in place, every process we use to make our products was a world
class lean environment right out of the gate? Many organizations
have attempted to do just that only to fail in the long term. Many
have attempted to implement fully operational lean manufacturing
environments that encompass all aspects of the business only to fail
at some point or another along the way. Even though they worked
feverishly toward developing their foundations to support the pillars
of lean, they came up fruitless in the end. Without a strong
foundation it becomes difficult if not impossible to grow an
organization to its’ fullest potential and to successfully execute the
processes needed to form a lean environment. The foundation of the
organization must have a specific structure that will allow for the
interaction of processes and those processes must be fully supported
by the systems on which they are built.
For example, years ago I worked with a company that had
implemented almost every tool in the lean toolbox. They were on
their way to reaching a true lean environment and achieving
excellence in every aspect of their business. This company had even
started to benefit from their efforts on a grand scale. They had
improved quality, delivery, process performance, and had even
provided their customer’s with some cost downs. They were on their

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way to the top! Recently, I had the opportunity to revisit that
company again and was shocked at what I had witnessed. It was in
worse condition than it was before they initially launched their lean
initiatives. What happened? After making a few observations I could
clearly see their catastrophic failures starring them right in the face.
It was simple! They failed to implement their improvements at the
systems level so it only took a management change to bring it all
crashing down around them. The new manager came in with his
own agenda and had no problem tearing down the walls because
there was no system foundation to rigidly support all the lean
initiatives they had so feverously built.
In my last book Problem Solving the Solution to the Puzzle
published a few years ago, I provided a very simplistic yet powerful
approach for solving even the most complex problems. The Define,
Measure, Analyze, Improve, Control (DMAIC) methodology has
helped many organizations map out their problem solving
approaches, work systematically to solve simple and complex
problems, and has allowed them to involve all of their employees
in the problem solving process. Most importantly, this book teaches
how to solve the problem at the systems level so the improvements
will sustain (even if the people or products change). In my career I
have been very fortunate because I have been able to work with
some very large, very successful, and very powerful organizations
where their processes were solidly built on strong foundations and

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were always able to solve problems at the systems level. They were
strong enough to house a structure that not only continually drove
improvements, but allowed them to expand with practically no end
in sight. Over the past five years of my career though, I have
drastically changed paths. I have taken on new challenging roles,
spent countless hours in educational programs outside of my
typical scope, and have been exposed to very different working
environments. This has forced me to realize that not every
organization out there has a strong foundation that will support
such internal processes as lean manufacturing or even simple
problem solving processes. I also came to the realization that
organizations with strong foundations really do not have such
complex problems to solve in the first place; because their systems
continuously drive improvements at the systems level (as well as
the process level). Their systems drive the improvements forcing
the processes to drive the people and the people to deliver
exceptional products to their customers, time and time again.
These type organizations rarely suffer from catastrophic failures
even when there is a change in management or a major change in
the products or services they provide their customers.
Unfortunately, most systems are built to be dependent upon the
people that run the business and often times those systems change
when management changes. We have all experienced this
phenomenon in our careers I’m sure. Remember working that after

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school job and spending most of the time goofing off? Then a new
manager was hired and everything changed overnight? This would
clearly explain how a good business turns bad and how a bad
business turns good. Who do they think they are fooling when they
fire an automotive CEO when a major recall occurs, or a CFO when
embezzlement occurs at a brokerage house? Do they think that one
single person was the root cause of the failure? I hope not because
even having good people and good products never guarantees
longevity of an organization. Why? Because one thing is
guaranteed, both of them will constantly change. Betting on people
or products is not enough to remain ahead of the game and to
continuously improve our environment or our future. It also takes
good systems to maintain and achieve long-term success. Good
processes can only be achieved by having a solid foundation where
systems reside. A good system allows us to build upon the
organization year after year. You may recall from Problem Solving
the Solution to the Puzzle our focus for solving problems always
covers all three levels of the organization; the Product, the Process,
and the System. However, most of our efforts were spent at the
systems level because we realized that without improving the
system the other two were futile to solve (process and product).
Our problems still MUST be addressed on all three levels because
without one of the three elements working as it should, the other
two will eventually break down. In other words, if good people are

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compensating for bad processes but still making good product,
eventually those good people will move on and the product will fail.
On the same token, if we have a bad system and are forced to build
elaborate processes to overcome design weaknesses, eventually
the process will fail and produce unfavorable results as well. Have
you ever worked with a person that had been with the company for
years and years and suddenly decides to retire and their job could
not be filled? It’s almost like cutting off a limb. It usually takes
months to recover the process back to an acceptable level of
performance. That’s because a good person was keeping a bad
process from failing. These potential failures should be obvious, but
in my experience, organizations tend to ignore them and bank on
the hope they will never occur. If they do occur, they simply replace
the person in charge as their solution.
In this book my goal is not to teach the reader about all the
intricate details of how to develop a lean environment or achieve
excellence in the work place. There are thousands of books
available for that. My goal is to educate the reader on how to avoid
the most common mistakes that lead to the demise of such
initiatives. I must have completed ten or fifteen notebooks over the
past several years of thoughts and observations I have made on
potential reasons organizations are not living up to their true
potential. What I have concluded is organizations simply fail to build
their processes from a solid foundation. In this book I will share my

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observations and insights in an easy read format with colorful
examples that will allow you to imagine yourself in similar
situations. My hopes are for you to be able to use these examples
and observations to fortify and build a stronger foundation for your
organization which will allow your lean initiatives to be successful
and eternal.
In my observations I have isolated the top ten reasons
organizations fail to achieve operational excellence. These 10
failures also prevents organizations from building and sustaining a
lean environment.
AND THE TOP TEN ARE:
#10 - They are not conducting true observations of their working
environments
#9 - They do not allow the data to drive their business decisions
#8 - They fail to design their measurement systems to output the truth
#7 - They fail to design their processes using a value added approach
#6 - They typically look for blame versus the true root cause
#5 - They treat symptoms and not the dis-eases
#4 - Employees fail to commit to excellence
#3 - They fail to sustain improvements made due to the lack of system
controls

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#2 - They try to manage the business from the process verses the
system
#1 - They fail to develop a map for employees to follow – Operational
Excellence Plans

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Table of Contents
Acknowledgments ..................................................................................4
INTRODUCTION.......................................................................................6
#10 – CONDUCTING TRUE OBSERVATIONS............................................ 14
#9 – ALLOWING THE DATA TO DRIVE DECISIONS .................................. 31
#8 – DESIGNING MEASUREMENT SYSTEMS TO OUTPUT THE TRUTH..... 43
#7 – DESIGNING PROCESSES USING A VALUE ADDED APPROACH ......... 62
#6 – LOOKING FOR THE ROOT CAUSE, NOT WHO TO BLAME ................ 75
#5 – TREATING THE DIS-EASE ................................................................ 89
#4 – EMPLOYEES COMMITTING TO ACHIEVE EXCELLENCE..................... 97
#3 – SUSTAINING IMPROVEMENTS ..................................................... 115
#2 – MANAGING FROM THE SYSTEMS ................................................ 126
#1 – DEVELOPING A MAP TO FOLLOW – OPERATIONAL EXCELLENCE PLANS
........................................................................................................... 136
AFTERWORD....................................................................................... 147
ENDNOTES .......................................................................................... 152

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#10 – CONDUCTING TRUE OBSERVATIONS
Reality TV has pretty much taken over our airways the past
several years. Those that have caught my interest are the ones
where a business is failing and is in need of major help in order to
survive. They call upon the industry expert and his team to take the
helm and steer them back to calmer waters. The range of shows
includes bars, restaurants, automobile dealerships, mom and pop
shops, and even major manufacturing businesses. I sometimes find
myself so engaged in the reality that I start offering up my
suggestions to my wife about how they can correct their dire
situations before they go belly up (so much so she often refuses to
engage in my hypothetical analysis and leaves the room). Maybe
you too have noticed no matter what the actual genre of the show
is they all pretty much have the same agenda. The gist of the show
is usually someone has a business that is failing, they are losing
mass amounts of money each month, and if they don’t find
resolution fast they will be forced to close the doors. Not to fear
because the knight in shining armor is on the way to save the day!
Usually within the one hour episode we will see the years of
mismanagement, neglect on assets, misuse of revenue, bad
reputation and horrible business practices have been resolved and
their business has been miraculously saved. Wow! It’s a miracle
how the host of the show (an expert in the field) can undo years of

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bad business practices and restore the operations back to a
successful level within a few days. Is it possible? I truly have my
doubts but I must admit I am a fan of the concept! What we don’t
see too often is how the owners run the business after the host and
camera crews are long gone. I suspect that if changes to the
operating systems are not truly in tack that it is only a matter of
time before they end up right back to where they were.
One thing we do know for sure is the experts are in fact real, and
are extremely good at what they do. Their backgrounds have
proven success and they have found a formula to use time and time
again to help others become successful. In fact, they have used the
formula so many times we often see them become enraged at the
business owner and managers for making what they think are
simple obvious mistakes. Some may think it adds to the drama of
the show, but I think the emotions of the experts are a sign of how
passionate they are about the process of success. The experts see
the business as a group of processes that make money and nothing
else, whereas most of the business owners see the business as
more of a lifestyle they have chosen to pursue. This is where we
find our first lesson on why organizations often fail to develop
strong foundations that will support a lean environment.
Conducting a true observation of the environment and
communicating the results should be an obvious instinctual motion
for all businesses, but rarely is the case.

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If you have actually watched several types of these reality shows
I have described, then you probably have also noticed a pattern.
What is the first thing the host expert does to get the show
underway? He makes a true observation of the business.
Sometimes they use hidden cameras, send people in undercover,
or the host and the owner takes a tour themselves. Whichever way,
the expert is launching his improvement project by observing the
actual environment the business is currently functioning within.
This observation method is called evaluation of the “current” state
(in lean terminology). After the expert makes his observations, he
then confronts the owner and it’s no surprise that the business
owner is in shock. What comes next? Yes, they get confrontational
and start providing every excuse in the book as to why things are in
the condition they are in, and blaming everyone except themselves.
They instantly avoid ownership and focus on who to blame verses
concerning themselves with the real root cause of the issues. We
will cover that in a later chapter though. What they are really upset
about is the fact someone from the outside had to come in and
point out all the blatant failures. Usually within the first ten minutes
of the show we start to see the real issues come to light that are
causing the business to fail. Managers claim the owner does not
give them resources to do their job, do not give them the authority
to make decisions, spend money on the wrong things, do not help,
and so-on and so-on. Attention then turns to the owner and the

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blame is served right back to the manager by claiming they are not
doing their job, mismanaging their resources, not enforcing work
duties, and so-on and so-on. This is not only a clever way to get the
audience pulled into the drama right out of the gate, but a great
way to measure the true environment in which the business
operates (current state).
The drama really heats up when the host starts to point out some
of the most obvious points of failure that he sees. Usually the
failures have very simple fixes like burned out light bulbs, missing
floor tiles, dirty work areas, dusty ceiling fans, etc. Nine times out
of ten the area where customers are served is in bad repair, is
unsightly to look at, and in some cases even bad odors are present.
The host starts to question the owner and or managers about why
these easily fixable conditions are present and why someone has
not taken care of them yet. Usually they get very defensive and try
to make excuses of why they have not fixed the problems, and even
admit they had not seen the problems until he pointed them out.
Very rarely do we see anyone step up and take ownership of the
condition because they feel it is someone else’s responsibility, not
theirs. I usually disagree with the owner in every case of why the
environment is in the condition it is in. I would love to intervene
and ask - “at what point did you decide to let your business run you,
verses you running your business?” The point of the exercise in the
observation phase is very simple; to gauge the owners ability to

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make a true observation of his environment and quantify the
current state.
From my observations over the years I have concluded that if
good people can truly see the problem they will typically fix it
immediately (if given the resources to). What do I mean by good
people? Good people are those individuals with initiative, care
about what they do, have the drive to succeed, and are provided
with the appropriate tools they need to succeed. In other words,
good people working within even a substandard process will
produce the best possible product they can (or the best product
possible within the environment).
This theory proved itself once again to me while working in a
world renowned boat manufacture a few years ago as the Quality
Manager. I had been on the job less than three weeks when I was
introduced to my first opportunity for improvement. There was an
inspector assigned to the end of line inspection station that had
been doing a great job for almost a year. He reported to the
production supervisor and his job was to find any and all defects
that were on the boats prior to shipment to the customer. He could
spot a defect a mile away. With him on the job our customers
almost never complained about finding appearance defects.
Unfortunately for us he decided to move to another position in
another part of the factory so we were forced to fill his position. His
replacement was trained for over four consecutive weeks before

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going solo. Only after about a week our customer support line was
lighting up with countless appearance defects being reported. The
defects were minor appearance defects like dusty, small scuffs, and
small scratches but were still considered unacceptable defects by
the customer considering they were spending upwards of $100,000
per boat. We immediately turned our attention to the new
inspector to see why he was missing so many defects (obviously it
must be the person). In our investigation we found the inspector
was not the problem, the process and methods were. The previous
inspector was so good at making observations he was actually
compensating for a poor process design. We tracked down the
previous inspector and asked him if all the appearance defects
being reported were normal for the process output. He replied
“yes”. It turns out, anytime he would find smaller appearance
defects, he would take it upon himself to just repair them and send
them on down the line as conforming product (and not include
them on his daily inspection report). He also explained that in the
past he would report all the defects he found on a daily basis to his
supervisor, but never saw any changes being made to correct them
upstream. Because no changes to the process were ever made, he
thought he was helping out by no longer “complaining” about all
the defects. He just started repairing them on his own. One thing
we did take note of was the new inspector communicated every
defect he found immediately; no matter if it was minor or major.

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Every hour upon the hour he would post his findings on the
production board. We were astounded by the number of
appearance defects he was actually finding and reporting, but still
confused as to why our customers were also finding defects once
the product arrived at their door. When we asked the supervisor
why the defects were not being fixed before shipping the boats, he
replied his new inspector was missing them (not repairing them).
The story above is an example of how one individual that is an
expert observer can compensate for a poor process design by
creating a hidden factory. A hidden factory is a step that has been
added to the process flow and is typically not part of the process
design. Hidden factories are perfect examples of how we build
elaborate methods to overcome weaknesses in the process. Usually
our response to overcoming weaknesses in the process is to add an
inspector. Having good inspectors is no doubt a benefit to the
process when needed; however it can also impair the process as we
have seen from our example above. How would you have solved
the issue above? Retrain the new inspector? Train him for a longer
period of time? Add more inspectors to the line to help? Give him
better tools like flash lights or overhead inspection lights so he can
spot the defects better? Install vision systems? If you said yes to any
of these, you would only be contributing to the continuing failure
of the process. Like I said, we tend to build very elaborate methods
to overcome the process design weakness. Our problem is a little

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more complicated than you might think. Our focus should not be
on why the new inspector is not detecting all the defects, our focus
should be on why the defects are being created in the first place
AND why our communication process is broken. How long did the
“expert” observer call out that he was finding defects before he
gave up and started fixing them himself?
After careful analysis of this situation and several others like it
over the years I have concluded the following: no matter how well
your observation skills are and no matter how good your people
are, without a means of communicating what you have observed
the observations have little to no value. We must consider our
systems and processes to compensate for this deficiency. If
everyone had the same level of observation and communication
skills, quality control would be obsolete! Think about it. Why do
organizations usually assign someone else to check your work? Is it
possible that we have unknowingly determined that observation by
itself is useless? Why do we need a quality control group that works
independent of operations? Is it because they can make true
observations and then communicate their findings without the fear
of retaliation?
Every manufacturing organization I have ever visited or worked
in has paid observers to inspect the quality of work. After I moved
over to the service industry I saw absolutely no variance in this type
of methodology. It is apparent that in order to control errors and

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defects we need good observers to point them out and properly
communicate the findings to those that can repair them. I am not
saying that observation is ALWAYS necessary. With a good system
and a good process that is capable of producing defect free
products and services there may not be a need for observations.
However, rarely is the case where we find processes that can
actually produce defect free products or services every single time.
Let’s get back to why conducting good observations are an
important part of developing a lean environment. Observations can
tell us many other things besides the obvious defects or errors we
find. For example, if we were to audit and inspect a restaurant
storage freezer and find food that had expired, what other failures
would you conclude has also occurred? Would you not conclude
that the restaurant also has a poor inventory control process? We
may conclude that the system does not hold the inventory control
process accountable for preventing food poisoning of their
customers either. We may even conclude that no one is
accountable for inventory control, there is no concern for
accidentally serving out-of-date food, or the kitchen manager has
not been properly educated in food safety. From this one
observation we found evidence (or symptoms) that the process has
the potential to produce nonconforming product and does not have
the proper controls in place to prevent them. Accidents do happen
and I’m sure if we were to question the owner or general manager

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they would provide a good excuse and try to explain it away. The
point is, no matter how good the excuse is from the manager, it
does not take away the fact that we observed a defect and that the
process produced it.
Typically, in an audit situation like this we would take more
samples to see if we could further substantiate our findings. We
may not find any more out of date food but it is highly probable that
we will find more symptoms of the system problems. For example,
we may inspect the bathroom and find it has not been cleaned
according to the posted schedule. How would this pertain to the
observation we made in the freezer? In order to make the
connection we must think on the systems level. The dirty bathroom
is a defect. Why was the bathroom not cleaned on schedule? The
process has once again proven to us that it has the potential to
produce a nonconforming situation and does not have the proper
controls in place to prevent it from occurring again (failure to clean
on schedule). These two failures may not seem to be connected to
the average observer, but they are. Both of these failures are
symptoms of a much larger problem that may be preventing the
business from achieving their highest potential. In this hypothetical
situation it appears that the restaurant may have processes in
place, but they are not always being followed. By observing the
actual product we are able to see the performance of the process
AND how well the system controls those processes.

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Becoming a good observer takes practice to master. It’s like
being a doctor in some sense, or a scientist. We must train
ourselves to look at the collection of symptoms so we can follow
them to the true disease that is causing them. It is a skill that must
be cultured and then practiced to perfection. Making observations
within our own environment where we work day after day is even
more challenging to master. It is much easier to make an
observation of areas that you are not so familiar with. Once you
become acclimated to an environment it is much more difficult to
pick out failures or potential failures because they have become
part of your “normal” surroundings (that’s why companies bring in
a new CEO to solve their problems). The reason being, we either
can’t see the issues or if we do see them we may be afraid we will
be forced to fix them when our current systems will not support
such improvements. We are even trained not to see obvious
defects in some cases, or just ignore them because we may not
have the resources to fix them in the first place. Use the 6 key points
below to become an expert observer:
1. Observe the most obvious first – look from a distance
2. Narrow your scope – look closer
3. Study the details – look around it
4. Investigate what you see – define it
5. Use measuring instruments – measure it
6. Confirm your findings – validate them

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I worked for an automotive parts supplier earlier in my career
and within the first two months of taking the job I learned my first
real lesson on why making true observations are so important. The
newly appointed Vice President was scheduled to make a visit so
we were instructed by the Plant Manager to gather a team of
employees and do what they called a red tag event (a process of
removing everything that was not needed). Basically we went
through the plant and cleaned it up taking all the unnecessary items
to an empty trailer truck container parked on the back dock. Once
the container was filled to capacity we would just pile up the
“unnecessary” items behind the building where they would be out
of sight and out of mind. The production floor never looked more
organized since my arrival. We were more than ready for his visit,
so we thought. To our surprise when he arrived he did not use the
front door to make his grand entry as did the previous VP. Straight
to the back lot he went as though he knew we were hiding
something really good back there. Within minutes he had
uncovered our secret hiding place and had finally solved the
mystery of why every VP came back and reported that we had such
an organized shop. He was not too happy to say the least and spent
the next few hours explaining to the management team why he was
not happy. Needless to say, by the end of the day we were all on
the back lot sorting through our “assets” to see what we had left

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after his consultation.
Many months after he left I continued to wrestle with why he
did that. Initially I thought he was just trying to make a statement
and show us who the smarter one in charge was. I figured he must
have been some ex-military drill Sargent or something like that.
Either that or he was just the type of guy that liked to start
confrontational wars with people for silly reasons. I actually ran into
him at the corporate office several months later and just came right
out and asked him why he did what he did. It was really quite simple
after all. He simply explained that every plant he goes to he always
tries to make an observation that others may not make, or will not
make and then communicate them directly to the management
staff. “It shows the management staff that we are concerned with
every aspect of the business, and we want to see any symptom that
may cause the operations to fail. Sometimes managers will not
realize the failures until it’s too late to cure the disease.
Mismanagement of resources was the leading symptom of our 10%
loss in revenue last year. Everyone must learn to use our resources
more carefully or it will be our demise”.
That small lesson not only taught me that we need to be good
observers, but we need to figure out how we can report
observations to those that can fix the problems we find. Good
observations can also help protect us from making defects if we can
communicate them effectively to those that would or could be

27
affected by the defect. How many times have you pointed out a
problem to someone that cannot fix it? Some of us may say that is
the definition of complaining! There is a big difference between
complaining and making true observations; and the difference is in
the way the problem is communicated. What benefit does it have
to make a true observation only to report it to someone that cannot
fix it nor do anything about it? What should our inspector have
done at the boat factory when his supervisor did nothing about the
defects he was finding? His supervisor did nothing; therefore our
inspector just repaired the defects until such time he could bid off
the job. The problem could have been resolved had the information
been communicated to the appropriate persons that could have
solved the problem. Unfortunately for us there are not that many
people out there that have the skills to communicate effectively.
When reporting problems or potential problems that are observed
we must consider the audience that we are presenting the
information to. It is our obligation to communicate our
observations so we must also present the information in a manner
that does not seem threatening or cast blame on others. True
observations must be supported by a functioning system based on
fact and not opinion in order to be truly successful.
Take a few moments next time you are at work and actually look
around your environment. Do you see things that are in need of
repair or might cause a defect in the near future that you can’t fix

28
yourself? How are you going to communicate those observations to
the people that can fix the problem? Does your organization have
a communication process that is effective and that is supported by
an actual system?
Consider this situation: You have been magically transported
back in time to December 26, 2004 at 7:30 AM and you’re standing
on the Banda Aceh beach in Indonesia. You feel a trimmer under
your feet and observe the tide slowly rolling out. You also observe
the animals doing strange things like heading for higher ground.
You suddenly realize this is the day the massive tsunami hit that
killed thousands of people. You have less than a half hour to report
the imminent threat and clear the beach to save over 31,000 people
from harm. Take a minute and think about how you would report
your observations of such an oncoming catastrophe to those that
needed to know.
At the time of the tsunami there were hundreds of observers
watching symptoms of the imminent calamity unfolding. Most
people on the beach had never seen such events in person but
knew something was just not right. Some on the other hand had no
inclination of what was going on and just stood and watched in
amazement. Those observers that figured out a tsunami was more
than likely on the way, still did not realize the threat was coming
straight toward them at 500 miles an hour and they would have
very little time to react. For the few observers that knew what was

29
coming, they all took various courses of action. Some started yelling
from the roof tops and hotel balconies, some grabbed up what they
could and heading out for higher ground, and others corralled
people into safer areas. Unfortunately, the observers that sent out
warnings were mostly ineffective in getting the message to those in
harm’s way. Their only means of communication was to yell in every
direction where they saw people. Obviously this method of
communication was not the optimum action as evidence of the
thousands of lives that were lost that dreadful day.
The horrific incident in the Indian Ocean that day prompted
many countries to adopt what is now known as the Indian Ocean
Tsunami Warning System. The system includes 26 communication
stations that warn people of the threat in enough time so they can
evacuate to higher ground. This is an example of systems
improvements that were made to optimize the early detection and
imminent danger communication that may someday save
thousands of lives. As we have learned from this example and many
others like it, making a true observation is futile unless it is coupled
with an effective communication process. Observations are only
one part of building a foundation for your problem solving
activities. Your foundation must also include a means in which to
communicate your observations to those that need to know.
There are many out of the box programs available for making the
connection between observations and the ability to communicate

30
those observations to the appropriate authorities that need to
know. Each organization must decide which programs and which
options suits their needs best.
The key solutions for a successful observation and
communication program are:
 The program must have the ability to allow anyone in the
organization to report an observation
 The program must require validation of the observation before
allowing it to progress to the next level (levels of validation)
 The program must allow for analysis of the observations so
trending conditions can be easily identified
 The program must allow people to make observations without
fear of persecution or retaliation
 The program must require actions to address an observation
once it has been made Actions are: reject it, send it back for
more details, accept it and launch improvements, or provide an
explanation of why the condition is acceptable as is.
 The program must allow the observer to report defects directly
to those that need to know

31
#9 – ALLOWING THE DATA TO DRIVE DECISIONS
Data actually has a voice and it speaks to those that listen. In # 10
we studied how making good observations and then
communicating the findings appropriately can avoid imminent and
present threats. Communicating WHAT is just as important as
making a good observation because we must describe the
observations in quantifiable terms in order to be effective. In this
chapter we will study what actually needs to be communicated so
the problem can be resolved or avoided. It is important that we pass
along the correct data as effectively as possible to those that need
to know.
We have all heard the story of the little boy crying wolf in Aesop’s
Fable. The moral of the story is not to lie. Is that correct? We may
need to reconsider that interpretation for a moment. The moral
may be, not to abuse your authority as a communicator. The little
boy was a shepherd and in the 17th century that was considered a
very important and somewhat authoritative position. Why else
would the villagers have left him alone on the hill to watch over
their prize sheep? His position of authority automatically instilled
trust into those around him so none of the villagers even
questioned the presence of a wolf when he cried out. This is a very
important lesson to consider. By human nature I think we
automatically assume those in authority know exactly what they

32
are talking about and we rarely question anything they say or do
because of their position. However, once they lose our trust it
doesn’t matter what data they have we no longer trust them (or
the data for that matter). How many people in power over the years
can you recall that have lost our trust? Do you think it’s possible to
trust them again? In Aesop’s Fable the last line of the story states
“No one believes a liar, even when they are telling the truth”. Trust
comes with the positions we hold so we must ensure we do not
break it by giving false data or firing from the hip.
In general, I don’t think people intentionally set out to deceive
decision makers by giving them false data on purpose. I think when
they provide false or unanalyzed data they truly believe it to be true
to the best of their knowledge and ability to analyze it. Remember
what George Costanza from Seinfeld says, “It’s not a lie, if YOU
believe it.” If people truly believe what they are presenting as facts,
they can really get behind it to convince others.
The burden of proof lies on the presenter of the data not to
confuse the decision makers but to convince them the data is
factual. As a young engineer I was told “if you can’t convince them
then confuse them” (with the data). That was very bad advice, but
unfortunately it did work quite well most of the time. We
sometimes forget the decision makers are not always data experts
so we do have the advantage if we set out to deceive them. Our
main task when presenting the data however is not to convince

33
decision makers to make a specific decision, but to ensure them the
data is factual (even if it hurts them to hear it). In #8 we will discuss
how telling the truth may sometimes be difficult, but if you can let
the data speak (and interpret it correctly) it will be much less
challenging to deliver the bad news.
So how do we ensure the data is factual? That is a very broad
topic! So instead of trying to “boil the ocean” let’s narrow down
that scope and focus in on some simple techniques we can practice
that will at least give us a better advantage of analyzing data. First
and foremost one of the most successful methods I have found is
to seek out the experts. Even if you are a statistical guru it is always
a good idea to get a second opinion. If you do not have access to an
expert, gather together a small work group and present your data
to them. Sometimes I find when I explain my analysis to others I
have forgotten a step or made a mistake. Another benefit of
presenting your analysis to a small work group is sometimes they
ask very good questions. Because they may not fully understand
what you are explaining they will ask questions that may prompt
you to explain in more details; which often times leads to finding
mistakes in your analysis. No matter which technique you decide
upon, at least try something to confirm your findings. The worst
thing a presenter can do is present data that is not ready to be
looked upon by the decision makers. An even more catastrophic

34
action that can be taken is actually acting upon a situation without
data or taking the time to analyze the data.
Over the years I have been fortunate enough to have worked
with some of the best engineers in the world; and probably some
of the worst. One of the most important techniques I have always
tried to pass along to my team is not to fire from the hip; meaning
you must never react until you have the facts (data) to back up your
speculations or assumptions (make a true observation). Engineers
in general are perceived to be the authority when it comes to
providing the correct data and the interpretation of data. One
minor mistake of misleading data or an indecisive decision could
lead to detrimental results. One of the worst examples I can
remember of an engineer firing from the hip involved a very
expensive pilot vehicle. I worked for a supplier that manufactured
windscreens (windshields) and supplied them to pretty much every
major automotive manufacture in North America. I was
participating in a pilot launch at a well-known automotive
manufacture and we were testing new adhesives that held both the
front and back windscreens in place. Once the windscreens were
glued into the test vehicle it was part of our job to then road test it
for validation. Unfortunately, no one on our testing team thought
to include the engineer from the adhesive manufacture, therefore
no one knew exactly how long it would take for the adhesive to cure
before it was safe to test. We had a conundrum on our hands

35
because the test had to be done and done quickly. When test cars
are built, typically several items are tested simultaneously to save
time and money, and this car was no exception. Another group of
engineers and suppliers had installed other test components on the
same test car and were eagerly awaiting the road test to
commence. The track was already reserved and our testing time
frame was quickly running short. The senior development engineer
working for the automotive manufacture finally contacted the
adhesive supplier engineer and demanded to know the cure time
so testing could get underway. He told him we only have 25 minutes
left to use the track and it takes at least 15 minutes to perform the
test. He explained that if we do not test in the next 5 to 10 minutes
we would not be able to test for another 2 days. Backed in the
corner with this ultimatum, the supplier engineer fired from the hip
and gave us the thumbs up to test. “If it’s not cured by now it will
never be cured” were his exact words. Less than 3 minutes into the
test, disaster struck! The car ended up on its’ side deep in the large
grassy area in the middle of the track and was a total loss! When
the test car reached testing speeds of around 55 mph the driver
rolled down the driver’s side window causing a force of wind to rush
in which blew out the rear windscreen. This distracted the driver
just long enough to drive the car off the hard surface of the track,
which then lead to loss of control and the car ended up crashing

36
into the infield. Obviously, the assessment of the adhesive cure
time made by the supplier engineer was incorrect.
We can avoid most if not all of these type situations if we build
into our system a process to prevent such “from the hip” decisions.
What would you have done in the situation above when the
automotive manufacture engineer confronted you about making a
decision? Did you pick out any factors that may have persuaded the
supplier engineer to make the hasty decision? There were factors
that should not have been considered by the supplier engineer in
his decision making. Factors such as; being told that we had a
deadline to use the track, and that we would not be able to use it
for two more days if we did not test within the next few minutes.
These influential factors are commonly presented to decision
makers to pressure them into making decisions. Rarely is the actual
data presented in conjunction with these factors. Maybe you have
heard some of these examples yourself?
“We need to ship the product or we will shut down the customer”
“We have always made them (the product) like this so I don’t know
why it’s a problem now”
“The customer has never complained before”
“The defect is really not that bad and I don’t think the customer will
even notice”
“This is all the raw material we have and if we don’t use it we will
not have any product to send”

37
Having the correct data will allow decision makers to ignore the
influential factors to the point of no consideration. I have only
encountered one situation that I can remember where the true
data was presented to make the appropriate decision and the
wrong decision was purposely made. I will withhold the details, but
can tell you the person that made the wrong decision was quickly
given the opportunity to pursue other opportunities in his career as
a result. Let’s look back at some of our examples above to see how
we may address such influential factors replacing them with data.
“We need to ship the product or we will shut down the customer”
Response: The data indicates the parts are 12.25 mm in length and
the customer requires them to be no longer than 12.20 mm. We
could call the customer to see if they would deviate from the
specification to prevent shutting them down due to the lack of
parts.
“The defect is really not that bad and I don’t think the customer will
even notice”
Response: The defect measures 2.5 mm in width and has been
detected by 3 independent inspectors. If the product needs to be
sent to the customer we should send them pictures of the defect to
see if they will accept it in this condition.
Allow the quantitative data to influence the decisions and not
the “fluff” as I call it. When presenting to the decision makers

38
remove all influential information that does not directly relate to
the actual condition of the product or service. I was once told by
one of my bosses that the more a person talks when describing the
problem the less they actually know about it. “More facts and fewer
words” he would always say.
Just as you should always provide the concrete details to the
decision makers seeking a solution so should your employees.
Always encourage them to let the data speak for itself. Avoid
putting your employees (or yourself) in a position to interpret the
data incorrectly. Don’t be afraid to call on the experts around you
to help decipher the data before you present it to the decision
makers. Most importantly, make sure you base your decisions on
data when at all possible. Let the data speak, give it a voice, and
allow it to guide you toward the correct decision. Data has no
opinion; those with opinions, usually have no data.
So how do we design a system that produces data and not
opinions? When someone approaches you for a proper greeting
and they extend their right hand, what do you do without even
thinking? Naturally without hesitation you extend your hand and
accept theirs and render a shake. I challenge you to try this little
exercise. Next time you find yourself faced with another person in
a situation where a hand shake is not “normal”, say nothing and just
extend your hand. See what happens almost instantaneously. I will
bet 99% of the time the person will extend their hand and render a

39
proper shake. Why do you think this is? The reason we do this is
because it has become instinctual. When was the first time you
shook hands with someone without even thinking? It was probably
so long ago in your childhood that you may not even remember.
Now imagine having all of your employees reacting to problems
instinctually without even having to think about it. Imagine having
all of your employees in a state where making true observations,
data collection, and data analysis just becomes a natural everyday
instinct they have learned. They will start to recognize problems
while they are still small enough to eliminate, therefore preventing
impact to the bottom line. Is all this possible? Sure, it is not only
possible, it is also very realistic to achieve. That is, if you provide
them with the opportunities they need and provide the appropriate
environment for them to achieve such success.
Let’s go back to the shaking of hands process for a minute. Have
you ever been introduced to someone that showed obvious signs
of being sick, working in the dirt, or was working on an old motor?
The natural instinct of course is to extend your hand for a proper
greeting. Did you shake their hand? I would bet that most often you
adjusted the process within micro seconds to overcome such a
hurdle; but yet still rendered your greeting. You may have done the
knuckle bump, the old glove shake, the remote shake, or may have
even given them a bow as the Asian countries do. One way or
another you quickly assessed the situation and overcame it with

40
hardly even a thought. That’s because it is now part of your core
tools you use so often that it requires no real thought to overcome
or adjust. Developing an organization that possesses these skills
requires the right tools to be available to the organization first.
When you give a person a choice, always give them the right
choice.
Data is the result of inputs. Data is a description of the actual
outputs we produce. We all remember from our days of High School
math the simple little equation, y=f(x). This simply means that the
output, y (which is our description or data) is a product of whatever
we put in, x. For example, if we have an input of 1 + 1, our output
would be 2. Typically the outputs are fairly easy to measure.
Thinking on the systems level, where we focus our attention on
preventing unfavorable outputs from occurring in the first place,
we should also consider the systems inputs that we put into a
process. If you also apply the same principles of data analysis to the
inputs (allowing the data to drive our decisions), we can actually
start to prevent those problems from occurring versus dealing with
them after they have occurred.
The first step in problem solving is to understand what is going
on in the process. Inputs can be mechanical, technical,
instructional, and procedural. Inputs are the factors that are put
into a process so the output can be created. For example; if a
person wanted to create a process to make a cup of tea, they will

41
need certain inputs such as water, tea bag, and a cup. Is that all? Of
course not, those are only the mechanical items they need. In order
that we achieve the output we desire we need many inputs other
than the mechanical ones. We also need the amount of water to
use, the size and type of tea bag, the size of the cup, a microwave
to heat up the water, the amount of time we need to heat the tea,
instructions on how to put all these items together, we need to
know how to stir and when to remove the tea bag, the list goes on
and on. So, even a process as simple as making a cup of tea actually
has many inputs that we do not normally consider. Imagine if we
wanted the tea to be a specific temperature, consistency, color,
smell, and taste. We would need to consider each input very closely
and install controls to ensure each input is met to achieve our
desired output. Consider the work involved in setting up a process
that would produce hundreds or even a thousand cups of tea.
Overwhelming to consider, but very possible if the process is set up
correctly using the appropriate inputs.
For existing processes it is sometimes easier to work backwards
to identify all the inputs; sometimes called reverse engineering.
There are occasions where a company will purchase a competitor’s
product and basically take it apart piece by piece to see all the
inputs they are using. The company will try and determine which
input is better and which one is worse than theirs. In order to

42
improve their own product, the company may adopt the best inputs
form the competition.
How do we establish what inputs to use for a process? We must
know what we want the output of the process to be of course. WE
NEED THE DATA to make our decisions!
The key solutions for allowing data to drive our decisions at the
foundation are:
 Ensuring the data originates from a trustworthy source (don’t
cry wolf)
 Verify, verify, verify! Do not be afraid to seek out the help of a
data expert
 Not “firing from the hip” to make hasty (and sometimes very
costly) decisions
 Remove influential factors when making decisions, base
decisions on data alone
 Data should remove personal opinions about how to react
 Make data collection and analysis an instinctual part of the
everyday environment

43
#8 – DESIGNING MEASUREMENT SYSTEMS TO OUTPUT
THE TRUTH
So far we have discussed how to make true observations and
how important it is to use data for decision making. Now, we must
make the connection between the two. We must establish a
measurement system that will allow us to make true observations
and then output the actual truth (the factual data). A successful
measurement system allows for the collection of data to occur as
accurately as possible, with as few errors as possible, and with few
opportunities for errors as possible. This is important for a
measurement system because we need to ensure our data is
credible. Let’s go back to our example of the tsunami warning
system in the Indian Ocean. Do you think the surrounding
governments have now established a successful measurement
system, and did they implement that system successfully? Would
you feel “confident” visiting coastal areas of the Indian Ocean now?
In order to design a successful measurement system we must
consider the possibilities (and risks) of how the system could
potentially fail. We must understand how the system could fail and
then design out those failure modes.
A System Failure is when one or more elements of the system
cause a catastrophic failure resulting in; no output of data, output
that does not meet our requirements, or a different output than

44
the system was designed to create. Typically our measurement
systems are not stand alone. We typically set them up in more of a
serial type configuration; meaning measurements are taken feature
by feature as the part is manufactured or the service is rendered.
An example of a serial type would be: A bartender fills a 10 oz glass
with cubed ice (which will typically consume 2.5 oz of volume), then
measures and pours in 1.5 ounces of vodka, he then fills the
remainder of the glass with orange juice to make a screw driver
mixed drink. There is no need to waste time measuring out the
correct amount of orange juice because it is controlled by the
amount of space available or remaining in the glass (which should
be 6 ounces). The amount of orange juice that is added is
dependent upon the remaining available space in the glass. In other
words, it is not possible to have too much orange juice if the correct
size glass is used, it is filled with cubed ice, and the correct amount
of vodka is used. It is possible though to have too little orange juice
if crushed ice was used, or too much vodka was used. If any of the
4 elements were not correct, the drink would not taste right. So,
our serial type measurement is good, but it is far from perfect. I’m
sure you have experienced the failures of the serial measurements
if you have ever ordered the same mixed drink at different bars or
restaurants.
Another type of measurement configuration that is commonly
used is a parallel type measurement. Parallel type measurements

45
are independent of each other and do not rely on previous
measurements or data outputs. In other words, the measurement
is set to output the same result no matter what other
measurements result. For example: a button is pushed and an
automatic dispenser measures out the exact amount of vodka, ice,
and orange juice into a 10 oz glass simultaneously; one is not
dependent of the other. If a smaller serving were programmed into
the machine, the ratio of vodka, orange juice, and ice would not
change, only the volume you received. The drink would taste fine;
however there would be less of it. Consider processes such as
mixing ingredients or compounds. Would these be an ideal
application for a parallel type measurement? What if we were
mixing concrete on a very hot dry day opposed to a damp cool day?
Would the same measurements apply to the amount of water to
concrete ratio?
There are advantages and disadvantages to both. Serial
measurements save time and will allow us to compensate for other
measurements that fall out of the tolerance band. We can add to,
or take away from, so our finished product ultimately conforms to
our requirements. The disadvantages of using serial measurements
are they may not report potential failures, like too little vodka or
too much orange juice, and they are highly dependent on other
measurements we take (dependent variables). Parallel on the other
hand is costly and usually requires some type of automation. It also

46
does not allow us to compensate if one factor needs to be adjusted
based on a particular condition (without reprogramming or making
some major adjustment to the process inputs). The advantage of a
parallel measurement is the ability to control each measurement
independently from the other to control error (independent
variables). For example: if each measurement was controlled
independently it would allow us to clearly detect if we were adding
too much or too little of something; our glass would either be
overfilled or under filled.
Other examples of serial and parallel type measurements:
 Serial – measuring how level the wall is each time a block is laid;
adding mortar or tapping the block down if not level
 Parallel – measuring the height of the block and the height of the
mortar thickness each time a block is laid, adjusting thickness if
not level
 Serial – measuring the alignment of the wheels on a car when
assembling, and adjusting the tie rod to align if not straight
 Parallel – measuring the alignment of the wheels when
assembling and measuring the location of the tie rod connector;
reject all tie rods not adjusted properly
 Serial – measuring the height of a gear in a transmission and
adding shims to adjust if not correct
 Parallel – measuring the height of the bearing location, the gear,
and the casing; rejecting if not all correct

47
It is now pretty obvious the differences in the types of
measurements selected. The main difference to consider is: can we
adjust components if needed so the ultimate finished product is
within specification? If we are supplying components that will be
added to other components at the customer’s location, the answer
would be pretty obvious as to which we would select. We would
not expect the customer to add shims to a transmission we sold
them so the gears would not rub would we?
5 of the Most Common Measurement Systems Failures:
1. Absence of a system, the system was never defined, the
system was never made part of the overall “formal”
operating system – “We’ve always been able to get by
without it”.
Examples:
 Never assessing the sustained knowledge of those that have
been through specific training programs: such as, Statistical
Process Control (SPC), Preventative Maintenance (PM), or
On the Job Training (OJT)
 Data not being analyzed and not used to drive process
controls (or make decisions)
 Absence of process or part design validation

48
2. Never measured the system – never defined a measurable
or criteria to gauge success or failure of the system – “tribal
knowledge”.
Examples:
 Process owners assume process is in control based off
downstream information received (no failures reported,
and no problems reported to them)
 It is assumed the characteristic cannot be measured, usually
because lack of knowledge by the process owners
 It is assumed that because one output of the process is good
that another one must also be good (this is another example
of a serial measurement system)
3. Measured incorrectly – measurements indicated system
was working properly, however downstream outputs
indicate the system cannot be performing correctly or
downstream operations cover up or repair the failures –
“we’ve always done it that way because that is the best we
can get from the supplier (upstream supplier)”.
Examples:
 Process owners are using the wrong type gage (linearity &
bias unknown)
 Results are analyzed or reported incorrectly

49
 Wrong type scale is being used (using inches should be using
millimeters for example)
4. Undeveloped systems – process capabilities unknown or
have never been in control – “this is the way I was taught to
do it, so that’s the way I teach others”.
Examples:
 Operator was self-taught to do the work
 Hidden factories are still within the process (unknown if
they are significant factors of the process or not)
 Input parameters and output controls are not all defined
5. Compliance – system rules have been defined but rules are
ignored by operators – “we tried it that way, but our
method works much better and faster”.
Examples:
 Operator uses a different tool because it is faster, easier to
use, or more convenient to get to
 Operator ignores rules because he does not agree with
them or he does not completely comprehend them
 Noise prevents the process from achieving specific outputs
– we cannot control the humidity outside, but the curing of
gel coats requires at least 5% humidity to reach full cure
before we can spray resin (noise are factors we cannot
control)

50
It is important to focus your attention toward measurement
systems at the highest possible level within the organization. If you
can design measurement systems at the systems level, all processes
designed under that system would be required to implement a
functional measurement system that would meet even the basic
requirements. No organization can be successful without highly
developed, well governed, and extremely well designed systems
that continually test the processes and the products. Well-designed
measurement systems will quickly alert us when adjustments need
to be made and will keep us aligned for successful and desirable
outputs.
There are essentially two methods of reporting data from a
measurement system that works with both quantitative and
qualitative data.
 Live results - allows for everyone to see the data at real time.
Live data is less likely to be “modified” but does not allow for the
analysis of error before it is presented. Some common examples
of live data are: lap times of a race car, heart rate monitoring,
weighing scales, oven temps measured with laser, end-of-line
parts counters.
 Stored results – allows for the analysis of the data before it is
presented; however it also allows the analyzer an opportunity to

51
manipulate the data that may hide error. Some common
examples of stored data are: Average lap speed of a race car,
average heart rate per minute, weight loss over two months,
average temp of an oven per shift, or total number of parts
produced per shift per month.
To select the best method we must consider how the data will
be used. For example, live data works great when we are in
constant contact with our race car driver. We can inform him to
speed up or slow down each lap he makes to conserve fuel or to
save the tires. Stored data in this case would be used throughout
the race or for the next race to determine how many miles we
averaged per gallon of fuel consumed or how many laps we got out
of each set of tires. The live data allows the operator to take
immediate action on the process to adjust it accordingly to what
the data is indicating. The stored data will later be used by the
process engineer to determine overall capability of the machine so
improvements can be made (at a later time). It’s not uncommon to
use live data to see the minute-by-minute condition of the process
and then use the stored data for later analysis to see the big picture
of the overall condition and capability. Imagine listening to a horse
race on the radio. The announcer is reporting the live results of
what he sees second by second. You probably used stored data to
place your bet though because stored data allowed someone to
calculate the odds of your horse winning the race.

52
Live data can also be assigned control limits where alarms can be
set to indicate an out-of-control condition (and stop the process if
need be). When the process reaches a specific target the machine
will stop or send out some type of visual or auditable alarm
informing the operator an error has occurred; whereas stored data
will not allow us to set up alarms that would shut down a process
before the defects were made. Imagine an operator measuring a
part every hour and writing it down on a collection sheet. The
process engineer collects the data at the end of the shift and
performs data analysis on it. If the engineer uncovers an issue, it’s
too late to correct the process at this point because an entire shift
of parts has already been produced.
Not all measurement systems are designed to measure discrete
features on a physical part. Some measurement systems (such as in
the service industry) may be designed to measure other sometimes
difficult attributes. Many organizations use the survey type
measurement system to determine their performance, customer
satisfaction, or to conduct market research. Although much more
difficult to obtain and assign discrete values to, surveys can provide
an organization with just as much information as the traditional
physical measurements used in a manufacturing environment.
As with traditional measurement systems, survey type systems
must also be designed to output the truth, or as close to the actual
condition as possible. Because we mostly deal with qualitative data

53
in a survey, it is very important to design the measurement system
in a manner that builds trust in the results we obtain. Most surveys
will ask people to provide a feeling, an opinion, or a personal
experience they had with a particular product or service and then
rate it on some scale. As you may know, it is sometimes very
difficult to measure such attributes with a great deal of accuracy.
Consider the mood the person is in or what kind of day they are
having when they provide such inputs. It may greatly affect the
results we receive, even by how we ask the questions or who we
ask them to. Design your survey to ascertain as much quantitative
data as possible so it can be used to support your qualitative results.
Follow the same basic principles that are used to design a
traditional discrete measurement system.
Start first by developing a Hypothesis that will need to be proved
or disproved. The hypothesis is simply the question “what am I
trying to solve” by conducting the research. The hypothesis is more
or less the problem statement that needs to be measured.
Next, develop research questions that will ultimately lead in the
development of survey questions that will prove or disprove the
hypothesis in quantitative terms. For example: Hypothesis –
Customers are not satisfied with the services we provide them.
Research Question: Precisely what data could I collect that would
allow me to measure customer satisfaction with our services? The
answer may be the speed in which the service was rendered.

54
Survey Question: Our goal is to provide service to our customers
within 5 minutes of their arrival. How long did you wait to be
served? _________(answer in quantitative terms here)_
This type survey question requires the customer to provide the
actual discrete data so it can be used for further analysis. We are
not asking them if they “feel” like the wait time was too long
because everyone will have different opinions as to “how long” is
too long. By requesting the actual time in minutes we can ask many
customers the same question, an average time of service could
then be determined. We may also ask the customer if they were
satisfied with the amount of time they waited. This would indicate
how satisfied they were at each level of wait time. For example-
75% of the people surveyed that waited longer than 5 minutes were
not satisfied with the wait time.
Define the goals of your research: Decide upfront what data is
needed that will prove or disprove the hypothesis. Create and then
answer the research questions by defining your goals and
objectives. Research questions should be global in nature and will
allow you to narrow down your survey scope. Remember, research
questions are not the same a survey questions!
Example research question to narrow scope: What is the purpose
of this study? – To determine if the tourism population in Perdido
Key Beach can be improved during colder months of the year.
Convert your research answers into research questions:

55
Example research question: How might I find out if there is a
significant difference in tourism when the temperatures fall below
60 degrees? When temperatures are between 30 and 60 how much
does the population fall (or rise)? These type research questions
will narrow the scope of the study.
Research questions can either be “Testable” or “Non-testable”. It
must be decide BEFORE designing the survey questions.
Testable = results can be statistically analyzed. For example: In our
survey, we will ascertain exactly how many people out of 500 would
visit the beach if the temperatures were below 60° F, and the hotel
rates were $50 per night.
Non-Testable = results cannot be statistically analyzed. For
example: In our survey we will ascertain if the Chamber of
Commerce members feel prices are too high in the winter months
to sustain the tourism population. This may be good information to
have that may led to further research, but not enough data to solve
the problem you set out to solve.
Test your Hypothesis by designing survey questions to answer your
research questions:
Example Survey question: I would visit the beach if my room cost
per night was only $50 even if the outside temperature was below:
a) 40° f b) 50° f c) 60° f d) 70° f
Let’s suppose our hypothesis was: More people would visit the
beach if the room rates were $50 per night, even if the

56
temperatures were below 50°. Let’s suppose out of the 500 people
surveyed, 75% of them answered they would visit the beach if the
temperature was below 50° and if the room rate was only $50 per
night. Based on the results of this survey question, can we prove or
disprove our hypothesis? I would say no. Why? Because the people
surveyed only provided the intention to visit; they have not visited
under those conditions yet. Maybe a better survey question would
be: Have you ever visited the beach when the temperatures were
below 50°? Yes or No? How much did you pay for the room when
the temperatures were below 50°? _$__________
Once the survey questions have been finalized and designed in a
manner that will clearly prove or disprove the hypothesis, the next
step is to validate the survey. Validate the survey by having the
industry experts review it. For example, send it to the Chamber of
Commerce to see if they understand the questions and if the
answers would allow them to derive a solution. Put together a small
focus group and have them take the survey while being present.
This will allow the group to ask questions about the survey that may
not be understood. Any question they have about the survey should
be considered a defect in the survey and should be resolved before
sending it out.
To further validate the survey, conduct the same survey at least
twice to determine if there is uncertainty in it. In other words, the
results should be repeatable from the same group if the survey is a

57
creditable measuring instrument. We must first build confidence in
the measurement method and be able to prove its validity before
sending it out to others.
Select the target audience for the survey. Go back to the scope of
the research and determine precisely who will receive the survey.
Determine what target group would be able to answer the
questions that will ultimately result in proving or disproving the
hypothesis. For example, from the initial research question above
“75% of the 500 people surveyed said they would visit the beach
when temperatures were below 50° if room rates were $50 per
night”. Who were the people that were surveyed in the study?
What were their ages, sex, financial status, and resident location?
This would be a perfect application for a parallel type
measurement. Demographic data could also be collected with the
survey. The data could then be further analyzed based on these
factors. Who knows, we may find that only those over the age of 60
would be willing to visit the beach if temperatures were below 50°
and the room rates were $50 per night.
Analyze the results statistically to determine if the hypothesis
has been proven or has been disproved so a determination for the
solution can be made.
For example: Hypothesis stated that people are more likely to visit
the beach when temperatures are lower as long as the room prices
are also lower.

58
Results from 500 people surveyed indicated they would visit the
beach if rooms were $50 when temperatures are:
a) between 45° – 55°: 75%
b) between 35° – 44°: 20%
c) below 35°: 5%
Age range of those that said they would visit if temps were between
45° – 55°:
a) 25 – 34 years old 10%
b) 35 – 45 years old 20%
c) 45 – 55 years old 70%
Result statement: 375 people out of 500 (or 75%) of the survey
group would visit the beach when temperatures are between 45°
and 55° if room prices were $50 per night.
Out of the 375 people that would visit, 70% of them were between
the ages of 45 – 55.
Results Meaning: Go back to the purpose of the research and
determine if these results will help resolve the issue or improve a
condition. For example: Do the results help revise a condition that
could improve it? In this research; will discounting the room rate
increase occupancy when temperatures are between 45° and 55°?
From this example data, should we decrease room rates when
temperatures drop below 45°? Are we targeting specific age
groups? Should we drop the room rates even further for
temperatures below 45° to increase tourism? Think the survey all

59
the way through BEFORE sending it out to ensure all questions like
these will be addressed. We would not want to get the results back
only to realize we have failed to prove or disprove our hypothesis
by not including additional questions. Try to design the survey to
“drill down” to the question we want answered.
Here are a few example questions that may help drill down.
Room rates are not a primary factor during the winter months
because: Check all that apply
 I do not like being at the beach during winter months
 I have children in school and cannot take a vacation
 There are not enough activities going on at the beach in the
winter
 My vacation time off is only taken in summer months
What might encourage you most to visit the beach during winter
months?
 Conducting business (business related visit)
 Theme parks and tourist attractions were still open
 More indoor activities were available like concerts, shows,
water parks
Measurement systems from organization to organization and
even within individual organizations may be very different from
each other. It is very important for the organization to establish the
system that works best for them and then perfect it over time. Build

60
it with the intent of unity and communization throughout the
organization. If everyone in the organization understands how to
use it, the system will be much more productive than if only a few
know how to use it. Focus the measurement system to output
discrete quantitative data so it can be thoroughly analyzed to draw
conclusions from. Ultimately, the measurement system should be
designed to answer all the questions posed by the organization.
The key solutions for developing a successful measurement
system at the foundation are:
 Deciding the type of configuration that works best, or a
combination of the two (serial and parallel)
 Deciding the type of data output that works best for the
application (live or stored)
 Building a measurement system that prevents the 5 most
common failures
1. Absence of a measurement system – not formalized
2. Failure to measure the system – validating the measurement
system
3. Measuring features incorrectly – not understanding how or
what to measure
4. Using undeveloped systems – allowing operators to figure it
out themselves
5. Establishing compliance controls – not enforcing
measurement system rules

61
 Knowing how to design and use data collection tools when
using qualitative data

62
#7 – DESIGNING PROCESSES USING A VALUE ADDED
APPROACH
In my current position I work directly with suppliers to help them
identify opportunities for improvement and teach them how to use
Lean tools to remove waste. I work with our suppliers on various
projects that will reduce the manufacturing cost of the products
they produce and sell to us. The suppliers and I focus our efforts on
improving the process and systems to become more efficient so
they will ultimately add value to the end products. Because of the
complexity of our products, we purchase components and
subassemblies from a very broad range of manufactures. We
purchase anything from simple plastic parts (what we call shoot and
ship) all the way to very complex parts like transmissions and drive
components. Every project we launch starts out by conducting a
Gemba walk with the team. The word Gemba comes from the
Japanese language meaning the place where the work is being
performed. What I have discovered is astounding to me! No matter
how simple or how complex the process is, the number one
observation the team always makes is poor process design. Actually
because the team has not yet completed the training, they identify
the symptoms of poor process design. We will talk more about
“symptoms verses the dis-ease” in failure #5 later.

63
Why do you think the number one observation is always poor
process design? If you know anything about conducting a Lean
project (Kaizens) then you already know that the teams are made
up of what is known as a “cross functional” team. Meaning the
team members are from various departments within the
organization. Typically members of the team will come from
Operations, Quality, Maintenance, Engineering, Purchasing, Human
Resources, IT, Shipping, Sales, Marketing, and any other
department that makes up the organization. The cross functional
team approach allows us to look at the value stream of the product
from different angles. It allows us to “make a true observation” at
a minimum of the entire process flow. From purchasing raw
materials to delivering the product to the customer, we need to
look for opportunities to remove waste everywhere. Remember
from #10 failure above about the reality TV shows where the host
comes in and starts to find old food, dirty bathrooms, and
unorganized freezers? Why do you think the people working there
are not seeing those issues? Because they are not cross functional
and they see the same mess every day. So, is the number one
observation found during a Gemba Walk because we are not
making a true observation? Actually no, it’s found because it exists.
Sure, we must make a true observation in order to find poorly
designed processes but if we do not design them correctly in the

64
first place we may fall victim to living with them as do almost every
organization out there today.
So what do I mean when I say a poor process design? A poor
process design allows for waste that could be prevented to occur.
Notice I said “waste that could be prevented” and not just all
waste? The reason I separate the two is because of the same reason
we have tolerances built into the product specifications for the
products we manufacture. Some wastes are “common cause”
which means they are purposely included in the process design. For
example, when we trim off metal from a stamped part when it is
being formed is common; in fact very common. The waste has to
be created in order for us to stamp out the part or we will deform
it during the forming process. Another more simple form of
common cause waste is the paper this book is printed on (if you
have a hard copy, if not use your imagination). Notice there are
blank spaces on the boarders top and bottom and both sides and
even blank pages with no information on them at all. When you
purchased this book you had to pay for those blank spaces with
absolutely no information on them. Hopefully you are getting value
out of this book and the information in it, but there is no value for
you where there is no information. Would you purchase this book
if it had no boarders; if the information was printed from end to
end of the pages with no blanks to separate the information?

65
Probably not! These types of waste we know exist and are
“common”; therefore we are typically willing to pay for them.
What if you opened this book and the font was so large that it
made the book twice as thick with twice as many pages? The book
would cost twice as much and you would probably not be reading
it now. This type waste is known as “special cause” waste. It actually
takes away from the value to the point where the customer is no
longer willing to pay for the product, or is dissatisfied with the
product as compared to what they had to pay for it. Maybe the
publisher decided to print the book onto a different format that
was designed to print large books such as encyclopedias. The larger
format may be great for encyclopedias, but very wasteful for short
constructive materials such as this book. Now think about this. Do
you own a house? Studies have found that about 8,000 pounds of
waste are created when building a 2,000 square foot house. Who
had to pay for that waste? Of course, the person that purchased the
house had to pay for all that waste. The main contributor to such
waste is a poor process design that allows the waste to occur. When
processes are set up with one major goal in mind, such as
completing the construction of the house, processes designers tend
to do whatever it takes to achieve that goal with very little
consideration to the waste that could be created (and the customer
that has to pay for that waste).

66
Organizations tend to focus their process design goals on two of
the three major factors that make up the process. One- Make it fast,
Two- make it good, and Three- make it cheap. I once saw a sign
hanging in a mechanics shop that read: Good, Cheap, Fast……Pick
any two! Don’t get me wrong, achieving two out of the three is
sometimes impressive. If however we do not master all three there
will always be waste someone has to pay for though. I try to stress
to my suppliers and those I consult with to consider an alternative
method to process design. Consider only one factor – take the
Value Added Approach. Design the process to create the product as
valuable as possible for the customer. In other words, remove all
the waste that the customer has to pay for to give them more value
for the money they pay for the product. My brother had a house
built a few years ago before the real estate bubble burst. He ended
up selling the house a few years later in one of the worst markets
in history and still managed to make a nice profit on the house. The
reason for this was he used a value added approach during
construction. He made sure the process of building the house was
designed so waste was kept to the bare minimum. Mainly, the
process of delivering only what was needed to the job site played
the biggest role in controlling construction waste. For example,
when the sheeting material arrived to construct the roof he met
with the site manager and informed him there was only enough to
cover the roof with nothing to spare. He provided the site manager

67
with a blueprint of exactly how the sheeting must be cut so there
would be no waste. He informed the site manager that if his
builders made a wrong cut or used a sheet of material for only one
cut then discarded the remainder that he (the site manager) would
be responsible for any extra material cost. After the roof was
covered the scrap pile had only minor scrap pieces in it. When he
showed the site manager the value of the material and informed
him of what he was willing to pay for the process was adjusted
accordingly. By using the value added approach for this situation
my brother was able to remove the intent, opportunity, and
capability of producing waste. For example:
 Intent = He was only going to pay for enough material to
cover the roof
 Opportunity = He only delivered enough material to do the
job
 Capability = He gave the site manager the precise layout of
how to make the cuts
Taking a value-added approach to process design should always
be a consideration during the actual design phase and before the
process is delivered to operations to produce the product.
Continuous Improvement Engineers (CIE) across the globe spend a
majority of their time “improving” existing processes that have
already been producing parts. Millions upon millions of dollars are

68
wasted every year because of a poor process design and failure to
use the value-added approach during the design phase. I recently
worked on a process with a supplier where we were able to reduce
the cost of the finished product by over $100,000 a year. That’s
great! However, the bad news was the process had already been in
operation for over 3 years. This is a perfect example of how the
supplier spent very little time during the process design phase
costing us over $300,000 before we found their mistake.
So enough on what we should have done, let’s focus our
attention on what we can do. Taking a value-added approach to
process design is actually quite a simple methodology. It should be
part of the every-day operations and instilled at the systems level
of the organization so it remains an eternal part of our business.
The first task of taking a value-added approach to process design is
to develop the criteria. In Lean it’s called the Ideal State. The Ideal
State is really how it reads; it’s how the output of the process would
“ideally” perform when it is running or how the process should be
designed in an ideal state. Creating the Ideal State criteria defines
the “goals” that we would like to achieve when we actually fire up
the process and produce the parts. The Ideal State criteria are like
handing over the operating rules to the process designer so he or
she can design the process to meet those goals. Imagine trying to
design an assembly line that produces sandwiches. From your
experience of making sandwiches at home you would probably

69
have a pretty good idea of how to set up the process. But, have you
ever had to make 10,000 sandwiches a day? It may become very
difficult for you to set up the process the very first attempt where
you met all the targets such as quantity produced per hour, total
manufacturing cost, as well as quality targets. Wouldn’t it be nice if
someone handed you a list of goals to shoot for during the planning
phase of the design that would not only help you achieve the
targets but would save you many hours of improvements later on?
Ideal State targets are comprised of just about any attribute that
can be measured on the process. Typical Ideal State attributes are:
 % of value added work (what % of the work actually adds value
to the product)
 Motion, how much does the operators move around to perform
their tasks
 How much square footage does the process consume
 How long does the operator spend inspecting the product
 How quickly can the process be changed over
 What is the total time it takes to perform each step (are they all
equal)
 How much time does the operator wait for the machine to
complete the cycle
 How much time does the machine wait for the operator to finish
their tasks

70
 How much inventory is at the process (raw materials and
finished goods)
 How many defects are produced
 How far are materials transported from storage to point of use
 How many extra parts are produced with each set up
The list above is the most common, however it is definitely not
all of the attributes that could be measured or used to establish
Ideal State goals. One of the greatest benefits of establishing Ideal
State goals is they can be used across the organization to set the
standard for all processes. For example, let’s suppose we
established an Ideal State goal for how much finished goods
inventory can be at a process at any given time. We set the goal to
be no more than 2 completed pallets. Notice that we did not set the
goal to be part quantity specific because each process may produce
more than others. Now, if we were given the task to design and set
up a new process we would design the process so that only 2 pallets
would fit at the process (we limit the opportunity). By doing this we
have controlled the amount of inventory that could be stored at the
process. By designing the process with this particular Ideal State
goal we have also removed the Intent, Opportunity, and Capability
for more than 2 finished goods pallets to be placed at the process.
By using this Ideal State goal for every process, we could look across
the shop floor and clearly see if there was excessive finished goods

71
inventory. It may also help us better control inventory flow,
material handling operations, shipping, and the use of floor space.
We do not arbitrarily create Ideal State targets. Ideal State
targets must derive from somewhere and must have meaning in
order to be a contributor to our success. For example, what if we
were to have an Ideal State target that requires all operators to
work within the “gold zone” 90% of the time? Gold zone is the area
within arm’s length of the operator. Suppose the machine cycle
time was much longer than the operator task time and the operator
could easily step over to another process to unload the tray and
keep another machine running. The operator would surely be out
of his gold zone more than 10% of the time, so if we had this Ideal
State across the organization we might actually be creating waste.
For this reason, we always build in exceptions. However, we would
still need to consider the Ideal State targets during process design.
What if we had the Ideal State target of 90% in the gold zone when
the process was created? We might have put the two machines
closer together so the operator could simply turn and unload the
additional machine versus having to walk over to it. It’s better to
have some goals than having none at all during the process design
stage. We can always make exceptions as needed.
So, where do Ideal State targets and goals derive from? The
simplest answer is: they derive from our strategic plans or
operational excellence plans. With just a little effort, an

72
organization should be able to develop the highest level Ideal State
targets and then define them even further as they work their way
from systems to processes to the part level. Using the example
below, try to mentally create an Ideal State target at all three levels
for your organization.
SYSTEM: Increase profit by 10% in 2016 by removing unwanted
waste in our processes.
PROCESS: Improve overall machine uptime by 25% by decreasing
non-value added activities by 15%.
PART: Increase productivity by 25% on the mill by balancing
operators task time to the machines cycle time.
As you can see, we drove the Ideal State target down from the
Systems level all the way to the part level. We will increase our
profit if we can remove non-value added activities allowing us to
improve the productivity. The table illustrates some common
examples of Ideal State targets that I have seen incorporated into
the process design (the recipe).

73
The key solutions for designing a process using a value added
approach are:

74
 Always consider the process from the value stream perspective
 Drive your Ideal State targets from the top down to support
your strategic goals
 Use the Ideal State targets to design your processes
 Continually improve upon your Ideal State targets to reach
higher levels of efficiency
 Develop standardized Ideal State targets even if you need to
make exceptions

75
#6 – LOOKING FOR THE ROOT CAUSE, NOT WHO TO
BLAME
Not long ago, I was sent out to work with a supplier that was
really struggling to produce enough parts to fulfill orders and was
having a hard time keeping their cost down on the products they
were able to produce. There was a very high probability if
something was not done the company would lose business, and no
doubt be in dire conditions for the future. The company was in a
small town in the South, employed about 50 workers, and their
major processes were welding and fabrication. As usual the first
task I performed was to conduct the “Gemba Walk” from end to
end on the shop floor. Accompanying me on my walk was the CEO,
Operations Manager, Project Manager, and the Quality
Representatives. As we walked from process to process I quickly
observed a pattern that none of the top managers had seem to
notice before (they had very poor observation skills). Mind you, this
was a company where the processes were somewhat messier than
most because they did have welding and metal cutting operations.
With almost every process we visited I noticed there was trash on
the floor, scrap parts lying around, and discarded soda cans in the
parts bins, tools and equipment scattered about as though there
were absolutely no rules on organization in use in any form. Most

76
operators were not wearing their safety glasses, had no sleeve
protection on to shield from weld sparks, and were hanging their
jackets on the weld gas bottles as though they were coat racks.
Once we finished the walk I asked the team what they observed.
Pretty much every person in the group stated they simply observed
workers performing their daily jobs and making parts as they should
be. No one reported anything out of the ordinary. I started to
question why there was trash on the floor, soda cans in the parts
bins, why everyone was not wearing their safety glasses, and why
they were using the gas bottles as coat racks. The team seemed
shocked that I was pointing out such simple failures when the
bigger issues were capacity and the high cost of production. They
assumed after the walk I would point out the major problems that
were causing them such turmoil and would just hand over the
solutions to solve everything. As I pressured them for answers, I got
the typical blame game responses. They offered every excuse from
not having enough time to keep the areas clean, to operators not
being “seasoned enough” to follow the rules (meaning the
operators were new employees). Not one root cause did I hear in
all the responses being lobbed at me. During our on-floor discussion
the Production Supervisor approached our group to speak with the
Operations Manager about yet another fire that needed to be put
out. After a brief moment I politely interrupted and asked the
Production Supervisor if the company had a policy on wearing

77
safety glasses. He took a hard swallow, looked the Operations
Manager in the eyes with embarrassment, hesitated, and
reluctantly said “yes we do”. He then turned his back to us, took his
safety glasses out of his pocket, and put them on.
The clear and apparent problem they were having was the
blatant disregard for even the simplest of system rules. How can
you expect operators to follow process specific rules when the
system rules are not enforced? I must admit, out of my twenty five
years of process engineering experience one of the most creative
excuses I’d ever received came from this particular Operations
Manager. He stated the root cause of all their problems was (and I
quote), referring to the employees: “They just don’t give a @#%&”!
I politely replied that I didn’t believe that particular root cause was
the contributing factor for all the anguish the company was
experiencing. It did however prove a point that most of us will do
our very best to assign the blame verses seeking out the true root
cause.
Just as the example we used in #10, most failures we observe are
merely symptoms of the real root cause. Out of date food in the
freezer and failing to clean the bathroom at the prescribed
schedule indicated a failure of control. Trash on the floors, soda
cans in the parts bin, and not wearing safety glasses are also
symptoms of something much bigger. We could easily blame the
operators for not doing their jobs and call it a day I guess. However,

78
something at the systems level has also tragically failed that we
need to solve in order to prevent it from occurring again and again.
Both cases will continue to occur and will continue to result in loss
of profit and or losses in capacity. Having systems in place does not
by any means guarantee they will be followed. Trying to solve the
individual observations one by one can be done, but will be futile if
the problem is not also solved on the systems level. We must use
our observations to find the root cause at the systems level, by
addressing the symptoms and making the determination of what
those symptoms all have in common.
Focusing on the root cause provided by the Operations Manager,
I walked the team through a very simple exercise to see if we could
track the problem back to a systems issue. Here are the questions I
offered up for them to answer:
Why do you think operators don’t give a _____?
Why do we have systems in place that can easily be ignored by the
operators?
Why are Managers ignoring the policies in place?
Why are Supervisors displaying bad behaviors and bad habits (later
that morning I observed him again not wearing his safety glasses on
the shop floor)?
Why do you think employees are ignoring process rules, cutting
corners, and creating multiple defects?
All the above questions came from the observations made

Achieving Op Ex Michael Ray Fincher copyright 2016

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