By David Tod Geaslin
earned my degree in Industrial Management & Marketing from the University of
Texas in 1967 (Back during the Jurassic Period) and I can never express
adequately how much I learned and what that education has meant to the quality
of my life. After my sophomore year, I never had an instructor that did not
"wow" me with knowledge. The constant revelations of the case studies by
efficiency experts made me want to be one.
However, all through my junior and senior years, there was one nagging
inconsistency that haunted me. That being that after being exposed to the
wonderful management tools offered in the case studies at Ford, Westinghouse,
General Electric, Boeing, etc. I found that they were all but useless when I
tried to apply them to my father's small, three-pump gas station and garage back
first attempts to explain and institute these types of scientific management
controls were met with good natured smiles from my father (Like he knew
something I didn't know.) but he supported me in attempting to put something
back into the small family business that was paying for my college education.
Some things worked so well, like our new inventory control policy, that those
innovations actually repaid him for all my college expenses; however, even the
ones that worked seemed to be some sort of photo-negative image of
the real industry models.
Eventually I decided that the problem with my father was what
SAP now calls a "corporate culture" issue. In my
youthful examination of the challenges trying to get my dad to do all the
accounting and measuring that I needed, I came to the conclusion that you can't
teach old dogs new tricks and abandoned my attempts to apply the scientific
management methods to the family business. After graduation from the business school at UT, I went
into the Marine Corps as an aviator and was never again directly involved in the
family business and you know what? It did just fine without my scientific
management method and expert advice. However, I couldn't wait
to get into a job where I could really apply my education.
1969, after finishing flight school and into squadron maintenance, I
believed that my degree would allow me to make significant improvements in
aircraft availability but was I wrong again! Compared to other squadrons, we had
a very good maintenance function but very much less than I felt was attainable.
Every attempt to apply industrial management and engineering methods to
maintenance at the attack squadron level fell apart in very short order.
Shortages in parts, high turnover in maintenance workers, significantly reduced
pilot training, and sometimes less than yearly changes in squadron commanders
cancelled each attempt to create an optimum aircraft maintenance effort. I
suddenly knew why my father smiled when I told him how my high-powered
management system could change his business. I realized that there were too many
variables to control and the data collection necessary to get enough data points
required extra personnel that we didn't have. My magnificent management sandcastles were
knocked flat with each rising of the tide.
was about then that I made a conscious decision to stop trying to control
the events that constantly thwarted my attempts at improvement. It was
not an easy decision to abandon the scientific management path that had served
others in industry so well. I made a plan to stop trying to control that which I
could not control and institute a policy of early detection and early
intervention to give me some advantage over each maintenance event. I decided to
catch and repair every problem as early as possible. In effect, I decided to
aggressively treat each symptom of trouble as soon as detected. Since I was not
allowed to have the resources to eradicate maintenance diseases, to succeed I
would catch each "illness" in the earliest stages and short-stop each disaster
whenever possible. Although it was not the solution that I wanted, it was better
than a sharp stick in the eye. My life as a manager of maintenance
stopped letting the lack of higher strategic leadership be the driver of
my maintenance resources. I began ignoring that which I could not control and
concentrated on what I could. I let each detected individual failure event of people,
parts, and leadership become the driver of my resource allocations to snuff out
each cigarette butt before it grew into a forest fire. Early detection and
early intervention became my mantra. It worked! However, again I noticed
that my solution seemed to be like a photo-negative of the textbook scientific
management solutions. I was having superior success, but each success was
reactive rather than a scientifically planned solution.
decided to leave the Marine Corps and in 1975 and started my own business
applying my aviation maintenance techniques to fleet preventive maintenance. My
company was a mobile PM service for commercial truck fleets. I applied time and
motion studies to the servicing of trucks and became the local preventive
maintenance guru for fifteen years. I was happy as a clam. I now owned my own
business and was not limited in my management options. I attempted to implement
what I thought would be a good scientific business control function but quickly
realized that I could "control" my workers production interactions
with the vehicles using time and motion studies, technology, and training but I
had little control other than that. I could be efficient servicing a truck, but I had
no control over when and where I would get a customer's
truck to service. Again I went to the "photo-negative" and began
responding to each scheduling "symptom" with gusto by creating an alternate PM Plan
for my clients. It worked again but I was not happy with the lack of control
when we were servicing up to a thousand trucks a month.
enrolled in an MBA program in my search for a method of control but withdrew because the
university offered no new options that would apply to my type of business. I
began to read the many authors such as Peters, etc. always looking for a new
option for control. After the energy crisis of 1973 I began to hear about The
Deming Method and although I didn't attend any of his classes, I studied
everything I could of his method and became a big fan. I tried to implement his
method as best I could but again there was always a point where the
"photo-negative" of The Deming Method provided better
results. I studied the Six Sigma and TQM methods and found them
to lose effectiveness at the point where I needed them the
would like to make it clear that I have never doubted the effectiveness of these
methods. The proof is there that they can work and work well. I had the highest
confidence that I was capable of implementing these types of programs and yet I
knew they would not work in my maintenance application. These methods
worked well in other maintenance applications such as refineries, production
lines, and process intensive operations so I asked myself, "If my failure to
control was not the Six Sigma process and it wasn't me, what could the problem
be?" In 1992, I discovered the answer.
1988, I had began to offer consulting seminars in the management of maintenance.
First for truck and forklift drivers and then to management. In 1990, I sold my
fleet PM service and began consulting full time. In 1992, while doing some
budget work for BFI, I read the book "Chaos" by James Gleick. The
name for this field of study is a misnomer. The Chaos Theory is the study of finding hidden order in
dynamic and apparently chaotic
systems. It is the organized study of nonlinear systems that seem
to be unpredictable in their result. The book constantly compared linear systems
that can be measured, quantified and analyzed to nonlinear systems that cannot.
Nonlinear systems have so many immeasurable variables that it becomes
impossible to quantify or predict their outcome no matter how many
accurate data points you have.
Reading about The Chaos Theory was like that cartoon "light
bulb" coming on inside my head! The companies that have successfully used
The Deming Method, TQM, and Six Sigma to manage their
operations and maintenance issues are linear business processes.
Each station in the linear production process is embedded or physically attached
to the process. Each linear station is dependent on the station before it and
responsible to the station after it. If any station becomes unreliable the
entire process is threatened and because the process is measurable, the cost of
the unreliable station can be computed, risk/reward decisions made, training
quantified, and actions taken. Philosophies such as Six Sigma excel in
this environment because the process can be defined,
measured, analyzed, improved, and
philosophies work wonderfully in the linear arenas such as scheduled airline
operations, food processing, steel production, automobile assembly plants,
refineries, pharmaceutical manufacturing, human resource management, and
accounting functions such as accounts payable and accounts receivable. When any
part of these processes are interrupted, the final delivery to the customer is
threatened. That creates an immediate alert and executive management becomes
personally involved in the solution and guarantees the necessary resources at
the right time to avoid the challenges in future operations.
My revelation revealed to me that the processes that I had been
supporting were nonlinear. Nonlinear processes such as truck
distribution systems and tactical aircraft (Unscheduled) operations were not
initiated by scheduled events and were in fact tasked by unpredictable events
and demands. These types of operations are not physically attached to the
process and in many cases the assets are interchangeable when delivering remote
services. If one truck or fighter plane fails to perform, the entire flow of the
process to the end user (Customer) is not threatened. Another interchangeable
asset is diverted and the critical part of the process continues at the expense
of a non-critical part of the process.
Top management attention and resources are not brought into the
problem/solution until enough individual pieces of the non-critical parts of the
process can no longer support the critical parts of the process. Then
the bells and whistles go off and executive management becomes involved only
after the process has deteriorated and heroic interventions are needed to assure
the final flow to the customer is restored.
Nonlinear maintenance functions in arenas such as
agriculture, health care, construction projects, trucking, facility management, warehouse
operations, spare parts inventories, and pizza deliveries seldom get top
executive management attention until the customer complains or a collapse is
I have learned from The Chaos Theory and Dr. Lorenz's
"Butterfly Effect" is that no matter how hard you try,
nonlinear events cannot be predicted with any accuracy in other
than the very short term. I now have a true understanding that the maintenance
of assets in nonlinear applications cannot be predicted. An example:
The maintenance needs and operating cost of a slurry pump in a
linear-process steel mill sump that is used to transfer suspended material
that is consistently uniform can be predicted with a high degree of
The same slurry pump used to empty or dewater sinking barges in a
ship repair yard may encounter any unknown liquid or material imaginable and
in any concentration or level of abrasiveness. The maintenance needs and
operating cost of this pump is not predictable.
All of my professional life I had believed that if I had more and more
and better and better data points about the assets to be maintained that I would
be able to predict maintenance needs and maintenance costs...
I no longer
believe this to be true!
came to question the validity of trying to apply successful linear maintenance
solutions (Such as Deming, Six Sigma, etc.) to nonlinear maintenance
applications. While examining the point at which linear maintenance functions
become nonlinear I realized that I needed to define a crux, a pivot point, at
which the change occurs and it was not an easy task. To do so I went back to
some of my resources and looked for a key.
consider Six Sigma to be a very effective tool for managing linear
processes and the maintenance to support them so I dusted off my reference text
titled "Six Sigma" by Mikel Harry and Richard Schroeder and found
the reference I was looking for. In discussing Six Sigma's breakthrough
strategy on page 23, I quote, "The Six Sigma Breakthrough Strategy is a
disciplined method of using extremely rigorous data-gathering and statistical
analysis to pinpoint sources of errors and ways of eliminating
not believe that there is a better way to describe the philosophy for
scientifically managing linear processes and the supporting maintenance. In a
linear process, this is the best type of approach one could apply and it
works. However; I have learned that no matter how hard you try to control a
linear process, production line, refining process, or manufacturing environment,
at some point the process will always become nonlinear, chaotic, and
impossible to measure.
think of nothing that is more disastrous than to try to manage a
nonlinear process with a wonderful linear method like Six Sigma because
the nonlinear processes cannot be measured.
a statistical process such as Six Sigma, The Deming Method, or
TQM attempts to control a nonlinear process:
best it fails.
the worst it succeeds and defines, measures, analyzes, and
renders a solution that is no longer valid.
linear process control like Six Sigma does the most damage to a nonlinear
process when it succeeds in gathering data points and attempts to fix a problem that no longer
exists! Nonlinear processes are extremely dynamic. By the time enough
measurements have been taken to formulate a solution, the problem vector has
either subsided or morphed into something very different.
management has a high confidence they have found a solution (Through Six
Sigma, etc,) based on measurement, they become very energetic
in the application of that solution. Unfortunately, in nonlinear processes, by
the time enough data points are collected to act, the problem has
evolved, changed, or no longer exists and the energetic
application of the now false solution only creates confusion. In the case of
cyclic or periodic nonlinear events, the scientific method will actually reinforce the changed vector
producing something like pilot-induced oscillations in aircraft and will amplify
is no one-stop solution to managing maintenance and attempting to apply linear
solutions to nonlinear problems actually creates problem maintenance and safety
discussing this concept at a steel mill I told an executive, "You can
control your linear process costs to a penny a ton inside the walls of the melt
shop and rolling mill; however, your process becomes nonlinear at the door going out
and into the scrap yard, and becomes chaotic at the gate when your trucks pull
out onto the Interstate."
trying to explain to him that he had achieved excellent linear process control
inside the mill but outside in the yard where scrap was being brought in on rail
cars and by truck, he had very little of the process control enjoyed in the
mill. Forklifts, front loaders, cranes, grapples, switch engines, rail track,
switches, conveyor belts, scrap shredders, magnets, metal sorters, and a
thousand other moving, grinding, bumping, sliding,
and reciprocating actions defied all attempts at control through
monitoring or measuring. The sum of these functions amplified by
unpredictable rail delivery schedules resulted in an almost totally nonlinear
Even worse than that was when his hundreds of trucks left their
yards with the products, all control was lost! When the drivers entered the
Interstate Highway System and municipal streets, the result of that interaction
was totally nonlinear and almost chaotic. Constantly changing street and traffic
conditions, weather, vehicle and tire condition, driver mental state, and the
random acts of other drivers offered too many variables to predict any useful
management information. All managerial process control had been lost at that
point and any attempt to measure and predict those nonlinear activities was
example could be the attempt to quantify the causes of suspension maintenance in
a delivery fleet. A quantitative solution would be to measure the problem over a
period of time to determine the cause of the damage, but would that actually work?
Assume that the road
surface in Houston at the interchange between IH-10 and IH-45 has degraded into
potholes that are damaging the fleet traveling from San Antonio to New
Orleans. After two months of study, the decision is made to divert
all east/west delivery operations onto Loop 610 North.
Unfortunately, during the time it took to measure the problem, the
state, always behind on their street maintenance, has repaired the IH-10 /
IH-45 interchange and the surface of the Loop 610 has begun to
deteriorate. Applying the measured solution to a problem that no longer
exists has created two more problems.
Logic and data collection has provided a very good solution to a
problem that no longer exists. This is where an alternate solution to the
scientific method is needed. This is where the "photo-negative" solutions
to nonlinear problems can provide much better solutions.
Is Your Process Linear or Nonlinear?
To consider an alternative
management solution, you must first decide if your maintenance function
supports a linear or nonlinear process. Remember the slurry pump
mentioned above? You may have like assets used in both types of operations. Or
you may have a linear process that can be controlled only to a point by the
scientific method and then it becomes nonlinear and chaotic.
If you are having doubts as
to whether nonlinear processes and the supporting maintenance actually exists,
all I can do is ask you to look at that point in your total process (From raw
materials to the final delivery of your product to your customer.) where
your linear process control fails to provide the control you expect. That is
about the point where your process becomes nonlinear. It is somewhere around
this point that you begin to get managerial feedback like:
"The solution is
undefined because the data points lack accuracy."
"The data is not
being collected correctly."
"There is a
'corporate culture' problem in that department and they refuse to learn or buy into our solution."
Or "We have
told them what needs to be done but they just won't do
experience is that this is a pretty good place to look for the boundary between
linear and nonlinear processes. If your solutions almost work, or they worked
before but the problem just keeps coming back, or if the challenge or problem
has been put on the "back burner" because the resources needed to
measure do not warrant the result; then you have begun to define your
linear/nonlinear boundary. Another way to define your linear/nonlinear boundary
is to examine the credentials of your managers in each department.
Linear processes have engineers, process engineers, industrial
engineers, MBA's, and accountants as managers.
Nonlinear processes do not!
can perform a quick litmus test. How many manufacturing companies do you know
that operate large truck distribution systems and have a mechanical or
automotive engineer as the Fleet Manager or Fleet Maintenance
Assume that a fleet consists of 1,000 tractor/trailer units valued at
$150,000 each. That is an investment of $150 million in high production
and high risk assets. Would it not be a wise and prudent policy to put an
automotive engineer over the maintenance of that $150 million in assets? Yet how
many transportation departments have an engineer managing those truck assets? I
personally have never been in a company that had a mechanical or automotive engineer directly responsible for
a truck fleet. I am sure there must be one somewhere, but I have never met
Another quick test of whether a process stops being linear is to examine
the credentials of the managers of the maintenance functions in an industrial or
manufacturing company. The Process Maintenance Manager will be an
engineer and the Fleet/Mobile Equipment Maintenance Manager (With a
thousand trucks and trailers, loaders and forklifts.) will be a former
mechanic. The Facility Maintenance Manager (In a $140 million building.) will probably be a former janitor or building maintenance
Why is this so? It is because one process is linear and the other
is nonlinear. I believe that this is because engineers will not take a
nonlinear management position even though the asset investment should seem to
demand it. Engineers are solution-oriented people. Their whole adult lives has
been devoted to creating scientific solutions and it is my opinion that
engineers will not take these truck maintenance (Nonlinear) type positions
because they know (At some level.) that their scientific management techniques cannot succeed. A
qualified engineer can have their choice of jobs and they are not going to
accept a nonlinear task that has no scientific solution or at worst could result
in failure. People do what they like doing and engineers like to solve
problems that can be solved. These points that I have mentioned are not written
down anywhere, they are just quick-test answers that keep proving themselves day
you apply these quick tests and feel they are accurate, then you must consider
that the scientific and quantitative solutions taught in our business and engineering
schools have limitations that begin at the linear/nonlinear boundary.
Managing Nonlinear Maintenance Challenges
If you canít
Define, Measure, Analyze, Improve,
and Control what can you
Attempting to force a linear solution onto a nonlinear problem only
creates more problems and safety issues and management should look for an
alternate solution. I have created a management and maintenance philosophy (The
Geaslin Method) based on "Early Detection" and
"Early Intervention" that is designed to identify the linear/nonlinear
boundary in an organization and create a vertically integrated, self-financing
program to tame the
"Inverse-Square Rule for
Deferred Maintenance". It appends to the successful linear management systems at their linear/nonlinear
consider the prime detractor of linear solutions in nonlinear applications to be
that the scientific collection of data points can only create a "revenge"
solution. An example:
In the early days of man-portable infrared antiaircraft missiles
the Redeye missile was a "revenge" missile. The IR sensor technology only
allowed the defensive missile to lock onto the enemy aircraft's hot exhaust
pipe metal. Therefore, you could only take a revenge shot after the enemy
plane had already bombed you and was flying away!
As technology improved, the IR seeker became "all-aspect" and could
lock onto an enemy aircraft's hot exhaust plume while it was flying toward you
and you could shoot him down before he could bomb you. This was a significant
proactive improvement to say the least!
Six Sigma and other philosophies are "all-aspect" solutions in
linear applications because they can predict the future by measuring the
Six Sigma and other philosophies are "revenge" solutions in
nonlinear applications because they only record the past disasters. Because
nonlinear problems change faster than useful data points can be collected, trying to use these data points to predict the future is futile.
have discovered that when coping with a nonlinear situations, a manager must create a method that maximizes
the "Early Detection" and "Early
Intervention" of every
single maintenance event and they must aggressively treat each symptom as it presents
In dealing with nonlinear problems, management must not wait for revenge information to be gathered
and analyzed to create an action plan. To
delay maintenance action in nonlinear situations will trigger my
"Inverse-Square Rule for Deferred Maintenance" that states,
"Any part that is known to be failing and left in service until the next
level of failure, will create an expense equal to the SQUARE of the cost of the
primary failure part."
penalty for not using early intervention will be the square of the
primary failure part or a minimum of 15-times the difference
between the early intervention invoice value and the breakdown invoice
If a $100 bearing in an industrial electric motor
is failing and not repaired when the machine demands it (Timely repair
invoice value of $667 parts and labor.) and is allowed to remain in service
until the bearing fails and the rotor damages the stator, the repair and rewind
invoice will easily exceed $10,000.
"Inverse-Square" rule proves itself every day and it is easy for
an executive to confirm without exhaustive "revenge" data points. Do you
have a maintenance event in your "action file" that
is so stinky that it will have to be examined by top management? If so, I
have another quick test for that repair order.
1st ~ Put the direct maintenance cost into your calculator.
2nd ~ Add the indirect cost associated with the event to the RO value.
(Idled workers, ruined materials, lost production or sales, customer
3rd ~ Total the direct and indirect expenses.
4th ~ Then tap the SQRT (Square Root) button on your
5th ~ Is that value the price of the primary failure
Does that value represent the cost of the
part that if repaired in a timely manner would have avoided the
excessive costs and disruptions to operations and customer service? If so, you have just seen the evidence to
prove the validity of my "Inverse-Square Rule for Deferred
Maintenance" and the disastrous consequences of deferring
When it comes to deciding the resource
allocations to achieve "Early Detection" and "Early
Intervention", I suggest that managers pretend that they are in a submarine and
feel a drop of water hit them on the top of their head. They should not stop looking until
they know where
that drop of water came from! It may be condensation, but it may not be.
It might be a leak in the hull! The risk/reward penalty difference between
condensation and a hull leak is dramatic and they should aggressively
investigate and treat each
symptom as if it was a hull leak. That same urgency should be applied to any
nonlinear maintenance event.
Once we learn how to recognize the linear/nonlinear boundary, it
becomes possible to identify:
How maintenance budgets
fail. (The Inverse-Square Rule)
The trigger that initiates the
failure. (Attempting to apply linear solutions to nonlinear problems.)
How to create a SELF-FINANCING
SOLUTION to improved maintenance.
And how to create a "corporate
memory" for operations, maintenance, and safety to remember "lessons
learned" across multiple financial periods, turnover in technical
personnel, and changes in leadership to assure the continuity of adequate
maintenance funding to avoid the Inverse-Square Rule.
new philosophy of "Early Detection" and "Early
Intervention" will produce the lowest maintenance cost per unit of
production possible in nonlinear systems. All other methods cost more.
If you should care to discuss how the application of wonderfully successful linear philosophies such as Six
Sigma, The Deming Method, Lean Manufacturing, and TQM are actually creating
problem maintenance and safety issues in nonlinear processes, I am at your
David Tod Geaslin