“Forty-Second Boyd” and Getting Inside the
Decision-Cycle of Your Machines to Fail
When I read
“Boyd – The Fighter Pilot that Changed the Art of War” by Robert
Coram, I recognized a useful tool. Through analysis and preparation in advance,
Col. John Boyd USAF could defeat aerial opponents with the advantage
over him in 40-seconds by getting inside their decision-cycle and
pressing the continually improving advantage. He had analyzed the options in
advance and had such a complete knowledge of all of the potential outcomes of the
fight that he could act faster than his opponent and always be several steps
ahead. The outcome became inevitable. This knowledge was formalized in his
Energy–Maneuverability Theory. He also simplified the process of winning
by getting inside the decision-cycle of an adversary with his famous
OODA loop. This refers to the decision cycle of observe,
orient, decide, and act. His contributed to the success of
aerial combat and Marine Corps ground combat by offering a known path to success
that works. His message, “Make decisions faster than you adversary
and you can win.”
What if your opponent is not an
What if your opponent is a dumb machine?
If you are responsible for
the maintenance of machines, facilities, or natural resources; how is it possible
to get inside the decision-cycle to fail of these single-minded adversaries that
are trying to grind themselves into pieces every minute they are in operation?
The obvious answer is
preventive, remedial, predictive, and preemptive maintenance and we are getting
better tools to forecast the condition of the assets every day; however, the
real challenge is not knowing the condition of the machines but getting the
permission to take the machines out of production for repairs before they fail.
A maintenance manager can provide reams of charts and graphs to the operational
authority for permission and the information seems to bounce off them.
The most challenging task
in managing maintenance is to get the right information to executive management
that can be understood in 60-seconds. If that can be accomplished, the
executives will listen for an additional five minutes.
It is in those six
minutes that a maintenance manager must make and prove their case for the
time and resources needed to get inside the decision-cycle of the machines to
fail and restore those parts of the machines that were consumed during the
creation of wealth for the investors so they can continue to create more wealth
in the future.
technology used to create wealth has become so complex that it is unreasonable
to expect executive leadership to sustain a working knowledge of the technical
details so a maintenance manager must translate the technical details into
concise financial terms that can be digested in 60-seconds.
The Geaslin Group has created a
series of special management tools to transfer the significance of technical and
mechanical needs into
rules of consequence and
financial ratios that will quickly deliver the gravity and urgency of
seemingly small maintenance needs across multiple levels of the organizational
We teach how
to deliver to the executive management a
core belief for managing maintenance that will produce the lowest
maintenance cost per unit of production possible and prove that all other
options will create a higher cost. Understanding this core belief will allow an
executive to cease being a spectator in the management of maintenance and
actively participate in the managing of the production assets that are creating
wealth of the investors.
In 2001, I discovered the
Inverse-Square Rule for Deferred Maintenance. When used correctly, this
tool offers the maintenance manager historical proof positive of the exponential
costs associated with operating an asset to failure. This tool then uses that
past knowledge to predict the cost for allowing an asset to operate to failure
by simply squaring the cost of the primary failure part to know the total cost
for the company to recover from the first level of operating to failure.
Squaring that value will predict the cost of allowing the breakdown event to
progress to the next level of failure. And, squaring that value will predict the
cost of allowing the breakdown to progress to the next level of failure.
When this exponentially
escalating cost in money and energy has been accepted as true by the executive
management, the disastrous effects of deferring maintenance should be readily
obvious and the time and resources needed for an early intervention should be
But there is a fly in the
ointment. Not every maintenance item that is deferred completes the journey to
failure before it is repaired. When the effort is applied to computing the rule
and the ratio, you will discover that those few machines that are allowed to
operate to failure (Breakdowns) will account for 60% of your maintenance dollars
How do you apply a risk
factor to the decision-making process that can be communicated in sixty-seconds?
I teach using the cost
numbers gathered while proving the Inverse-Square Rule to compute the
True Risk/Reward Ratio for Deferred Maintenance. This is quite simple.
Divide the early intervention cost (The cost to fix it before it fails.) into
the total breakdown cost to the company. When my clients use this method with
their numbers, they are stunned that the risk/reward ratio is seldom less than
60:1. Offering this absolute knowledge, the proof of consequence for not
repairing the asset, to executive leadership will allow them to see the benefit
to be gained from getting inside the decision-cycle of the machine to fail.
From the time a machine is
assembled it begins failing by corrosion or storage damage. The machine begins
wearing out from the first moment of operation and the optimum life is finite
but cannot be cannot be predicted no matter how many accurate data points are
collected because of the
nonlinear forces it must operate in.
So, if it is not possible
to predict when a machine will fail, we must act at the earliest possible moment
to get inside the machine’s decision-cycle to fail. You must pretend you are in
a submarine and feel a drop of water on your head and act quickly.
I offer a maintenance
philosophy and management techniques to create a body of knowledge within an
organization that will offer a distinct maintenance path to creating the lowest
maintenance post per unit of production possible. Using these computations
allows anyone within the organization to see the dramatic and exponential
consequences of deferring maintenance and operating an asset to failure.
To win at maintenance, all
you have to do is convince your executive leadership that the consequences of
deferring maintenance is exponential to the whole organization and that early
intervention stops this dramatic penalty. To employ my philosophy does not
require your leadership to change anything but their minds as to the real
risk/reward ratios and allow the maintenance manager to intervene early.
Know the energy
states of your maintenance options.
Decide to get
inside the decision-cycle of your machines to fail.
Execute an early
exponentially escalating expenses that square with each successive level of
failure. (Inverse-Square Rule for Deferred
And, plow back the
exponential savings in man/hours and money into more early interventions.
Winning at maintenance is just that
If you should be
interested in understanding my computations, I will send you an Excel
spreadsheet to run your own computations to confirm the validity. I will require
that you spend about ten minutes on the phone with me to explain the functions
on the spreadsheet to be sure your data is applied and interpreted correctly. If
you should care to visit, I am at your service.
David Geaslin is a
graduate of The University of Texas at Austin with degrees in Industrial
Management & Marketing; a former Marine Corps Aviator and Aircraft Maintenance
Officer (1968-1975); the CEO of his maintenance service company for 15 years;
and has consulted offering coaching and seminars in the management of
maintenance since 1990. He lives in Gonzales, TX and travels offering his
services wherever needed.
Houston Mobile: (832)