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Ensuring the Longevity and
Successful Implementation Of Your Advanced Maintenance
Strategy (Predictive Maintenance)
Opinion and fact by Michael
Korf-Consultant-National Reliability Systems
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print friendly 160k pdf version
Introduction:
This paper is focused on identifying and preventing the
most common causes of advanced maintenance strategy
implementation failure and more specifically predictive
maintenance program implementations. The ideas
presented are applicable, in most cases, to any advanced
maintenance strategy implementation. A recent report
stated the following as the top 2 reasons why new
business opportunities fail.
32.1%
Poor management of financial activities
14.6%
Lack of management competence or experience
The
design and implementation of any advanced maintenance
strategy should be viewed as the start-up of a new
business. This presentation/paper looks at the
categories of People, Processes, Technology and
Reporting and the root-cause failure mechanisms that
hinder their progress. I believe that the major issues
regarding failed advanced maintenance strategy
implementations fit into these categories. Fishbone
root-cause analysis diagrams are shown to identify
leading problems. ROI/CB methods are specifically
discussed to ensure proper reporting for management.
Recommendations are also offered to predict and prevent
these failure mechanisms from occurring, so that
long-term program longevity can be assured.
Many of the activities
outlined below that lead to business failure are
prevalent in our facilities. But what are the
root-causes for these problems? Poor management of
financial activities can typically be boiled down to
poor reporting practices. Lack of experience can usually
be addressed through improved training. Economic
conditions typically lead to difficulty in getting
access to capital to acquire technologies that may
improve performance. Poor books and records yield a
questionable roadmap as to the “as-is” condition and the
improvements you have made. Sales and Marketing can be
directly correlated to quality (product) and price
(influenced by many factors). Staffing problems can
be equated to poor project scope and mis-communicated
expectations. While there are many root cause catalysts
regarding union problems, often times experience has
shown us that individuals who are happy in their
profession are more focused. The implementation of
advanced technologies can sometimes cure these ills.
Failure to use external advice is intuitive. Rely on
your vendor and networking with people of like minds to
pull you through the rough patches. I believe that by
eliminating several of these issues we can reverse a
trend that is plaguing corporations and keeping
consultants very, very busy.
32.1% Poor management
of financial activities
14.6% Lack of management competence or experience
12.4% Inflation and economic conditions
12.3% Poor books and records
10.7% Sales & marketing problems
9.0% Staffing problems
6.2% Union problems
2.7% Failure to use external advice
100.0%
Courtesy of Coleman
Management Services Inc
The Pain: What
is the Problem?
Historical data reviewed from surveys of PDM programs
yielded the following results. The industry “secret”
that no one bothers to tell you is that
approximately one-half of all programs fail within the
first year. There are a number of factors that drive
this. The chart below shows the data from the survey.
A questionnaire of 25 questions was sent to over 500, of
which 165 responded. Those surveyed were asked to self
rank themselves (it is agreed to be a little subjective)
on a scale of 1-10. We considered an average of less
than 5 over 25 questions as program failure. Our
philosophy was that anything operating at 50% or worse
may not be worth doing at all. I know there will be a
number of purists that argue about subjectivity and a
multitude of factors that could skew these results. I
know. However, I believe that if we ask enough people
the same questions, you will get fairly uniform
resultant responses with a few errant data points. A
modified Delphi approach, if you will.
In the data received, I rounded the number of years the
program was in existence to the nearest year.
The data is based on a
survey performed in 1999.
In year one, of the 165 respondents, 81 individuals
submitted data and ranked themselves low enough to
suggest that their program had failed or was failing.
This trend continued so that by year 5 (and older), only
37 respondents ranked the success of their programs as
successful. That’s a shocking 37 of 165. That is
22.4%. Note: By year five,
only 46 respondents said there program had truly gone
away. By “gone away”, I mean the equipment is sitting
on the shelf collecting dust or being used as a door
stop.
Okay, So What?
Well, the failed implementation of any advanced
maintenance strategy or program at your facility costs
money. In the case of a failed PDM program we can
conservatively estimate that $100k was spent between
equipment, people, training, etc. That equates to some
nice stuff:
-
2004
Dodge Ram Pickup 2500
4DR Quad Cab Laramie RWD SB (5.7L 8cyl
5M) 4 of
them
-
1.2
Shares of Berkshire Hathaway stock
-
5,555
Outback Special Steak Dinners
Of
course this does not include the intangible losses of
credibility and trust. In short, if you are at the helm
of a failed PDM program then you have probably committed
a career limiting offense. It is unlikely that you will
be given a second chance and it is likely that you may
either loose your position or job.
So, the
question then becomes, “Why would I be adventurous
enough to undertake an activity that has historically
shown a propensity for failure?” You might say to
yourself, “I am only a few years away from retirement;
I’ll just ride out my time here in my current
position”. If you’re that individual, then you are
right, DO NOT undertake an effort of this magnitude. I
believe that the Risk-Reward ratio is actually quite low
when the proper people, processes, technology and
reporting are put in place.
WIFM-What’s in
it for me? I mean you.
We all
must ask this, right? I mean anything worth doing is
worth doing well. If you’re like me, then you’re
probably saying if I am going to do it, I need to know
how I am going to win. Well, here is how. Predictive
Maintenance as an advanced maintenance strategy has
saved companies millions of dollars. I have seen the
results first hand. I have seen the leaders of
successful programs glorified by fortune 500 CEO’s at
company meetings and PDM program managers mentioned in
company 10-k reports. I even knew a guy who got a
parking spot right next to the plant managers (I am not
kidding). For those who know what they are doing, the
accolades are numerous. Fame, glory and in some cases,
big bucks were inevitable. Ask me off-line, and I’ll
tell you some guys I know/knew who did it well. What
else? Well, the technologies (for the most part) and the
trends they develop are mathematically sound, repeatable
and have been around for a lot of years. The trends do
not lie. I consider myself a youngster, but even my
first experience goes back almost 25 years. I witnessed
the application of Vibration Analysis for the first time
while serving as a Machinists Mate aboard a United
States Naval Vessel, (USS Mt. Whitney) in 1981. The
technology, housed in large file cabinet size boxes was
lowered into the ships engine room (11 decks down) and
hooked up to the power plants main engines, auxiliary
turbines, generators, feed pumps, and condensate pumps,
etc. Data was collected by the team and helped the
engineering team to focus resources on critical
equipment during the up-coming outage and yard work. I
asked a lot of questions during that cruise about the
technology and knew then that someday I would learn more
about it.
Well,
what about industry trends? I knew you would ask. A
few years ago I read a report by one of the large
consulting firms. It has been around the block so you
may have already seen it. The report published in 1998
by Deloitte and Touche asked plant managers to assess
the number of work-orders generated at the facility and
to categorize them into the following bins:
First,
let me explain the bins. The bin of “Reactive”
(sometimes referred to as corrective) represents a
work-order that was generated independent of the
preventive maintenance schedule. It typically is
reactive in nature since it was not expected or planned
for. The “Preventive” bin represents the
preventive maintenance or time based scheduled tasks.
These tasks are typically derived from the OEM’s
recommendation and are based on some schedule of time,
e.g.; replace the bearing every six months. The “Predictive”
maintenance bin is some task that is “predictive” in
nature. In other words, some technology (non-intrusive)
has been utilized to assess when maintenance should be
done. An example is fitting: I replace the oil in my
car every 3,000 miles since the manufacturer suggests it
(Preventive). I replace the oil in my car
when the temperature gauge is pegging and smoke is
bleeding from the valve covers (Reactive).
I install a reliable on-line oil sensor in the car’s oil
sump and it tells me (based on some magical correlation)
that the viscosity, contamination and wear particle
counts suggests, that based on my driving habits over
the past several months, that I should change the oil
next week (Predictive). The Pro-active
bin represents root cause failure analysis. Determining
the root cause of why things are failing and eliminating
that condition. I will not say anymore about Pro-active
maintenance (topic for another paper).
These
plant managers were asked to assess on a percentage
basis the number of work orders their site generates
today (solid column with vertical stripes). They were
then asked to assess where they would need to be 5 years
from that point to stay competitive (solid column with
horizontal stripes). Obviously, this is a subjective
guess on their part; I am assuming that guess is based
in part on experience, knowing coming trends and
networking/benchmarking with their peers as to where
their facilities needed to be. Finally, the surveyors
took the surveyed companies and based on operations and
maintenance costs, plant/equipment availability, MTBF
(mean time between failures) and unplanned downtime,
ranked them. They extracted the top 10% and called them
“benchmark” facilities. These top 10% of facilities and
their breakdown of work order generation is represented
by the solid bar. The results are somewhat
predictable. Do less reactive maintenance (do not want
to wait until smoke is coming from underneath the
hood). Do more preventive maintenance (I believe this
comes from years of pounding by maintenance
consultants). Preventive maintenance has its roots in
Airline and military industry. Breakdown is typically
not an option here. Also, for the average facility,
preventive maintenance costs a lot of money and is
sometimes over done. Finally, the survey reveals that
these plant managers agree that they should be doing a
lot more predictive maintenance. The good news for us
is that we probably do not need to do much evangelizing
to senior management. The word is certainly out.
Predictive maintenance works. There are a number of
other reasons plant managers would like to move towards
non-intrusive ways of monitoring their equipment to
ensure better reliability. They know that their senior
level executives are not going to allow them to invest
capital dollars into installing redundant systems (would
not want to do this anyway). They know they need to
find non-intrusive ways to monitor the equipment. They
have limited talented craft resources that are over
extended as it is. Their maintenance staff some times
introduces more errors by open-inspecting and fixing
things (did I say that out loud). They are pressured to
reduce outage lengths and therefore must stop the
blanket approach of performing preventive maintenance on
all equipment during an outage. They must find ways to
perform maintenance on a “condition” basis. Predictive
Maintenance offers that solution. Therefore, it’s not a
surprise that these plant mangers responded this way.
How
about another chart? We have some other data based on
feedback from customers regarding their return on
investment in predictive technologies. Again, it’s no
surprise that the heavy industries with extremely
critical equipment operating 24-7 showed the best
returns.
Data collected from PDM
users 2001-2004
Now for
one of my favorites. Imagine, if you will, a
maintenance strategy that returns eight to twelve
dollars for every dollar invested. There are few if any
other advanced maintenance tools that provide a similar
return on investment. These savings are realized in a
number of different areas of the facility. They are:
improved reliability, availability and capacity that
leads to greater revenues. The “predictability”
afforded the engineering, operations and maintenance
teams allows for better planning of outages. Outage
planning is enhanced by the simple fact that you can
better predict MTBF (mean time between failure) rates.
Replacement of Time based schedule preventive
maintenance tasks with non-intrusive predictive
maintenance tasks allows for a reduction in the PM
program without risk of higher failure rates.
Additionally, with so few qualified journeymen rising up
through the ranks, the qualified maintenance resources
can now be focused on those critical tasks that must be
done right. The less harried environment leads to less
rework issues, a reduction in the back log and a much
better environment to work in. Other benefits are
captured in the chart below.
With so
many advanced maintenance strategies to choose from the
question becomes, “Are there easier battles to
conquer?” Perhaps so. Predictive Maintenance in and of
itself is not an easy implementation. The barriers to
success are numerous, as we will discuss later. The
fact remains, however, that the biggest bang for the
buck amongst potential maintenance strategies comes from
predictive maintenance implementations. So, with the
type of returns and improvements documented above, it is
no wonder that for those who have been successful the
paybacks have been nothing short of remarkable. A
survey performed in the 2002 timeframe queried managers
who had managed the implementation of multiple advanced
maintenance strategies over the past ten year period.
They were asked to objectively rank the impact of
implementation. The results (See below) show that a
properly implemented PDM program achieved better results
then such heavy weights as RCM (Reliability Centered
Maintenance), and CMMS (Computerized Maintenance
Management Systems). Interestingly, craft training
(good old wrench turning skills) ranked nearly as high
as predictive maintenance. The chart below captures
the results of a survey of users during the 2002-2003
timeframe.
Okay,
so there exists a pretty good story why this strategy is
worth pursuing for personal and company reasons. There
is a multitude of objective data collected by third
parties that validates it. We know it is worth doing
and that not many people do it well. The remainder of
this paper addresses those issues of why people do NOT
do it well and offers recommendations for improvement.
This will not be highly subjective content. I promise
you.
Assessing the Problem (As-Is Condition)
During
the nineties, I had many opportunities, while working
for a major vendor, to tour facilities around the world
and assess their predictive maintenance programs. The
assessments were part of a process whereby the
individual facilities we were assessing were competing
for a coveted Predictive Maintenance Program of the Year
Award this vendor awarded. To be honest, it was
strategic marketing at its best. During the years I
was involved with all of these assessments, and visited
and reviewed several hundred predictive maintenance
programs around the world. The majority of these
companies were considered benchmark in their respective
vertical markets. In all cases they had shown a unique
capability to implement successful predictive
maintenance programs. Some of the programs had been in
existence for 16+ years. While evaluating them, the
participants shared their failures, and the pitfalls and
issues they had faced over the years in excruciating
detail. I began to see common threads that ran through
all of these successful programs. I saw the same
challenges conveyed to me time and time again. I
realized that their success was not based on luck or
some magical formula but sound successful business
practices that on the surface seemed simple and
obvious. Remarkably, the problems fell into four
predictable categories: People, Processes,
Technology/Vendor and Reporting. I decided to perform
the standard fishbone analysis on the problem,
remembering the issues, challenges and pitfalls my
interviewees had suffered through. The following
diagram states the most commonly heard problem in a
succinct one liner. I will elaborate further on some of
the key issues and discuss potential solutions.
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