MIKE BUKOWSKI is a sales principal with Dataiku
(www.dataiku.com), the maker of the all-in-one data
science software platform Dataiku Data Science
Studio (DSS). It is an advanced analytics software
solution that enables companies to build and deliver
their own data products more efficiently. Bukowski
has more than 25 years of experience working
with customers leveraging predictive maintenance,
advanced analytics and data science.
IntoRoad Time
November/December 2017 | VehicleServicePros.com 43
APIs (application programming interfaces), like
the weather; static data, like manufacturer
service recommendations; manual data from
human inspection; geographical or terrain data;
equipment usage history data; parts composition;
and more.
Once all of the data are combined, predictive
algorithms and machine learning can be applied
to produce a model that successfully predicts failures
or optimizes for any number of factors, and
the models are tested to ensure an acceptable
degree of accuracy that minimizes false negatives
and false positives.
With predictive maintenance, it’s important to
consider the costs of these failures and whether
it’s more optimal to over- or under-estimate the
so-called remaining useful life that could lead to
either catastrophic failure or unnecessary maintenance,
respectively.
Most importantly, once developed, all of these
things – combining data sources and applying
machine learning for predictions – can happen
in real time as assets are in use.
Comprehensive predictive
maintenance strategy
Predictive maintenance isn’t simply about producing
predictive outputs in raw format. Aft er all,
transportation companies have large staff s dedicated
to maintenance, not all of which are data
scientists or analysts able to interpret outputs of
a machine learning model.
Instead, to get the most out of predictive maintenance,
organizations must really rethink and
optimize their entire strategy from top to bottom,
integrating the predictive component seamlessly
into all parts of the business. Collaboration is key
here and it is vital to include business small-to-medium
enterprises (operational and maintenance),
business analysts, engineers, data scientists and
application developers in the mix.
At a bare minimum, any outputs from a predictive
maintenance program must be human-readable.
Generally, this is most eff ective in the form of
dashboards and visualizations, which easily close
the feedback loop and allow maintenance managers,
operators or staff to react to any impending
failures or optimization techniques.
Th e output must be relevant, timely and
actionable.
More realistically, for maximum return, predictive
maintenance must be an entire system
around which the company’s eff orts revolve, not
just a simple dashboard (although this is certainly
a good fi rst step).
A comprehensive predictive maintenance
system means:
1. Conducting initial analysis to determine if a
predictive maintenance strategy makes sense
for all assets or only a portion of them based on
cost analysis, and from there, optimal combination
of predictive versus preventive maintenance
per asset or asset type.
2. Extending predictive maintenance upstream.
In other words, not just determining when a
part will fail or when to fi x something before it
breaks, but also looking at optimizing for convenient
service locations and anticipating need for
parts in advance. Look to avoid follow-up and
ensure that the most appropriate and cost-eff ective
service team can be deployed the fi rst time.
3. Setting larger-picture goals than simply “detect
failure.” Predictive maintenance isn’t the end
goal, per se. Companies with truly eff ective
predictive maintenance programs operate
them at much a wider scale than just anticipating
failures. Set big-picture business goals,
like reducing fuel costs, increasing uptime in
general, reducing downtime due to waiting
on parts, increasing revenue yield, decreasing
shipment delays, etc., and then use predictive
maintenance to get there.
4. Aft er detecting failure, have a plan. Again, going
beyond the actual failure detection, it’s critical
to then decide how to best execute necessary
repairs through second-order or secondary
analytics. Secondary analytics means having
a process in place for an entire deeper layer of
analysis to determine the best time to actually
remove the asset from service and which additional
repairs - if any - should be conducted
simultaneously to minimize the cost of having
to remove the asset again for a diff erent failure
within a short window.
5. Leveraging automation and moving more
toward self maintenance by optimizing for,
and automating, the immediate next steps once
predictive systems point to imminent failure,
whether this automatically triggers a work
order, notifi es a technician or certain team,
places an order for a replacement part, etc.
Get started
Not sure where to begin? A good fi rst step is to
assemble a cross-functional team – maintenance
management, data science, business analysts, IT,
etc. – to prototype and lead the project. Th is team
can start setting big picture business goals and
conduct a cost/benefi t analysis to determine the
savings predictive maintenance might provide.
If your business already has a predictive maintenance
program in place but wants to go further,
start moving toward a second-order or secondary
analytics program to determine the most cost-effective
maintenance plan when taking assets out
of service for predicted failure. Now is also the
time to start identifying opportunities for further
AI (artifi cial Intelligence) automation with the goal
of getting closer to self-maintenance.
Whether just getting started or looking to
advance a predictive maintenance program,
remember that agility and fl exibility in implementation
are essential. New technologies will
continue to emerge and evolve, and being able to
adapt along with them will ensure your predictive
maintenance program remains cutting edge.
Guidebooks and whitepapers on predictive
maintenance, data science and predictive analytics
can be found online at www.dataiku.com/
resources/whitepapers.
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