Predictive MaintenanceTaking pro-active measures based on
advanced data analytics to predict andavoid machine failurePredictive Maintenance | Position Paper - Deloitte Analytics Institute02

Predictive Maintenance | Position Paper - Deloitte Analytics Institute"In the environment of Industry 4.0, maintenanceshould do much more than merely preventing
downtimes of individual assets. Predicting failures
via advanced analytics can increase equipment
uptime by up to 20%."03

Predictive Maintenance | Position Paper - Deloitte Analytics Institute04

Predictive Maintenance | Position Paper - Deloitte Analytics Institute

Introduction
Knowing well ahead of time when an asset will fail avoids
unplanned downtimes and broken assets. On average,
predictive maintenance increases productivity by 25%,
reduces breakdowns by 70% and lowers maintenancecosts by 25%. It is based on advanced analytics andmarks a new way of organizing and implementing
maintenance on an industrial scale. Deloitte hasdeveloped an approach to smoothly introduce predictive
maintenance into business processes in a customized
and structured manner.
The Deloitte Analytics Institute is a center of expertise
operating at the intersection of business, science andtechnology. We develop and implement customized
state-of-the-art analytics and big data solutions.

05

Predictive Maintenance | Position Paper - Deloitte Analytics Institute

The Value ofMaintenance for
Production Processes
Industrial production has evolved enormously over thelast centuries. It has come all the way from steam andhydro power via assembly lines to digitalization.
Industry 4.0 – the proclaimed fourthindustrial revolution – is unfolding at
the moment. It is characterized byinterconnectedness and vast amounts ofavailable information.
Productivity has risen continuously due
to modern machines. They are highly
complex and often represent substantial
investments. In 2015, German automotive
OEMs alone have invested over 14 billion

Euros in tangible assets1. Despite all efforts
to prolong lifecycles, wear, erosion anddepletion will eventually lead to machine
failure.
The Value of Predictive MaintenanceIn the environment of Industry 4.0,
maintenance does much more than merely
preventing downtimes of individual assets.
Machines are increasingly interconnectedalong the production chain. One failing“German Industrial Businesses‘ Investments in Tangible Assets per Industry Sector in 2015“, Statistisches
Bundesamt, destatis.de, November 2016
1

2

“IoT Slashes Downtime with predictive maintenance”, Gary Wollenhaupt, ptc.com, March 201606

machine might halt the whole production
process. Today, poor maintenance strategies
can reduce the overall productive capacityof a plant by 5 to 20 percent2. Long andcontinuous runtimes of capital-intensive,highly-integrated assets can represent asignificant competitive advantage. So canefficient and well-orchestrated maintenance.
Deloitte has identified six core dimensions ofmaintenance and failure to be managed.

Predictive Maintenance | Position Paper - Deloitte Analytics InstituteSix Dimensions of Maintenance and Failure

REACHABILITY
There are more and less convenient
times for maintaining an asset.

CASCADED DAMAGE
The failure of a single part can cause
damage to a wider system.

QUALITY
Assets progressing towards failureendanger production quality levels.

Maintaining a train in the depot overnightis easier than maintaining one that has
broken down during business hours on aremote track.

An electrical discharge due to a
malfunctioning capacitor may render awhole circuit board broken beyond repair.

Gluing nozzles which are increasingly
clogged may not dispense sufficient glue
to ensure a long lasting connection.

EQUIPMENT LOGISTICS
Maintenance requires preparation suchas ordering of parts or deployment oftools.

INTERDEPENDENCE
Unplanned maintenance can lead to
substantial downtimes and jeopardize
productivity.

SAFETY
Breakdown of an asset may directly or
indirectly jeopardize the safety of theprocess.

Broken gears can only be identified after
opening the gearbox and need to beordered from the manufacturer while theasset remains idle.

Maintenance of a pipeline may render
subsequent production idle.

Breaking of a food processing machine
may introduce chards of debris into the
food.

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Predictive Maintenance | Position Paper - Deloitte Analytics Institute

Maintenance Strategies on an
Industrial Scale
Depending on assets, costs and technicalsophistication, a broad spectrum of
maintenance strategies can be applied.
These strategies range from merereaction to failures to highly evolved
systems optimizing maintenance efforts
for groups of assets. Figure 1 illustrates
four maintenance strategies by levelof sophistication. It highlights theircharacteristic traits, advantages anddisadvantages.

Reactive Maintenance entails acting whena failure has already occurred. It is theclassic form of maintenance, usually being
applied when the object is of low value,easy to replace and does not have a severe
impact on the business process. Examples
include replacing a broken light bulb orgear as well as fixing a ruptured tube.
Preventive Maintenance attempts toprevent failure by maintaining machinesat pre-scheduled time intervals. Thisapproach is usually taken when the cost of
maintenance is moderate and when it canbe done outside of production hours. A
typical example are the biannual checkups
of wind turbines.

“Predictive maintenance increases
equipment uptime by 10 to 20% while
reducing overall maintenance costs by
5 to 10% and maintenance planning timeby 20 to 50%”

08

Condition Based Maintenance is similar
to preventive maintenance but rather
considers the actual use of the object
instead of relying on pre-scheduledintervals. As for Preventive Maintenance,
this is usually done when maintenancecosts are not very high and maintenancecan be carried out at convenient times.
An example for this approach are thecheckups for airplanes that have travelled acertain distance or hours.
Sometimes, however, these approaches
are not enough.
Predictive Maintenance utilizes a wealthof process data and advanced analyticalmethods to predict failures well before
immediate action has to be taken. With theimplementation of concepts like Industry
4.0 or Smart Factory additional processdata becomes available. This allows for
estimating the remaining runtime of assets
with increasing accuracy. This maintenanceapproach is usually taken when highcosts are incurred due to downtimes ormaintenance. Also, it can ease schedulingwhen maintenance activities are complex.

Predictive Maintenance | Position Paper - Deloitte Analytics Institute

Maintenance strategies in an industrial context• no analytics or sensors needed• spare parts are fully used up

Reactive Maintenance
Assets fail before being maintained.

• high downtimes• failures damage the assets

Preventive Maintenance
Systems are maintained at fixedintervals to ensure continuous
availability.

• no in-process sensors needed• reduces unplanned downtimes

Condition Based Maintenance
Systems are maintained basedon simple rules using equipment
information.

• good usage of equipment life cycle• limits unplanned downtimes• waste in life cycles of spare parts• excessive maintenance and downtime• limited failure forecasting accuracy• medium risk of failures remains

Predictive Maintenance
Systems are maintained before failure,but run as long as possible without
interruption.

Figure 1. Maintenance strategies in an industrial context• optimal usage of equipment lifetime• minimizes unplanned downtimes• upfront investment necessary• expert knowledge required

production

warning		asset failure

downtime		

maintenanceavoided asset failure09

Predictive Maintenance | Position Paper - Deloitte Analytics Institute

A Closer Lookat Predictive
Maintenance
Each maintenance strategy comes with certain upsides
and challenges.
In the context of Industry 4.0 – increased
interconnectedness and new opportunities
to collect, process and analyze information– predictive maintenance can be a very
powerful strategy. In this section, we will
discuss its benefits and challenges before
outlining how it can be implemented into
production processes.
Benefits of Predictive Maintenance
The obvious benefit of predictive
maintenance is that it maximizes
runtime. Repairs can be carried out justbefore a breakdown. That represents
a major advantage – studies show that
unplanned downtime is costing industrial
manufacturers an estimated $50 billion
each year3. Also, necessary maintenanceefforts can be orchestrated to minimize
system-wide downtimes. Predictive
maintenance can further be used toease logistics by maintaining machines atconvenient times – for example outsideof production hours or while the needed
personnel is close by. Lastly, it can assist
purchase departments by predicting whichspare parts will be needed at which point
in time.

Predictive Maintenance.
An Investment That Pays Off
Predictive maintenance programs create the following benefits:

5-10%
10-20%
5-10%
20-50%

cost savings in operations and MRO
material spenincreased equipment uptime
and availability
reduced overall maintenance costs
reduced efforts on maintenanceplanning time

“How Manufacturers Achieve Top Quartile Performance”, Industry Week & Emerson, Partners.wsj.com,
accessed May 2017310

Predictive Maintenance | Position Paper - Deloitte Analytics InstituteThe benefits of predictive maintenanceare dependent on the industry or eventhe specific processes that it is applied
to. However, internal Deloitte analyses
have concluded that material cost savings
amount to 5 to 10% on average. Equipment
uptime increases by 10 to 20%. Overall
maintenance costs are reduced by 5 to 10%
and maintenance planning time is evenreduced by 20 to 50%4.
The profit of predictive maintenanceincreases with the underlying maintenancecosts. The higher the expenses caused by
failure the bigger the benefits.
Challenges of Predictive Maintenance
Data is the fuel of any predictive
maintenance engine. Its quality andquantity is the limiting factor for analyzing
root causes and predicting failures
well ahead of time. Therefore, a major
challenge inherent to any predictive
maintenance program is increasingdata quality and coverage. The more
information is available on events to bepredicted the better predictions become.

We distinguish between required data
– without which predictive maintenancecannot be applied – and additional data,
which improves the quality of predictions.
Predictive maintenance is an investment,
which reveals implicitly the second key
challenge: Establishing the needed
processes initially creates costs.
Businesses need to add sensors to theirmachines and set up a wide array of IT
infrastructure, processes and trained
personnel.
Data from various sources must be
integrated and transformed so that it
can be made available on a suitable
platform. Dashboards, email or SMS
warning systems must be put in place tocoordinate the necessary maintenanceefforts. Process experts’ and data
scientists’ knowledge is needed to build
and maintain a functioning predictivemodel. Also, personnel needs to be
trained to handle the information inflow
and interpret alerts correctly.

"The more information is available
on events to be predicted the better
predictions become."

4

Required data

Additional data

Process failures with exact date andtime are the very basic requirement.

Indirect process parameters like raw
materials, supplier details, assigned
employees can help the analysis.

Process/Component variables like
temperature, pressures, voltages
and other physical measurements
are needed to identify root causes offailures.

Additional sensors like cameras or laser
sensors can be integrated and connected
to track important process parameterssuch as shape or speed of rotation.

Deloitte internal research11Predictive Maintenance | Position Paper - Deloitte Analytics InstituteHow to Implement Predictive
Maintenance
After evaluating the different maintenancestrategies and having decided to implement
predictive maintenance, businesses
begin a journey with new learnings andinsights waiting along the way. Initially,
there is no certainty as to which level offailure prediction can be reached. In our
experience, an incremental approach as
illustrated in Figure 2 has proven to bevaluable for mitigating project risk. Eachstep yields better results leading to reduced
downtimes and increased productivity.
Before implementing a predictive
maintenance program, we aggregate expert
knowledge from our far-reaching networkof global industry experts. Workshops withthe client help lay a foundation for trusting
collaboration – a crucial prerequisite foroverall success.
Once this non-technical framework is set
up, the journey is initiated at the individual
maturity level. When starting at stage 0,
creating an understanding of the process
through data is the primary objective.
For step 1, we need a constant stream ofsensor data into an integrated platform.
With the help of powerful visualization,
experts can identify which parametersindicate imminent failure.

12

For step 2, expert insights about theprocess and its parameters are vital.
With their help we can deduct simple
rules. Applying these may prevent a large
proportion of failures from occurring
already.
Anomaly detection requires sufficientaccumulated sensor data with a minimum
frequency of measurements per time
unit and sensor. This data allows us todefine the norm for any given process.
Aberrations from the norm can then trigger
alerts to the operators – who will have todecide if an actual failure has occurred.
To be reactive and preventive, we need asignificant amount of failures with detailed
log data. Applying advanced analytics,
failures can then be detected immediately
and reliably.
Conducting root cause analysis requires
recordings about successful as well as
unsuccessful approaches are needed
to resolve failures. Causes for occurring
failures can be narrowed down andappropriate actions proposed.

“Applying advanced analytics, failures canbe detected immediately and reliably.”Predictive Maintenance | Position Paper - Deloitte Analytics Institute

5

le x
Comp

0

Reactive &Preventive

Ad-hoc, reactive
resolution of failures
and/or preventive
maintenance. Raw
system data isinspected on-demand.

1

Visualization

Continuous andcomprehensive
visualization of
the key processparameters.

Root Cause
Analysis

tial
oten
P
&
it y

2

4

3

Anomaly
Detection

Rule Based

Immediate warningsof typical exceptions
based on rules developedwith experts to
identify out-of-bounds
machinery sensor values
automatically.

Immediate
identification of
anomalies andunknown events in themachinery leveraging
historical and realtime data.

Reactive &Preventive

Automated support
to identify andmitigate root causesof failures and toenhance process
understanding.

Early warnings withhigh accuracy basedon predictive models
leveraging historicaland real-time data.

Condition based
maintenance (simple
rules, not very precise)

Figure 2. Journey towards higher levels of predictive maintenance13

Predictive Maintenance | Position Paper - Deloitte Analytics InstituteDeloitte Analytics Approach
Each of the five steps described above
follows our proven analytics approach.
Firstly, we define success criteria in close
cooperation with our client. Subsequently,
we have a look at the available data andaim to understand, model and validate it.
To do so, we apply a well-tried approachfor datamining – the CRISP-DM process.
This process results in an offline prototype
for testing purposes and illustrating
functionality and business value. As
the next step, the resulting models are
deployed on productive systems.

Enabling business users, intuitive
dashboards and user interfaces aswell as alert systems are designed formaintenance crews. As a crucial element
project finalization includes a long-term
strategy for model upgrades.

“A long-term strategy for adequate model
updates on failure prediction is crucial.”

Case Studies
We helped several clients implement predictive maintenance programs. In each of the four cases outlined below, the depth
and sophistication of implemented predictive maintenance measures was tailored to the available data quality.
01. For a large construction company we have evaluatedthe possibilities of predictive maintenance for facility
management. Through explorative analysis andcomprehensive visualization of repair protocols, we havemade maintenance efforts transparent and have laid out
paths towards higher levels of predictive maintenance.
02. For a major telecommunications provider we havedeveloped a prediction model for customer trouble
tickets related to DSL connection problems. The model
was based on network performance counters and allows
prediction with high precision. It enables pro-active
repair and thus improves network quality and customer
satisfaction.

14

03. We have provided an automotive manufacturer witha harmonized maintenance solution for its welding
robots producing vehicle bodies. The solution comprises
visualization and several predictive models to detect
failure onset in process data.
04. For another automotive OEM we have installed astate-of-the-art Big Data platform allowing for failure
prediction in real time. Ingesting process data fromvarious sources and running it through an anomaly
detection engine enables continuous monitoring of themachines in use.

Predictive Maintenance | Position Paper - Deloitte Analytics Institute

Can Predictive
Maintenance HelpYour Company?

Predictive maintenance can help you
manage maintenance more efficiently.
However, keep in mind that not allenterprises require the same level ofreliability from their assets. A good place tostart the assessment for your enterprise is
to look at mission critical requirements andmaintenance program maturity.
Ask yourself the following questions:•• How reliable do our assets need to be ?
•• What are our availability targets?
•• What is our machines’ current failurerate?
•• How high are our current maintenancecosts?
•• Do we have the right spare parts in theright place at the right time?
•• How do we determine when it is time toreplace an asset rather than to maintainit?
•• What data do we already have that is not
being used effectively?
•• Have we identified the critical assets in
our production system?
•• Are there some critical assets that would
benefit from a predictive maintenanceprogram?
•• Do we have the needed technologicalexpertise in house to develop a
predictive maintenance program?
•• Do I have advanced analytics experts inhouse?

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Predictive Maintenance | Position Paper - Deloitte Analytics Institute16

Predictive Maintenance | Position Paper - Deloitte Analytics Institute

A Glimpse into the Engine Room
Predictive models are one of two key components that any predictive maintenance program relies on. Their algorithms are
the “machines” turning raw process data into targeted failure prediction insights. Predictive maintenance typically makes useof two well-established techniques from machine learning.
Classification algorithms learn to distinguish between
normal and erroneous operations. These learnings must
be based on a wealth of historic data providing sufficient
examples for the failures to be detected. These methods
are very powerful. They can differentiate even subtle
variations from normal operations but require at least 20 to
30 recorded failure occurrences of each type. The algorithms
are mostly insensitive to unseen failure conditions. Classicalexamples in this area are Random Forrest, Nearest
Neighbours or Support Vector Machines (SVM). The
methodic spectrum also includes more advanced methods
such as hidden markov chain models.

Anomaly detection describes a class of methods thatspecialize in finding outliers against the backdrop of
normal operations. While these methods require a sounddata basis representing “normal operations” they detect
previously unseen error patterns as a mere aberration fromnormal behavior. Typical methods in this area range from
simple one class SVM to nonlinear neural networks such as
autoencoders.

The other key component for successful predictive maintenance is an architecture that enables fast and scalable
detection algorithms.
Parallelization is crucial to predict different assets
simultaneously. This is important to ensure timely
predictions of an array of assets but is especially crucial
during the computationally intensive model retraining. In
this phase, the predictive models are updated with the latest
Data. Therefore, algorithms used for predictive maintenanceare parallelized and make heavy use of Distributed
Computing and GPUs wherever possible.

Streaming architectures for Big Data ensure real-timeprocessing of the incoming data. Architectures like ApacheKafka, Apache Spark or Flink ensure extremely fast
data processing in the below seconds regime to ensure
maximally long reaction times before an asset failure occurs.
Additionally, these frameworks provide a redundant andhighly available configuration.

17

Predictive Maintenance | Position Paper - Deloitte Analytics Institute"Predictive maintenance could help you
manage maintenance more efficiently.
However, keep in mind that not allenterprises require the same level ofreliability from their assets."18

Predictive Maintenance | Position Paper - Deloitte Analytics Institute

Meet the teamOlaf Peter Schleichert
Partner, Analytics Leader Germany
Head of Analytics Instituteoschleichert@deloitte.deMore information
http://www.analytics-institute.de

Dr. Björn Bringmann
Director
Deloitte Analytics InstituteDr. Hardy Kremer
Manager
Deloitte Analytics Institutebbringmann@deloitte.dehkremer@deloitte.de

Dr. Sergey Zablotskiy
Senior Consultant
Deloitte Analytics Institute

Dr. David Köpfer
Senior Consultant
Deloitte Analytics Instituteszablotskiy@deloitte.dedkoepfer@deloitte.deAcknowledgment
We especially thank Fabian Timm for his contribution to this paper.

19

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Issue 7/2017