Manufacturing industries strive for maximizing asset availability but face lots of challenges while implementing. Ineffective maintenance strategies can cause reductions, ranging from 5% to 20%, in the overall productive capacity of plants. The same study also suggested that unplanned downtime causes companies to incur around $50 billion annually. Companies worldwide are trying to figure out the optimum frequency of servicing their equipment.

1. Predictive Maintenance
A Smart Solution to Maintain your Equipment!

2. T
A
B
L
E
O
F
C
O
N
T
E
N
T
S Executive Summary
01. 3
Introduction
02. 4
Journey from Reactive
Maintenance to Risk-Based
Maintenance
03. 6
Predictive Maintenance Cycle
04. 8
Drivers of Predictive
Maintenance
05. 9
Potential Use Cases
06. 11
Challenges in Adopting
Predictive Maintenance
07. 16
Characteristics of the
Thoucentric Predictive
Maintenance Solution
08. 17
Potential Benefits of the
Thoucentric Predictive
Maintenance Solution
09. 18
Glimpses of a Sample
Dashboard
10. 19
Conclusion
11. 22
About Thoucentric
12. 23

3. Manufacturing industries strive for maximizing asset
availability but face lots of challenges while
implementing. Ineffective maintenance strategies
can cause reductions, ranging from 5% to 20%, in the
overall productive capacity of plants. The same study
also suggested that unplanned downtime causes
companies to incur around $50 billion annually.
Companies worldwide are trying to figure out the
optimum frequency of servicing their equipment.
Note -
“Predictive maintenance and the smart factory” by Deloitte points towards the fact, that inadequate maintenance
strategies can reduce the overall productive capacity of plants by 5 to 20%
EXECUTIVE SUMMARY
03
Predictive Maintenance | White Paper
Traditional strategies like opting for breakdown maintenance, in the hope of maximizing the useful life of an
asset, at the cost of machine downtime, or, opting for preventive maintenance, by replacing potentially
good parts early have proved to be ineffective. Unplanned downtime is very expensive and involves huge
opportunity costs whereas preventive maintenance leads to increased replacement costs over time, along
with more number of times of maintenance and disruption to operations. This also has chances of
inefficient spare parts management where excess spare parts inventory blocks working capital and
increases chances of obsolescence, which ultimately negatively impacts the bottom line of companies.
Predictive maintenance, leveraging
predictive analytics can offer
companies the potential to strike a
balance between opportunity costs
due to downtime and excessive
repair costs owing to early repair.
The predictive maintenance solution
designed by Thoucentric, can help
organizations in manufacturing
industries, improve the reliability and
performance of their assets. This
paper presents a brief about how
companies can leverage this tool in
their operations and maintenance
processes.

4. INTRODUCTION
04
Predictive Maintenance | White Paper
In recent years, organizations worldwide are facing
increased competition and dynamically changing
consumer demands because of which it is of
crucial importance for them to maximize the
efficiency and reliability of their equipment. The
amount of “big data” available today has a huge
opportunity to help them in deriving actionable
insights by engaging in data-driven decision
making.
Traditional strategies like opting for breakdown
maintenance, in the hope of maximizing the useful
life of an asset, at the cost of machine downtime,
or, opting for preventive maintenance, by replacing
potentially good parts early have proved to be
ineffective. Unplanned downtime is very expensive
and involves huge opportunity costs whereas
preventive maintenance leads to increased
replacement costs over time, along with more
number of times of maintenance and disruption to
operations. This also has chances of inefficient
spare parts management where excess spare
parts inventory blocks working capital and
increases chances of obsolescence, which
ultimately negatively impacts the bottom line of
companies.
Predictive maintenance, leveraging predictive
analytics can offer companies the potential to
strike a balance between opportunity costs
due to downtime and excessive repair costs
owing to early repair.
The predictive maintenance solution designed
by Thoucentric, can help organizations in
manufacturing industries, improve the
reliability and performance of their assets.
This paper presents a brief about how
companies can leverage this tool in their
operations and maintenance processes.

5. 05
Predictive Maintenance | White Paper
Predictive maintenance has an advantage over other types of maintenance strategies in that it is based on
dynamic data-defined decision rules and employs advanced predictive analytics techniques such as
machine learning and deep learning which helps it to predict potential equipment failures which may
occur in the future by leveraging advanced statistical techniques. Organizations utilizing predictive
maintenance can realize great savings in terms of opportunity costs and can also maximize their profit
margins.
The below table highlights the pros and cons of each of the four types of maintenance strategies.
Maintenance Advantages Disadvantages
Reactive Simple to implement
Fewer manpower is required as a
small team of people managing the
equipment can implement reactive
measures when the equipment
breaks down
Failure is highly unpredictable
Very costly
Poses a safety risk to other assets
Preventive Keeps assets up and running for
longer times than other strategies
Decrease in unplanned downtime
Improved safety
Increased maintenance costs due
to early replacements of parts
Failure of assets cannot be
prevented if a part experiences
a major problem before the next
inspection
Condition-based Carried out while the equipment is
working, which reduces the probability
of disruption of normal operations
By scheduling tasks, it is possible to
reduce overtime costs of technicians
who would otherwise need to repair
equipment during sudden
breakdown
Reduces the possibility of system
collateral damage.
High costs to train staff as
knowledgeable professionals are
needed to analyse data and perform
maintenance
Unplanned condition based
maintenance events may happen
when many assets require care at
the same time
Predictive Reduces the time of maintenance
of the equipment and reduces loss
of man-hours
Reduces the cost of spare parts
and supplies
Predicts potential equipment failures
in advance by utilizing advanced
predictive analytics
Higher upfront cost than other types
of maintenance strategies

6. 06
Predictive Maintenance | White Paper
JOURNEY FROM REACTIVE MAINTENANCE TO
RISK-BASED MAINTENANCE
01 - Reactive Maintenance allows an
equipment to run until failure. It is
suitable for assets that entail very low
replacement costs and do not require
investment in advanced technology.
02 - Preventive Maintenance
prescribes maintenance to be done
as per fixed time schedules without
considering the current condition of
the asset
03 - Condition-Based Maintenance examines the present working condition of the equipment and
prescribes maintenance on a rule-based logic which does not dynamically change based on loading,
ambient or operating conditions
04 - Predictive and Prescriptive maintenance is a proactive maintenance approach which relies on
continuous monitoring of equipment performance through sensor data, by utilizing advanced predictive
analytics techniques, for providing warning related to potential equipment failures
05 - Risk-Based Maintenance specifies a way to define, measure and constantly improve the asset
performance through optimized use of reactive, preventive, condition-based, predictive and prescriptive
maintenance strategies
Organizations currently implementing predictive maintenance can plan to implement prescriptive
maintenance as a next step since it would give them an opportunity to utilize an analytics platform which
is capable of utilizing advanced machine learning techniques to adjust operational conditions for
achieving the desired outcome as well as intelligently plan and schedule maintenance processes.
As a part of their long-term goal such organizations can also decide to implement risk based maintenance
and thus, reap the benefits of all types of maintenance in an optimized fashion.
Planned based on time
or is age statistics
Diagnostic to predict
impending failure
Run to failure
Reactive Maintenance
Preventive Maintenance
Rules-based logic
using sensor data
Condition-Based Maintenance
Predictive and
Prescriptive Maintenance
Connect the asset
strategy to the
corporate strategy
Risk-Based
Maintenance

7. 07
Predictive Maintenance | White Paper
From the below diagram, it is evident that simple reactive maintenance leads to the lowest original
equipment effectiveness (less than 50%) and thus, the lowest equipment uptime. On the other hand,
predictive maintenance helps organizations to benefit from greater of 90% OEE and the maximum
equipment uptime, as well, helping them to minimize opportunity costs and maximize their profit margins
with investments in the right set of technologies.
This paper focuses exclusively on predictive maintenance and how the predictive maintenance solution designed
by Thoucentric can help organizations globally to overcome potential challenges and maximize equipment
uptime.
Let us now explore the technology in much more detail and understand the potential benefits that diverse
industries in can derive from it.
<50% OEE
Reactive
Fix when broken
50%-75% OEE
Schedule maintenance
activity
Planned
75%-90% OEE
Defect elimination to
improve performance
Proactive
>90% OEE
Advanced predictive analytics
and Sensing data to predict
Machine reliability
Predictive
- Kerry Baskins
The great differentiator in business is when an organization steps out and creates value
from something never tried before.
"
"

8. 08
Predictive Maintenance | White Paper
PREDICTIVE MAINTENANCE CYCLE
01 - Collection of Data
Signal specific sensors collect equipment data. Improving
sophisticated sensors improves the quality of collected data
02 - Data Processing and
The sensors collect raw data on a real-time basis, which is
converted to information for enabling application of
analytics
03 - Environmental condition
Analysis of collected data and environmental conditions by
the software in order to understand the condition of the
equipment better
04 - Forecasting next
Prediction of potential failure events by advanced predictive
analytics

9. 09
Predictive Maintenance | White Paper
DRIVERS OF PREDICTIVE MAINTENANCE
Managing a large variety of data - structured and unstructured data sources can be handled by
flexible and scalable platforms, which can easily manage many data formats, structures, and
schemas
i. Internet of Things
Real-time analytics on data - data processing and conversion of large volumes of incoming data into
information to enable application of predictive analytics at a high speed
Machine Learning and Modelling capabilities - Building predictive Machine Learning models and
continuously iterating on them
Diverse analytical capabilities - Advanced predictive analytics techniques such as machine learning
and deep learning techniques are required for high precision results. Also, integration of the analytics
platform with leading business intelligence and visualization tools is an added requirement
Data security - implementation of sensitive data protection, total encryption, and data governance

10. 10
Predictive Maintenance | White Paper
ii. Condition-based maintenance
Cost
Risk
Risk
Total Cost
Repair Cost
There is trade-off between repairing an equipment at the right
time vs repairing an equipment late. Before breaking down, the
condition of an asset continuously degrades over time. It is
really very important to have an opportunity window at early
stages to identify signals of potential equipment breakdown and
plan appropriate courses of actions in order to ensure that it is not
too late to repair the equipment. However, it should also be
ensured that the equipment is not subjected to repair too early,
otherwise the repair cost would again be much higher than what
is reasonable.
Therefore, it is extremely important to set the right maintenance
schedule for equipment by leveraging appropriate technology.
Let’s say we have an engine maintenance task
for an aircraft. The fixed maintenance is after
competition of 150 flights. For plane A, the fixed
maintenance schedule after 150 flights, is
optimal for it, else, it would fail in a short span of
time. For plane B, the optimal maintenance
schedule is not even near to 150 cycles.
So, if maintenance is done then we are not
taking full advantage of our equipment. So, it’s
very important to go for dynamic maintenance
strategy, rather than static maintenance
strategy
Fixed Static
Maintenance Policy
Optimal timing
for Plan B
150 Flights Time over Flights
Degradation
after Shorter
Lifespan
PLANE A
Degradation
after Longer
Lifespan
PLANE B Opportunity
With the IoT, we are headed to a world where things aren’t liable to break
catastrophically - or at least we’ll have a hell of a heads up. We are headed to a world
where our doors unlock when they sense us nearby - Scott Weiss

11. 11
Predictive Maintenance | White Paper
POTENTIAL USE CASES
01 Predictive maintenance for connected vehicles in
the automotive industry
Manufacturers of commercial vehicles
such as trucks, buses and defence
vehicles tend to schedule vehicle
maintenance based on the distance
travelled or on the time elapsed since the
last maintenance cycle. As a result, these
organizations face increased opportunity
costs per vehicle due to sudden
breakdowns or high replacement costs
incurred due to employment of
preventive maintenance techniques. With
lakhs of vehicles and lakhs of customers,
the impact can be significant in total.
An extensive data-driven approach is
required to minimize downtime, or the
time spent due to unplanned
maintenance. For this purpose, modern
data warehouses capable of supporting
high-volume and diverse data sources
need to be in place.
A variety of data from connected vehicles such as data pertaining to engine performance, acceleration, speed,
coolant temperature and brake wear, when correlated with other third-party data sources such as those
pertaining to meteorology, geolocation, traffic, historical warranty and inventory can generate useful insights by
the use of machine learning and advanced predictive analytics techniques.
As a result, engine problems can be detected early, coupled with, accurate prediction of maintenance
requirements. Organizations can also monitor vehicle health and performance from multiple devices, prioritize
repairs and promptly identify service locations having the requisite spare parts in stock, available technicians,
and the available service days.
Description

12. 12
Predictive Maintenance | White Paper
Leading manufacturers of industrial
turbines, generators, machinery for
hydroelectric power plants incur
significant costs due to lost
productivity, even if, the downtime is
only a few minutes. Manual
inspections and lack of regular
monitoring of the health of assets
leads to costly downtimes.
A predictive maintenance solution
based on acoustic monitoring can
capture, analyse, and interpret
sounds in hydropower plants for
diagnosing the health of assets and
can also provide early warnings of
equipment failures based on these
acoustic signals.
A platform capable of leveraging machine
learning and advanced predictive
analytics techniques can use acoustic
sensors to capture noises from equipment
such as turbines and generators. Machine
learning algorithms applied on huge
volumes of data obtained through
connected power units, can detect
anomalies and eventually predict, when
these issues might disrupt plant
operations. Alerts would be visible on the
platform dashboard and operators
located remotely can view them and plan
appropriate actions related to
maintenance.
Description
02 Predictive maintenance in the hydropower
industry

13. 13
Predictive Maintenance | White Paper
Suppliers of robotics equipment and factory
automation systems face huge losses in terms of
revenues due to sudden downtimes. Thus,
operational uptime is of critical importance for such
organizations.
Ensuring that assets run in the plant in good health
and monitoring the performance of equipment in
real-time so that timely detection and prediction of
issues can take place before they impact plant
operations is the key to maximize operational
uptime.
Platforms which gather, store, process and analyse
data from thousands of connected equipment in
real-time by leveraging machine learning and
advanced predictive analytics capabilities can
monitor the health of critical equipment and detect
potential issues before they cause operations to fail.
Whenever a potential failure is detected, the
dashboard of the platform can flash an alert, by
viewing which, maintenance personnel can plan
appropriate courses of actions to solve the problem.
Thus, reduced downtime obtained through
predictive maintenance can help to optimize the
manufacturing environment and extend the useful
life of equipment compared to reactive or
preventive maintenance.
Platforms which gather, store, process and analyse data from thousands of connected equipment in real-time by
leveraging machine learning and advanced predictive analytics capabilities can monitor the health of critical
equipment and detect potential issues before they cause operations to fail. Whenever a potential failure is
detected, the dashboard of the platform can flash an alert, by viewing which, maintenance personnel can plan
appropriate courses of actions to solve the problem.
Thus, reduced downtime obtained through predictive maintenance can help to optimize the manufacturing
environment, extend the useful life of equipment compared to reactive or preventive maintenance.
Description
03 Predictive maintenance in industrial robotics

14. 14
Predictive Maintenance | White Paper
Organizations which provide cargo
and load-handling solutions such as
loader cranes need to have high
level of efficiencies because of which,
they would need to collect, process,
and analyse data from a lot of
connected equipment by using
sophisticated sensors and apply
predictive analytics techniques to
derive actionable insights. Sensor
data would also need to be
combined with external and internal
data sources for the purpose of
analysis.
Platforms equipped with machine
learning and deep learning
capabilities can collect, analyse, and
correlate incoming data from
multiple sensors located in numerous
cargo handling equipment and data
from external data sources. This
enables monitoring of equipment
health remotely as well as in
performing predictive maintenance.
This can reduce unplanned equipment
downtime significantly. In this way, numerous
equipment in diverse geographical locations
would no longer function in silos and
predictive maintenance coupled with
enhanced operational efficiency can be
achieved across millions of such
interconnected assets.
Description
04 Predictive maintenance in cargo handling and
shipping
Machines can let you know when
they are not feeling well! But the
challenge lies in applying the
technology across the full scope of
operations!

15. 15
Predictive Maintenance | White Paper
For manufacturing companies,
ensuring that the appropriate spare
parts are available at the desired
places and at the correct time is a
tremendous challenge for utilizing
predictive maintenance. As many
companies still rely on reactive
maintenance, it becomes very
difficult to forecast the demand for
spare parts, which are in fact, are
very costly and often involve long
lead times from the suppliers’ ends,
adding to the inconvenience.
Thus, companies must aim for
maintaining the right amount of
spare parts in the right places and at
the right times, for minimizing the
cost of inventory in spare parts. This
is where predictive maintenance
solutions play a key role in helping
companies to generate spare parts
inventory plans by optimally
balancing the trade-off between
service levels and cost.
The predictive analytics-based solution can analyse historical and current data of equipment performance to
create predictive maintenance plans, which can be further used to forecast demand for spare parts. These
predictive spare parts inventory plans can be integrated with the predictive maintenance plans, which can
ultimately help the companies to order spare parts when they are required and minimize the costs associated
with spare parts inventory levels. It can also help to minimize equipment downtime by ensuring that the relevant
spare parts are available at the appropriate locations and at the time of requirement, for conducting predictive
maintenance.
Description
05 Predictive maintenance for optimization of
inventory of spare parts in the manufacturing
industry

16. 16
Predictive Maintenance | White Paper
CHALLENGES IN ADOPTING PREDICTIVE
MAINTENANCE
Although the benefit of predictive maintenance is immense for organizations, but there are certain areas in
which organizations often struggle which are highlighted below-
For utilizing the true power of predictive maintenance, organizations need to have scalable and flexible data
management and platforms capable of applying advanced predictive analytics techniques in place for
ingesting, storing, managing, and processing the data generated by IoT devices. This involves large upfront costs
which becomes a challenge with respect to implementation for many organizations.
To implement condition-based monitoring as a precursor for predictive maintenance, organizations need a
platform that is capable of handling diverse data structures and schemas. This can include intermittent reading
of parameters, along with fully unstructured data as well.
For driving continuous monitoring and predictive maintenance, organizations need suitable platforms for
ingesting, storing and processing data streaming in from multiple sensors in real-time or near real-time
scenarios. This would help the platforms to deliver insights instantly.
For implementing real-time predictive maintenance
organizations require platforms equipped with
advanced predictive analytics capabilities like
machine learning and modelling. These platforms
should be integrated with leading Business
Intelligence (BI) solutions. Existing platforms possess
limited capabilities to derive in-depth actionable
insights and perform predictive analytics.
Current platforms provide very limited or no
machine learning or modelling capabilities to
predict instances or issues before they cause
disruptions in operations.
Costs of managing large volumes of data
Handling large volumes and varieties of data generated by IoT
Managing complexities associated with real-time data
Diverse analytical requirements
Predictive Analytics requirements

17. 17
Predictive Maintenance | White Paper
CHARACTERISTICS OF THE THOUCENTRIC
PREDICTIVE MAINTENANCE SOLUTION
Thoucentric Predictive maintenance Solution enables anomaly detection in assets and forecasts their
remaining useful life using advanced predictive analytics techniques like machine learning and deep
learning.
The solution would ingest, store, manage, and process various types of structured and unstructured data
generated by sensors and correlate them with multiple other external sources for monitoring the
equipment health and other ambient environmental conditions.
It is equipped with advanced predictive analytics-based capabilities like machine learning and deep
learning and is integrated with advanced BI capabilities as well which can help to predict potential
equipment failure instances much before they occur, to allow maintenance personnel sufficient time to
come up with strategies to address concerns.
This solution can help organizations answer the following key questions-
The solution provides a visual representation of the following, both on a cumulative and individual level
It also provides a glimpse of the current date, the equipment description, its current working condition, and
the duration after which it would require maintenance based on its current state and the environmental
conditions.
Once an anomaly is detected in equipment performance, the solution leverages these deep learning
algorithms to forecast the remaining useful life of the equipment. This is critical for organizations as it
helps them to take important decisions about whether the equipment can be operated up to the next
maintenance cycle or if a shutdown is required urgently. It also allows for optimized maintenance
scheduling and effective ordering of spare parts to cause minimum disruption in plant operations.
Number of machines which require
immediate attention
Real-time alerts on anomalous machines
behaviour
Estimated time until failure
How critical is the equipment?
How has been the historical performance of this equipment and what is its current condition?
What would be the consequence of the failure of this equipment on the business?
What actions should be taken now and how should we strategize to achieve the overall business
objectives?

18. 18
Predictive Maintenance | White Paper
With the help of the predictive maintenance
solution designed by Thoucentric, organizations
can reduce considerably, the costs associated
with inventories of spare parts by determining the
requirements of spare parts based on historical
downtime and failure rates of equipment and
opportunity costs.
Also, actual and expected performance of assets,
when analysed with respect to current
environmental conditions, provide very clear
indications of subtle changes in the performance
of equipment, which would otherwise go
unnoticed. In this way, risk management becomes
robust and accordingly organizations can prioritize
their capital and operational expenditures.
This predictive analytics solution can benefit organizations by taking in input data from various silos, and
applying advanced analytics on them, to come up with predictions of upcoming equipment failures,
much before they actually occur. It can also be integrated with their existing ERP systems, which can
enhance collaboration between various cross-functional departments and increase the efficiency of
business processes of these cross-functional departments as well by creating ripple effects. It can
contribute towards optimum spare parts management and also help in gaining real time visibility towards
prediction of upcoming equipment failures, in advance, so that organizations get sufficient lead time to
plan appropriate courses of action.
Let us now have a look at one of the sample dashboards designed to monitor equipment performance. The
dashboard can be customized depending upon specific requirements in different industries.
POTENTIAL BENEFITS OF THE THOUCENTRIC
PREDICTIVE MAINTENANCE SOLUTION
Affordable, scalable and flexible solution
More lead time to plan courses of action
to prevent breakdown
Optimized scheduling of maintenance
Increased productivity and ROI
Lesser requirement of inventory and
spare parts
Integrable with existing ERP systems
Indicates subtle changes in equipment
performance
Thus, through early warning notification
capabilities of the software, organizations would
have more lead time available with them for
planning maintenance activities and increase
productivity. This will also help them to spend less
time looking for potential issues and spend more
time in maximizing the potential amount of return
from every asset.

19. 19
Predictive Maintenance | White Paper
GLIMPSES OF A SAMPLE DASHBOARD
Count of equipment in each condition
Factors affecting RUL prediction and variation in RUL of selected
equipment with cycles performed

20. 20
Predictive Maintenance | White Paper
Maintenance history, change in working condition and forecasted
RUL for all equipment

21. 21
Predictive Maintenance | White Paper
Historical variation of RUL of selected equipment
Maintenance history and first anomaly detection of selected
equipment

22. Thoucentric Predictive maintenance solution helps organizations to apply advanced predictive analytics
techniques on massive amounts of data on a real-time basis which helps to significantly maximize reliability
and performance.
This is achievable through early warning of potential equipment failure, thus allowing maintenance personnel
sufficient lead time to plan appropriate maintenance activities. Identifying the right partner and investing in
the right solution can help a lot in better conservation of assets and in their increased life expectancy,
thereby eliminating premature replacement of machinery and equipment. This further leads to reduced
overtime costs and helps in more economical usage of maintenance workers by enabling them to work on a
scheduled basis, instead of a crash basis to repair breakdowns.
22
Predictive Maintenance | White Paper
CONCLUSION

23. 23
Predictive Maintenance | White Paper
We are a niche management consulting firm
solving complex business problems across
industries and functions. We believe in solving
classic business problems by addressing three
pillars-people, process and technology in our
solutions.
About Thoucentric
Team Digital focuses on providing data and data
driven solutions to the businesses we work with. The
vision of the practice is to become a best-in-class
data and analytics consulting organisation. Our
journey began in 2017 when we began working on a
production planning assignment for a large CPG
player and that laid the foundation to creating a
small team of data scientists that has now grown
into a full-scale data organisation. We now provide
solutions across the data landscape – from data
management to data analytics & decision
sciences. Our core capabilities lie in understanding
functional problem statements and customised
solutioning grounds-up around the same.
About Thoucentric Digital
Team Applied Research is a strong, dedicated
effort to productionize cutting edge technologies to
add value to our customers or prospective
customers by doing focused research on pushing
the bar on the current state-of-the-art. We enable
Thoucentric to venture into new and exciting
applications of machine learning and diversify
offerings on a continual basis.
About Thoucentric Applied
To learn more about the Thoucentric predictive
maintenance solution, and explore the possibilities
of a diverse set of business challenges which this
solution is capable of addressing, please feel free
to get in touch with:
Contact
01. Sid Roy
Thoucentric Labs Lead
Thoucentric Technology Pvt. Ltd.
sidroy@thoucentric.com
02. Manu Joseph
Applied Research Lead
Thoucentric Technology Pvt. Ltd.
manujoseph@thoucentric.com
03. Anuja Singh
Business development Lead
Thoucentric Technology Pvt. Ltd.
anujasingh@thoucentric.com