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Enterprise Asset Management 2.0
A Technology Perspective on Industrial
Asset Management
The biggest challenge for enterprises are the economic conditions
and disruptive markets which makes it a mundane challenge to
address the management and capitalization on the return on assets
(ROA). The factors driving is primarily aging infrastructure, return on
assets and ability to track.
Typically, an organization would expect the Enterprise asset management to deliver benefits
• Higher productivity of Equipment
• Reduced maintenance costs
• Higher visibility of operations
• Increased uptime
• Faster service time”

The performance monitoring and the
associated maintenance, repair, operations
tracking have always been managed by the
respective manufacturers. However, with the
exponential increase in number of installations
and geographically diversified locations has
added to the dimensions of the requirements
for these critical support services. This diversity
results to the need for remote monitoring with
accuracy in real time.
The productivity and profitableness depend on
business continuity of heavy duty, critical
assets working efficiently and maintenance
effectively. The full range of extraction,
transport and storage machinery must be
designed, maintained, and replaced for
maximum operational productivity.
Though the equipment’s are monitored
continuously but are configured for alerts or
alarms when the operational thresholds are
reached. This is how the risk part of the
operations is addressed but in order to
accentuate on the performance, efficiency,
increased uptime the continuously collected
data needs to be analyzed to build predictive
algorithm. These algorithms will ascertain
organization is equipped to address a situation
within the best turnaround time for a higher
First Time Fix Rate and guaranteed assurance
of Consistent performance trends across the
entire install base
Analytics – Predictive vs Suggestive
A good predictive analytics model can identify
a potential issue (What could go wrong) before
its occurrence and communicate the
organization beforehand. If the model is built
in combination with the Suggestive model that
could propose a corrective resolution (the right
solution) it would complete the loop and
transform EAM solution to a complete new
platform (Enterprise Asset Management 2.0 as
we call it)
This high end data analysis will be driven from
the asset stand point in building a better
outlook encapsulating the equipment past, its
present & probable future to secure a better
over
IT industry is currently being overrun by the
terminology war – If I try to map the IT phrases
in the above context
Additional benefits derived out of this new
platform could result in
• Increased Service performance
and parts inventory control
• Improved risk management
• Extended useful life of critical
machinery
• Low Maintenance costs with
short waiting times, better
diagnostics and parts delivery
• Better planning of Spares
• Maximized cost efficiency of
capital expenses

Business Processes IT Terminology
Equipment, Assets Install base
Enterprise Resource Planning Infor, Oracle, SAP, Microsoft
Asset monitoring software – Service Platform Infor EAM, IBM Maximo, Oracle EAM, SAP, ABB
Continuous Data collection Big Data, Data Warehousing
Physical tracking of the Equipment IOT
Predictive Analytics Requires statistical analysis with logical
conglomeration of data from various data entities
like Contracts, Warranty, Performance History &
trends, safety, environmental standards etc.
Suggestive Analytics Machine learning
The high deployment costs associated with the
Enterprise Asset Management software has
always been a rational reason for Small and
Medium- Sized Businesses (SMBs) to opt out
along with the lack of awareness. The cloud
model of deployment being cost effective
would prove beneficial and would let them
seize the opportunity to exploit the best breed
solutions becoming economical.
The monitoring component is usually handled
by EAM software application in combination
with the base ERP (Enterprise Resource
Planning). In order for best outcome this
integration has to be seamless. The data mart
for analysis is built with sources from EAM &
ERP. Integration of EAM software application
and big data analytics is big opportunity for the
software vendors.
Industries that are best suited:
 Oil & Gas
 Manufacturing
 Healthcare & Pharma
 Hi-Tech engineering & Instrumentation
 Transportation & Utilities
Geographical Regions we understand:
 North America
 Europe
 Asia Pacific
 Middle East
Organization sizes we cater:
 SMB
 Large Enterprises
Services we provide:
 Implementation
 Managed services
 Training and Support
With our EAM 2.0 team at Sailotech
IT industry is currently being overrun by the terminology war – If I try to map the IT phrases in the
above context

Find us on the Web
Web: www.sailotech.com
Blog: sailotech.com/sailotech-blog
YouTube: youtube.com/sailotech
LinkedIn: linkedin.com/sailotech
twitter: twitter.com/sailotech
Contact
Sailotech
2nd Floor, Cyber Ville
Hitech City, Madhapur
Hyderabad – 500081
Telangana, India
Sales: sales@sailotech.com
Enquiry: enquiry@sailotech.com
Call Us: +91 40 – 69000226
Copyright@2015. Sailotech . All rights reserved.
Published by: Kartik Chintalapaty
Email: kartik.chintalapaty@sailotech.com

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