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Engineering analytics a big game changer opportunities and challenges Pari Natarajan, Co-founder and CEO, Zinnov Management Consulting Pvt. Ltd.
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Engineering Analytics OpportunityZinnov Report – September 2013
Engineering Analytics has the potential to transform businesses across all verticals
Across the globe areas such as Automotive, Aerospace, Healthcare, Energy and Industrial use several complex physical machines
These machines are continuously collecting data. The data can be1. Machine Data (Self Monitoring Lifecycle
Parameters)2. Machine to Machine Data (Communication and
exchanges)3. Contextual Data (Based on the environment,
users, traffic, et al)
Definition:Deriving meaningful insights by processing information provided by physical machines
1
The data is transferred real-time or logged and this data can be transferred/stored by OEMs or service providers
2
Analytics the data collected can be processed, examined and interpreted for better business decisions
3
Source: Zinnov research and analysis, interviews with key industry stakeholders 2
Engineering analytics is broadly the amalgamation of three systems which form its value chain
3
Hardware Systems Information generated by
Machine, System and Contextual Data
Connectivity Systems Various Transmission
mechanisms such as M2M, WiFi, RF etc..
1 2 3
Analytics
Predictive Maintenance
Applications for Handhelds
ENGINEERING ANALYTICS VALUE CHAIN
Software SystemsMechanism to process
information gathered either in Real-Time or a Passive
manner
Business Logic
Sensor enablement
Configuration
Embedded development
Testing / VA / VE
Communication Enablement
M2M configuration
Protocol development
Testing / VA / VE
Illustrative Activities - across value chain
Source: Zinnov research and analysis, interviews with key industry stakeholders
4
Era of personalization1
Advancement in analytics2
Macro trends that enable engineering analytics
Source: Zinnov Research and Analysis and Industry Reports
Shift towards services3
Accelerated cloud adoption in core industries
4
Prevalence of sensors5
Distributed computing
6
Use Case: OEMs can use aircraft data to predict failure of a component and plan a proactive maintenance schedule
5
The data captured from aircraft engine is used to build predictive models and predict the risk of failure and forecast equipment failures.
ConnectivityEnablement
Data capture Backend Analytics ConnectivityEnablement
Response Enablement
Source: Zinnov research and analysis, interviews with key industry stakeholders and multiple industry reports
Improved Efficiency Increased Revenue
~ USD 149 bil-lion
~ USD 98 billion
Potential benefits from EA are expected to double over the next five years
6
Potential Benefits From Engineering Analytics (EA) Adoption (2012 - 2017)
Due to the nature of EA the value of benefits from improved efficiency are greater than those of increased revenue
Engineering Analytics (EA) Market Activity Split2012 2017
~ USD 247billion
~ USD 501billion
CAGR15.2 %
Source: Zinnov research and analysis, interviews with key industry stakeholders
OEMs, suppliers and end customers are currently spending close to USD 13 billion on EA related products and services
7
Engineering Analytics (EA) Market Activity Split
Engineering spending makes up for the majority of the EA market
En
gin
ee
rin
gA
na
lytics
Syste
m In
teg
ratio
n
Engineering ~ USD 7.1 billion
Analytics Infrastruc-ture
~ USD 2.4 billion
Analytics Ser-vices
~ USD 1.3 billion
System Integra-tion
~ USD 1.8 billion
EA Total Industry Spending Projection (2012 - 2017)
2012 2017
~ USD 12.6billion
~ USD 27billion
CAGR16.5 %
Source: Zinnov research and analysis, interviews with key industry stakeholders
Out of the EA related spending about 5.4 billion is completely addressable by Indian service providers
8
System Development makes up for the majority of the addressable EA market
System Integra-tion ~22 %
Managed Ser-vices ~21 %
Addressable Engineering Analytics (EA) Market Activity Split
2012 2017
EA Addressable Market Size Projection (2012 - 2017)
~ USD 5.4billion
~ USD 14.8billion
System Development
~57 %
CAGR22.3 %
Source: Zinnov research and analysis, interviews with key industry stakeholders
9
Aadhaar: World’s Largest Big Data
Experiment
Government Initiatives
HardwareEcosystem Presence
Top big data companies in India
4/5
Software3/5
Available Talent
Startups
Forbes Top 10 most funded Big Data startups
Technology: Big Data, Cloud, Analytics
1.5x India exceeds USA in relevant fresh Big Data
talent
50,000+ highly qualified
analytics professionals
Engineering
GI Cloud: An initiative to enable central and
state governments to leverage cloud for effective delivery of eServices
Picked over Google by GE for deployment of software over 400,000
desktops
Asia’s Largest Tier 4 data
centre
Asia’s Largest operating data
centre
GoI has recently nearly tripled its
expenditure in IITs
874 MNC R&D centres
operating across 11+ verticals
~270,000 Installed engineering talent base across MNCs, govt. labs and service providers
~1.200,000 Engineering enrolments
every year
Aerospace and Defence
R&D Services
India Value Proposition: India understands technology as well as engineering
Source: Zinnov Research and Analysis and Industry Reports