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

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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