Engineering Analytics Opportunity
Zinnov Report – September 2013
Engineering Analytics has the potential to transform
businesses across all verticals
Definition:
Deriving meaningful insig...
Engineering analytics is broadly the amalgamation of three
systems which form its value chain

ENGINEERING ANALYTICS VALUE...
Macro trends that enable engineering analytics
1

Era of personalization

2

Advancement in analytics
3

Shift towards ser...
Use Case: OEMs can use aircraft data to predict failure of a
component and plan a proactive maintenance schedule
The data ...
Potential benefits from EA are expected to double over the
next five years

~ USD 501
billion

CAGR
15.2 %

~ USD 247
bill...
OEMs, suppliers and end customers are currently spending
close to USD 13 billion on EA related products and services

Syst...
Out of the EA related spending about 5.4 billion is completely
addressable by Indian service providers
System Development ...
India Value Proposition: India understands technology as well
as engineering
Technology: Big Data, Cloud, Analytics

Gover...
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NASSCOM Engineering Summit 2013: Engineering analytics a big game changer opportunities and challenges - Pari Natarajan, Zinnov Management Consulting Pvt. Ltd.

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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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  • Era of personalizationThe vast amount of customer data captured every day helps organisations better understand their customers. This shift towards improving customer relations will help in driving EA as:Organisations seek more parameters to differentiate customers as the need for micro segmentation growsOrganisations attempt to respond to customers' needs and demands in real timeAdvancement in analyticsEvolution of computing technologies such as in memory computing, analytical platforms such as Hadoop and NoSQL and analytical techniques such as sentiment analysis, speech analytics etc. have enabled EA as: These technologies allow organisations to process large streams of data including semi structured and unstructured data. Thus, allowing them to draw actionable insights from these data streams. These technologies allow organisation to measure complex parameters such as customer sentiments further enabling data driven decision makingShift towards servicesOrganisations are shifting their focus from product to service. MNCs such as IBM have transformed to such an extent that services account for more than 55 percent of the company’s revenue. Such a change will drive EA as:Services require more customer centric focus with a need to provide further value adds to the customers. This can only be provided by EA like enablement.Accelerated cloud adoption in core industriesCloud penetration in technology industries has always been high but the increase in cloud adoption in core industries such as power, manufacturing, oil & gas will further enable EA across industry segments as:It will make data storage more scalable and allow organisations to store large chunks of data economicallyCloud based analytics will bring computing infrastructure closer to the data resulting in decreased latency and faster decision makingPrevalence of sensorsSensors are increasingly being deployed in multiple systems and are generating data that can be used to gain actionable insights. This increase in deployment is due to reduced cost of deployment and improvements in computing power. This is driving EA as:This has resulted in an increase in the number of data streams which makes in-depth analysis of parameters possibleDistributed computingDistributed computing has come to play a key role in EA as :The evolution of distributed computing in terms of grid and cluster computing allows pooling of geographically distributed computational infrastructure for analyticsThe next level of distributed computing technologies such as fog computing integrates the characteristics of distributed computing with cloud thus becoming the ideal EA computing platforms
  • Through sensor enablement, data on engine performance indicators such as fuel usage, emissions, acoustic data, temperature levels etc. is captured and transmitted to the analytics systemsUsing predictive analytics on captured data, various parameters are calculated:Probability of failure of deviceTime to failure of deviceFuel usage forecastsWarnings and maintenance needs are communicated to the MROs
  • NASSCOM Engineering Summit 2013: Engineering analytics a big game changer opportunities and challenges - Pari Natarajan, Zinnov Management Consulting Pvt. Ltd.

    1. 1. Engineering Analytics Opportunity Zinnov Report – September 2013
    2. 2. Engineering Analytics has the potential to transform businesses across all verticals Definition: Deriving meaningful insights by processing information provided by physical machines 1 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 be 1. 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) 2 The data is transferred real-time or logged and this data can be transferred/stored by OEMs or service providers 3 Analytics the data collected can be processed, examined and interpreted for better business decisions Source: Zinnov research and analysis, interviews with key industry stakeholders 2
    3. 3. Engineering analytics is broadly the amalgamation of three systems which form its value chain ENGINEERING ANALYTICS VALUE CHAIN 1 2 3 Hardware Systems Connectivity Systems Software Systems Information generated by Machine, System and Contextual Data Various Transmission mechanisms such as M2M, WiFi, RF etc.. Mechanism to process information gathered either in Real-Time or a Passive manner Illustrative Activities - across value chain Sensor enablement Communication Enablement Analytics Embedded development M2M configuration Predictive Maintenance Testing / VA / VE Protocol development Business Logic Configuration Testing / VA / VE Applications for Handhelds Source: Zinnov research and analysis, interviews with key industry stakeholders 3
    4. 4. Macro trends that enable engineering analytics 1 Era of personalization 2 Advancement in analytics 3 Shift towards services 4 Accelerated cloud adoption in core industries 5 Prevalence of sensors 6 Distributed computing 4 Source: Zinnov Research and Analysis and Industry Reports
    5. 5. Use Case: OEMs can use aircraft data to predict failure of a component and plan a proactive maintenance schedule The data captured from aircraft engine is used to build predictive models and predict the risk of failure and forecast equipment failures. Data capture Connectivity Enablement Backend Analytics Connectivity Enablement Source: Zinnov research and analysis, interviews with key industry stakeholders and multiple industry reports Response Enablement 5
    6. 6. Potential benefits from EA are expected to double over the next five years ~ USD 501 billion CAGR 15.2 % ~ USD 247 billion Due to the nature of EA the value of benefits from improved efficiency are greater than those of increased revenue ~ USD 149 billion ~ USD 98 billion Improved Efficiency 2012 2017 Potential Benefits From Engineering Analytics (EA) Adoption (2012 - 2017) Source: Zinnov research and analysis, interviews with key industry stakeholders Increased Revenue Engineering Analytics (EA) Market Activity Split 6
    7. 7. OEMs, suppliers and end customers are currently spending close to USD 13 billion on EA related products and services System Integration ~ USD 12.6 billion CAGR 16.5 % 2012 2017 EA Total Industry Spending Projection (2012 - 2017) Source: Zinnov research and analysis, interviews with key industry stakeholders Engineering ~ USD 7.1 billion Analytics Services ~ USD 1.3 billion Analytics ~ USD 27 billion Engineering Engineering spending makes up for the majority of the EA market Analytics Infrastructure ~ USD 2.4 billion System Integration ~ USD 1.8 billion Engineering Analytics (EA) Market Activity Split 7
    8. 8. Out of the EA related spending about 5.4 billion is completely addressable by Indian service providers System Development makes up for the majority of the addressable EA market System Integration ~22 % System Development ~57 % Managed Services ~21 % ~ USD 14.8 billion ~ USD 5.4 billion CAGR 22.3 % System Development 2012 2017 EA Addressable Market Size Projection (2012 - 2017) Source: Zinnov research and analysis, interviews with key industry stakeholders System Integration Managed Services Addressable Engineering Analytics (EA) Market Activity Split 8
    9. 9. India Value Proposition: India understands technology as well as engineering Technology: Big Data, Cloud, Analytics Government Initiatives Ecosystem Presence Aadhaar: World’s Largest Big Data Experiment GI Cloud: An initiative to enable central and state governments to leverage cloud for effective delivery of eServices Engineering GoI has recently nearly tripled its expenditure in IITs Top big data companies in India 4/5 Hardware 3/5 Software 874 Asia’s Largest Tier 4 data centre Asia’s Largest operating data centre MNC R&D centres operating across 11+ verticals ~270,000 Available Talent 1.5x India exceeds USA in relevant fresh Big Data talent 50,000+ highly qualified analytics professionals Installed engineering talent base across MNCs, govt. labs and service providers ~1.200,000 Engineering enrolments every year Forbes Top 10 most funded Big Data startups Startups Source: Zinnov Research and Analysis and Industry Reports Picked over Google by GE for deployment of software over 400,000 desktops R&D Services Aerospace and Defence 9

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