Big Data & Analytics: Bid Confusion, Big Threat or Big ... · DATA ANALYTICS OEM Domain Knowledge...

A. Vaccani & Partner AG | 2017 |

A. Vaccani&Partner AG

Zollikerstrassse 141

P.O. Box 1682

CH-8032 Zurich

Switzerland

T +41 44 392 99 00

[email protected]

www.avp-group.net

Big Data & Analytics:

Bid Confusion, Big Threat or Big Opportunity?

Presented by Scott Affelt

January 2017


What is Big Data, Analytics and the

Internet of Things?

Source: Dataconomy


What is it?Big Data is high-volume, high-variety and high-velocity data that needs

Analytical tools to reveal trends, patterns and correlations that can

create actionable insights into decision-making.

Internet of Things (IoT) is the inter-networking of physical devices,

embedded with electronics, software, sensors, actuators, and network

connectivity that enable these objects to collect and exchange data.


An Explosion of Interconnected Devices


Why Now?

45%

Cost of

Sensors

Cost of

BandwidthCost of

RAM/Storage

60X 40X 20X

Source: Goldman Sachs Investment Research. Changes over last 10 years.

Connectivity

Sensors

Computing

Analytics

It’s All Coming Together!

Enablers

Solutions

that were

provided to

$500M

assets ….

… can now

be applied

to $1M

assets.

Cost of

Computing


I’M NOT GOOGLE.

WHY DO I CARE ABOUT BIG DATA?


Challenging Industrial Markets

Capital Spending Competition Profit Margins

NA Growth Europe Growth Asia Growth

Want to Grow Your Business?

Status Quo Will Not Work!

Safe is Risky!


If you don’t figure it out. Someone else will!Old Model New Model

Sears, Macy’s Amazon

Yellow Taxi Uber, Lyft

Kodak/Motorola Digital Cameras, iPhone

Boiler OEM ??????

Don’t Let

This Be You!


Where does Big Data, Analytics and

Internet of Things Fit in the

Power/Energy Industry?


Asset Performance Management

Predictive

Preventive

Reactive

Optimization

Advanced Control

ControlV

alu

e C

reate

d

Asset

ManagementOperations

Management

Assets

Main

ten

an

ce P

rod

uctio

n



Predictive

Preventive

Reactive

Optimization

Advanced Control

ControlV

alu

e C

reate

d

Asset


Management

Assets

Main

ten

an

ce P

rod

uctio

n

Current Industry Practices(limited by technology & cost)



Predictive

Preventive

Reactive

Optimization

Advanced Control

ControlV

alu

e C

reate

d

Asset


Management

Assets

Main

ten

an

ce P

rod

uctio

n

Potential Future

Applications(enabled by technology & cost)


How Data Analytics Adds Value

Difficulty

Value

Descriptive

Analytics

Whathappened?

Diagnostic

Analytics

Whydid it

happen?Predictive

Analytics

Whenwill it

happen?Prescriptive

Analytics

What should I do

about it?

Source: Gartner

Past

State

Current/Future

State of Analytics


Key Areas of Value Creation

Sources: DOE Study on Predictive Maintenance. NETL & EPA studies on Efficiency Improvements.

Benefits Value Created

Predictive

MaintenanceAdvanced Pattern

Recognition +

Diagnostics +

Prognostics

• Early detection of failures

• Improved maintenance planning

Remaining useful life predictions.

• Reduced capital spending

• Lengthen maintenance intervals

• Reduce downtime by 35%

• Reduce unplanned outages by 70%

• Reduce maintenance costs by 25%.

• Increase availability

Performance

MonitoringReal-time process

monitoring + Diagnostics

• Real-time performance data

Identify specific components

contributing to inefficiency

• Improved thermal efficiency

• Improve efficiency by 2-8%

• Maintain optimum capacity

Advanced ControlsAdaptive, Predictive

Controls

• Optimized process control

• “Best” operator 24/7

• Optimize over transients

• Improve operational flexibility


• Maintain process quality

• Maintain process stability


Predictive Maintenance Benefits

Direct Value Created

– Avoided Cost of Unplanned Outages

– Lower Maintenance Costs

– Higher efficiency

Indirect Value Created

– Better Risk Management

– Real-time Decision Support

– Strategic Capital Investment Decisions

– Increased workforce effectiveness

– Improved safety

<1 year Return on Investment

Accenture Study

10GW Fleet

Duke Energy

$31.5M Cost Avoidance


Predictive Maintenance Solutions

SmartSignal/Predix (acquired by GE)

PRiSM (acquired by Schneider Electric)

Mtell (acquired by AspenTech)

EtaPro APR (General Physics)

FAMOS (Curtiss-Wright)

SureSense by Expert Microsystems


Domain Expertise

Computer Science

Math & Statistics

Where does the OEM fit?

Machine

Learning

Data

ProcessingTraditional

Controls

DATA

ANALYTICS


Domain Expertise

Process/Reliability

Computer Science

Math & Statistics


Machine

Learning

Data


Controls

DATA

ANALYTICS

OEM Domain Knowledge is

KEY to Maximize Value

Extracted from Data Analytics


New Business Models Can Capture Value


New Business Models

Product Driven Model

Run-to-failure

Warranty response

Spares

Field Service

Retrofits/upgrades

Internet of Things Model

Remote Monitoring

Predictive Maintenance

Operating Performance

Design Optimization

Products

Services

Products

Services


Evolution of GE Business ModelC

us

tom

er

Va

lue

Hig

hL

ow

1980 2017

• Share risk

• Reduce Total Cost of Ownership

• Long-Term Service Agreement

• Preventive Maintenance

• Data analytics provides decision support

• Predictive Maintenance

• Extended intervals in LTSA

• Sell Parts/Spares

• Reactive Maintenance

TransactionalBreak/fix

ContractualLTSA

Customer

OutcomesOptimized Assets & Production


Value Created & Value Captured

Product-Based

Mindset

Internet of Things-Based

Mindset

Value Created for Customer

Customer

Needs

Reactively solve existing needs Proactively address real-time and

emergent needs

Offering Stand-alone product that become

obsolete over time

Continual update of product, features &

value created

Role of Data Monitoring, Control and Safety Optimization, Improvement, Autonomy

Source: SmartDesign

Value Captured by Supplier

Path to Profit Sell the next product Enable recurring revenue streams

Customer

Control Points

Technology know-how, IP and brand Synergies between products & services

Customer reliance of Value Created

Capability

Development

Maximize use of core competencies &

existing resources

Focus is on Product

Leverage use of core competencies &

existing resources

Create new network/system value

Focus is on the System


Concepts of New Solution

Offerings and Business Models


Predictive Maintenance SolutionKey Components of Offering

• On-site software monitors & detects equipment anomalies before component failure

• Integrates physics-based (OEM) knowledge with data-driven models

• Diagnostics capability determines likely cause of failure

• Prognostic capability to predict remaining useful life (RUL) of component

Key Enablers

• Physics-based performance, reliability and lifecycle models based on OEM

designs/knowledge

• Diagnostic “rules” for common faults

• Robust advanced pattern recognition analytics with diagnostic and prognostic capability

• Embedded or network edge analytic solutions

• Price allows access to smaller assets


• Early detection of potential failures and cost avoidance of unplanned outages

• Guidance for users to make repairs based on the diagnostics

• Better planning for repair/manage risk of failure using RUL

Value Captured for Supplier

• On-line diagnostics can better prepare for on-site work/repair

• Early failure detection can facilitate spares/inventory

• SaaS – Recurring revenue stream


Performance Monitoring SolutionKey Components of Offering

• On-site software monitors & detects performance degradation against design

specifications

• Quantifies impact of degradation

• Utilizes OEM process knowledge/models

• Diagnostics capability determines likely cause of performance shortfall

Key Enablers

• Price point of solution to customer

• May be integrated in other advanced pattern recognition solution (i.e. Predictive

Maintenance)


• Improved thermal efficiency, reliability and availability

• Identifies and quantifies specific areas for potential upgrade/repair projects





Remote Monitoring ServiceKey Components of Offering

• Remote, real-time service monitors & detects equipment and/or performance anomalies

• Integrates physics-based and process (OEM) knowledge with data-driven models

• Diagnostics capability determines likely cause of failure or degradation


Key Enablers

• Data connectivity

• Same as Predictive Maintenance & Performance Monitoring Solutions

• Subject matter experts and remote monitoring resources


• Same as Predictive Maintenance & Performance Monitoring Services

• Less in-house SME requirements

• Less resources required to monitor assets

• Less investment in APR/IT/Resources




• Data used to: identify common faults across installed base; improve hardware designs

• Triggers action for sales team for repairs, upgrades or asset replacement

• Leverage SME and knowledge base


Advanced Controls SolutionsKey Components of Offering

• Predictive, adaptive process control optimizes process (safely)

• Goes beyond standard P&ID control systems

• Can be used in supervisory or closed-loop mode

• Real-time quantification of potential value of control changes

Key Enablers

• In depth knowledge of process, control and automation

• Advanced control technology (either build, buy or license)

• Price point allows access to smaller assets


• Optimized process control simulates the “Best” operator 24/7

• Optimize process over transient conditions (various loads, fuels)


• Improve efficiency

• Reduce emissions


• Expand automation/controls solutions to offer higher value

• Leverage process knowledge

• SaaS or shared savings – Recurring revenue stream


Equipment as a Service (EaaS)Key Components of Offering

• Sell hardware as a service or outcome (i.e. $X/Y lb/hr steam, power by the hour)

• Aligns customer and supplier risk and rewards

Key Enablers

• Connectivity to data

• Same as Remote Monitoring Service

• Access to capital to fund initial outlay


• Lower capital outlay for equipment

• Transfer risks to those best suited to manage it

• Allows focus on core business


• Creates long term and aligned relationship with customer

• Creates steady recurring revenue stream

• Feeds aftermarket business

• Leverages Predictive Maintenance, Performance Monitoring and Remote M&D

platforms


How will the IoT Opportunity Evolve?

We are Moving to Here NOW.


Areas to Compete in Big Data

Distinctive Technology

– Analytical tools

– Connectivity

– Data storage, management

Distinctive Data/Knowledge

– Unique process knowledge/algorithms

– Physics-based life assessments

Platform Providers

– GE Predix, Siemens

– MicroSoft, IBM

End-to-end Solution Providers

– Asset Performance Management Suites

– Enterprise Asset Management Suites

Domain

Knowledge

Unique to OEM


If not you, who?

Traditional

GE

Siemens

Schneider

Emerson

ABB

Honeywell

AspenTech

Yokogowa

Rockwell

Non-Traditional

IBM

Google

Oracle

Microsoft

SAP


Domain Expertise

Computer Science

Math & Statistics

Where does the AVP Group fit?

Machine

Learning

Data


Software

DATA

ANALYTICS

AVP

Group

Knowledge of:

Domain,

Analytics,

Market,

Value Proposition,

Players/partners


Digital Strategy Development -

Functional Process ElementsIn

itia

liza

tio

n

Dig

ita

l S

tra

teg

y

Imp

lem

en

tati

on

Phase 1

Digital

Business

Analysis

External

Internal

Phase 2

Strategy

Formation

Phase 3

Preparation of

Implementation


Project Phases Details

Goals, deliverables,

key issues

Finalizing project

organization, kick-off

teams

Define needs for

internal and external

analysis

Deep understanding of:

Current strategic vision

What data and value of data in the

business model

Digital SWOT

Customer expectations

Competitive landscape

Mapping of key players

Capability assessment

Digital Vision and roadmap

Digital strategy including

Target focus areas to provide

customer vale

Digital Value Chain and gaps

Monetizing strategy

Make or buy

Partnering strategy and interfaces

Final alignment of the digital

strategy with top

management

Organizational

requirements

Development budget

Roadmap and Millstones for

implementation

ImplementationStrategy DevelopmentDigital Business AnalysisProject Initiation

Activity postponed into Phase 2


Mistakes to Avoid

Believing this Big Data movement will pass

Adding functionality customers don’t want

Underestimating data security risks

Failing to anticipate new competitive threats

Waiting too long to get started

Overestimating internal capabilities


Conclusions

“The future is already here – it’s just not very equally

distributed”

– William Gibson

“The only strategy that is guaranteed to fail, is not taking any

risks and not changing anything – because the world is moving

too fast”

– Mark Zuckerberg


Thank You.

AVP Group

[email protected]

+1 303-883-0399

A. Vaccani & Partner AG | 2017 | Page 2


Internet of Things?

Source: Dataconomy


Why Now?

45%

Cost of

Sensors

Cost of

BandwidthCost of

RAM/Storage

60X 40X 20X


Connectivity

Sensors

Computing

Analytics


Enablers

Solutions

that were

provided to

$500M

assets ….

… can now

be applied

to $1M

assets.

Cost of

Computing


I’M NOT GOOGLE.








Safe is Risky!






Boiler OEM ??????

Don’t Let

This Be You!



Predictive

Preventive

Reactive

Optimization

Advanced Control

ControlV

alu

e C

reate

d

Asset


Management

Assets

Main

ten

an

ce P

rod

uctio

n



Predictive

Preventive

Reactive

Optimization

Advanced Control

ControlV

alu

e C

reate

d

Asset


Management

Assets

Main

ten

an

ce P

rod

uctio

n

Potential Future




Difficulty

Value

Descriptive

Analytics

Whathappened?

Diagnostic

Analytics

Whydid it

happen?Predictive

Analytics

Whenwill it

happen?Prescriptive

Analytics

What should I do

about it?

Source: Gartner

Past

State

Current/Future

State of Analytics





Predictive


Recognition +

Diagnostics +

Prognostics










Performance










Controls



















– Improved safety


Accenture Study

10GW Fleet

Duke Energy



Domain Expertise

Computer Science

Math & Statistics


Machine

Learning

Data


Controls

DATA

ANALYTICS


Domain Expertise

Process/Reliability

Computer Science

Math & Statistics


Machine

Learning

Data


Controls

DATA

ANALYTICS





New Business Models


Run-to-failure

Warranty response

Spares

Field Service

Retrofits/upgrades


Remote Monitoring



Design Optimization

Products

Services

Products

Services



us

tom

er

Va

lue

Hig

hL

ow

1980 2017

• Share risk










ContractualLTSA

Customer




Product-Based

Mindset


Mindset


Customer

Needs


emergent needs


obsolete over time


value created


Source: SmartDesign



Customer

Control Points



Capability

Development


existing resources

Focus is on Product


existing resources









Key Enablers


designs/knowledge
















specifications




Key Enablers



Maintenance)













Key Enablers



















Key Enablers













platforms





– Connectivity





Platform Providers


– MicroSoft, IBM




Domain

Knowledge

Unique to OEM


If not you, who?

Traditional

GE

Siemens

Schneider

Emerson

ABB

Honeywell

AspenTech

Yokogowa

Rockwell

Non-Traditional

IBM

Google

Oracle

Microsoft

SAP


Domain Expertise

Computer Science

Math & Statistics


Machine

Learning

Data


Software

DATA

ANALYTICS

AVP

Group

Knowledge of:

Domain,

Analytics,

Market,

Value Proposition,

Players/partners




itia

liza

tio

n

Dig

ita

l S

tra

teg

y

Imp

lem

en

tati

on

Phase 1

Digital

Business

Analysis

External

Internal

Phase 2

Strategy

Formation

Phase 3

Preparation of

Implementation




key issues

Finalizing project


teams

Define needs for


analysis




business model

Digital SWOT








customer vale


Monetizing strategy

Make or buy



strategy with top

management

Organizational

requirements

Development budget


implementation




Mistakes to Avoid








Conclusions


distributed”

– William Gibson



too fast”

– Mark Zuckerberg


Thank You.

AVP Group

[email protected]

+1 303-883-0399



Internet of Things?

Source: Dataconomy