Post on 28-May-2020
A. Vaccani & Partner AG | 2017 | Page 1
A. Vaccani&Partner AG
Zollikerstrassse 141
P.O. Box 1682
CH-8032 Zurich
Switzerland
T +41 44 392 99 00
info@avp-group.net
www.avp-group.net
Big Data & Analytics:
Bid Confusion, Big Threat or Big Opportunity?
Presented by Scott Affelt
January 2017
A. Vaccani & Partner AG | 2017 | Page 2
What is Big Data, Analytics and the
Internet of Things?
Source: Dataconomy
A. Vaccani & Partner AG | 2017 | Page 3
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.
A. Vaccani & Partner AG | 2017 | Page 4
An Explosion of Interconnected Devices
A. Vaccani & Partner AG | 2017 | Page 5
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
A. Vaccani & Partner AG | 2017 | Page 6
I’M NOT GOOGLE.
WHY DO I CARE ABOUT BIG DATA?
A. Vaccani & Partner AG | 2017 | Page 7
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!
A. Vaccani & Partner AG | 2017 | Page 8
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!
A. Vaccani & Partner AG | 2017 | Page 9
Where does Big Data, Analytics and
Internet of Things Fit in the
Power/Energy Industry?
A. Vaccani & Partner AG | 2017 | Page 10
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
A. Vaccani & Partner AG | 2017 | Page 11
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
Current Industry Practices(limited by technology & cost)
A. Vaccani & Partner AG | 2017 | Page 12
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
Potential Future
Applications(enabled by technology & cost)
A. Vaccani & Partner AG | 2017 | Page 13
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
A. Vaccani & Partner AG | 2017 | Page 14
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
• Improve efficiency by 2-8%
• Maintain process quality
• Maintain process stability
A. Vaccani & Partner AG | 2017 | Page 15
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
A. Vaccani & Partner AG | 2017 | Page 16
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
A. Vaccani & Partner AG | 2017 | Page 17
Domain Expertise
Computer Science
Math & Statistics
Where does the OEM fit?
Machine
Learning
Data
ProcessingTraditional
Controls
DATA
ANALYTICS
A. Vaccani & Partner AG | 2017 | Page 18
Domain Expertise
Computer Science
Math & Statistics
Where does the OEM fit?
Machine
Learning
Data
ProcessingTraditional
Controls
DATA
ANALYTICS
A. Vaccani & Partner AG | 2017 | Page 19
Domain Expertise
Process/Reliability
Computer Science
Math & Statistics
Where does the OEM fit?
Machine
Learning
Data
ProcessingTraditional
Controls
DATA
ANALYTICS
OEM Domain Knowledge is
KEY to Maximize Value
Extracted from Data Analytics
A. Vaccani & Partner AG | 2017 | Page 20
New Business Models Can Capture Value
A. Vaccani & Partner AG | 2017 | Page 21
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
A. Vaccani & Partner AG | 2017 | Page 22
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
A. Vaccani & Partner AG | 2017 | Page 23
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
A. Vaccani & Partner AG | 2017 | Page 24
Concepts of New Solution
Offerings and Business Models
A. Vaccani & Partner AG | 2017 | Page 25
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
Value Created for Customer
• 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
A. Vaccani & Partner AG | 2017 | Page 26
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)
Value Created for Customer
• Improved thermal efficiency, reliability and availability
• Identifies and quantifies specific areas for potential upgrade/repair projects
Value Captured for Supplier
• Identifies and quantifies specific areas for potential upgrade/repair projects
• SaaS – Recurring revenue stream
A. Vaccani & Partner AG | 2017 | Page 27
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
• Prognostic capability to predict remaining useful life (RUL) of component
Key Enablers
• Data connectivity
• Same as Predictive Maintenance & Performance Monitoring Solutions
• Subject matter experts and remote monitoring resources
Value Created for Customer
• Same as Predictive Maintenance & Performance Monitoring Services
• Less in-house SME requirements
• Less resources required to monitor assets
• Less investment in APR/IT/Resources
Value Captured for Supplier
• Same as Predictive Maintenance & Performance Monitoring Services
• SaaS – Recurring revenue stream
• 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
A. Vaccani & Partner AG | 2017 | Page 28
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
Value Created for Customer
• Optimized process control simulates the “Best” operator 24/7
• Optimize process over transient conditions (various loads, fuels)
• Improve operational flexibility
• Improve efficiency
• Reduce emissions
Value Captured for Supplier
• Expand automation/controls solutions to offer higher value
• Leverage process knowledge
• SaaS or shared savings – Recurring revenue stream
A. Vaccani & Partner AG | 2017 | Page 29
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
Value Created for Customer
• Lower capital outlay for equipment
• Transfer risks to those best suited to manage it
• Allows focus on core business
Value Captured for Supplier
• 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
A. Vaccani & Partner AG | 2017 | Page 30
How will the IoT Opportunity Evolve?
We are Moving to Here NOW.
A. Vaccani & Partner AG | 2017 | Page 31
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
A. Vaccani & Partner AG | 2017 | Page 32
If not you, who?
Traditional
GE
Siemens
Schneider
Emerson
ABB
Honeywell
AspenTech
Yokogowa
Rockwell
Non-Traditional
IBM
Oracle
Microsoft
SAP
A. Vaccani & Partner AG | 2017 | Page 33
Domain Expertise
Computer Science
Math & Statistics
Where does the AVP Group fit?
Machine
Learning
Data
ProcessingTraditional
Software
DATA
ANALYTICS
AVP
Group
Knowledge of:
Domain,
Analytics,
Market,
Value Proposition,
Players/partners
A. Vaccani & Partner AG | 2017 | Page 34
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
A. Vaccani & Partner AG | 2017 | Page 35
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
A. Vaccani & Partner AG | 2017 | Page 36
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
A. Vaccani & Partner AG | 2017 | Page 37
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
A. Vaccani & Partner AG | 2017 | Page 38
Thank You.
AVP Group
S.Affelt@AVP-Group.com
+1 303-883-0399
A. Vaccani & Partner AG | 2017 | Page 2
What is Big Data, Analytics and the
Internet of Things?
Source: Dataconomy
A. Vaccani & Partner AG | 2017 | Page 3
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.
A. Vaccani & Partner AG | 2017 | Page 4
An Explosion of Interconnected Devices
A. Vaccani & Partner AG | 2017 | Page 5
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
A. Vaccani & Partner AG | 2017 | Page 6
I’M NOT GOOGLE.
WHY DO I CARE ABOUT BIG DATA?
A. Vaccani & Partner AG | 2017 | Page 7
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!
A. Vaccani & Partner AG | 2017 | Page 8
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!
A. Vaccani & Partner AG | 2017 | Page 9
Where does Big Data, Analytics and
Internet of Things Fit in the
Power/Energy Industry?
A. Vaccani & Partner AG | 2017 | Page 10
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
A. Vaccani & Partner AG | 2017 | Page 11
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
Current Industry Practices(limited by technology & cost)
A. Vaccani & Partner AG | 2017 | Page 12
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
Potential Future
Applications(enabled by technology & cost)
A. Vaccani & Partner AG | 2017 | Page 13
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
A. Vaccani & Partner AG | 2017 | Page 14
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
• Improve efficiency by 2-8%
• Maintain process quality
• Maintain process stability
A. Vaccani & Partner AG | 2017 | Page 15
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
A. Vaccani & Partner AG | 2017 | Page 16
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
A. Vaccani & Partner AG | 2017 | Page 17
Domain Expertise
Computer Science
Math & Statistics
Where does the OEM fit?
Machine
Learning
Data
ProcessingTraditional
Controls
DATA
ANALYTICS
A. Vaccani & Partner AG | 2017 | Page 18
Domain Expertise
Computer Science
Math & Statistics
Where does the OEM fit?
Machine
Learning
Data
ProcessingTraditional
Controls
DATA
ANALYTICS
A. Vaccani & Partner AG | 2017 | Page 19
Domain Expertise
Process/Reliability
Computer Science
Math & Statistics
Where does the OEM fit?
Machine
Learning
Data
ProcessingTraditional
Controls
DATA
ANALYTICS
OEM Domain Knowledge is
KEY to Maximize Value
Extracted from Data Analytics
A. Vaccani & Partner AG | 2017 | Page 20
New Business Models Can Capture Value
A. Vaccani & Partner AG | 2017 | Page 21
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
A. Vaccani & Partner AG | 2017 | Page 22
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
A. Vaccani & Partner AG | 2017 | Page 23
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
A. Vaccani & Partner AG | 2017 | Page 24
Concepts of New Solution
Offerings and Business Models
A. Vaccani & Partner AG | 2017 | Page 25
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
Value Created for Customer
• 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
A. Vaccani & Partner AG | 2017 | Page 26
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)
Value Created for Customer
• Improved thermal efficiency, reliability and availability
• Identifies and quantifies specific areas for potential upgrade/repair projects
Value Captured for Supplier
• Identifies and quantifies specific areas for potential upgrade/repair projects
• SaaS – Recurring revenue stream
A. Vaccani & Partner AG | 2017 | Page 27
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
• Prognostic capability to predict remaining useful life (RUL) of component
Key Enablers
• Data connectivity
• Same as Predictive Maintenance & Performance Monitoring Solutions
• Subject matter experts and remote monitoring resources
Value Created for Customer
• Same as Predictive Maintenance & Performance Monitoring Services
• Less in-house SME requirements
• Less resources required to monitor assets
• Less investment in APR/IT/Resources
Value Captured for Supplier
• Same as Predictive Maintenance & Performance Monitoring Services
• SaaS – Recurring revenue stream
• 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
A. Vaccani & Partner AG | 2017 | Page 28
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
Value Created for Customer
• Optimized process control simulates the “Best” operator 24/7
• Optimize process over transient conditions (various loads, fuels)
• Improve operational flexibility
• Improve efficiency
• Reduce emissions
Value Captured for Supplier
• Expand automation/controls solutions to offer higher value
• Leverage process knowledge
• SaaS or shared savings – Recurring revenue stream
A. Vaccani & Partner AG | 2017 | Page 29
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
Value Created for Customer
• Lower capital outlay for equipment
• Transfer risks to those best suited to manage it
• Allows focus on core business
Value Captured for Supplier
• 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
A. Vaccani & Partner AG | 2017 | Page 30
How will the IoT Opportunity Evolve?
We are Moving to Here NOW.
A. Vaccani & Partner AG | 2017 | Page 31
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
A. Vaccani & Partner AG | 2017 | Page 32
If not you, who?
Traditional
GE
Siemens
Schneider
Emerson
ABB
Honeywell
AspenTech
Yokogowa
Rockwell
Non-Traditional
IBM
Oracle
Microsoft
SAP
A. Vaccani & Partner AG | 2017 | Page 33
Domain Expertise
Computer Science
Math & Statistics
Where does the AVP Group fit?
Machine
Learning
Data
ProcessingTraditional
Software
DATA
ANALYTICS
AVP
Group
Knowledge of:
Domain,
Analytics,
Market,
Value Proposition,
Players/partners
A. Vaccani & Partner AG | 2017 | Page 34
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
A. Vaccani & Partner AG | 2017 | Page 35
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
A. Vaccani & Partner AG | 2017 | Page 36
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
A. Vaccani & Partner AG | 2017 | Page 37
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
A. Vaccani & Partner AG | 2017 | Page 38
Thank You.
AVP Group
S.Affelt@AVP-Group.com
+1 303-883-0399
A. Vaccani & Partner AG | 2017 | Page 2
What is Big Data, Analytics and the
Internet of Things?
Source: Dataconomy
A. Vaccani & Partner AG | 2017 | Page 3
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.
A. Vaccani & Partner AG | 2017 | Page 4
An Explosion of Interconnected Devices
A. Vaccani & Partner AG | 2017 | Page 5
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
A. Vaccani & Partner AG | 2017 | Page 6
I’M NOT GOOGLE.
WHY DO I CARE ABOUT BIG DATA?
A. Vaccani & Partner AG | 2017 | Page 7
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!
A. Vaccani & Partner AG | 2017 | Page 8
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!
A. Vaccani & Partner AG | 2017 | Page 9
Where does Big Data, Analytics and
Internet of Things Fit in the
Power/Energy Industry?
A. Vaccani & Partner AG | 2017 | Page 10
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
A. Vaccani & Partner AG | 2017 | Page 11
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
Current Industry Practices(limited by technology & cost)
A. Vaccani & Partner AG | 2017 | Page 12
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
Potential Future
Applications(enabled by technology & cost)
A. Vaccani & Partner AG | 2017 | Page 13
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
A. Vaccani & Partner AG | 2017 | Page 14
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
• Improve efficiency by 2-8%
• Maintain process quality
• Maintain process stability
A. Vaccani & Partner AG | 2017 | Page 15
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
A. Vaccani & Partner AG | 2017 | Page 16
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
A. Vaccani & Partner AG | 2017 | Page 17
Domain Expertise
Computer Science
Math & Statistics
Where does the OEM fit?
Machine
Learning
Data
ProcessingTraditional
Controls
DATA
ANALYTICS
A. Vaccani & Partner AG | 2017 | Page 18
Domain Expertise
Computer Science
Math & Statistics
Where does the OEM fit?
Machine
Learning
Data
ProcessingTraditional
Controls
DATA
ANALYTICS
A. Vaccani & Partner AG | 2017 | Page 19
Domain Expertise
Process/Reliability
Computer Science
Math & Statistics
Where does the OEM fit?
Machine
Learning
Data
ProcessingTraditional
Controls
DATA
ANALYTICS
OEM Domain Knowledge is
KEY to Maximize Value
Extracted from Data Analytics
A. Vaccani & Partner AG | 2017 | Page 20
New Business Models Can Capture Value
A. Vaccani & Partner AG | 2017 | Page 21
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
A. Vaccani & Partner AG | 2017 | Page 22
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
A. Vaccani & Partner AG | 2017 | Page 23
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
A. Vaccani & Partner AG | 2017 | Page 24
Concepts of New Solution
Offerings and Business Models
A. Vaccani & Partner AG | 2017 | Page 25
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
Value Created for Customer
• 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
A. Vaccani & Partner AG | 2017 | Page 26
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)
Value Created for Customer
• Improved thermal efficiency, reliability and availability
• Identifies and quantifies specific areas for potential upgrade/repair projects
Value Captured for Supplier
• Identifies and quantifies specific areas for potential upgrade/repair projects
• SaaS – Recurring revenue stream
A. Vaccani & Partner AG | 2017 | Page 27
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
• Prognostic capability to predict remaining useful life (RUL) of component
Key Enablers
• Data connectivity
• Same as Predictive Maintenance & Performance Monitoring Solutions
• Subject matter experts and remote monitoring resources
Value Created for Customer
• Same as Predictive Maintenance & Performance Monitoring Services
• Less in-house SME requirements
• Less resources required to monitor assets
• Less investment in APR/IT/Resources
Value Captured for Supplier
• Same as Predictive Maintenance & Performance Monitoring Services
• SaaS – Recurring revenue stream
• 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
A. Vaccani & Partner AG | 2017 | Page 28
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
Value Created for Customer
• Optimized process control simulates the “Best” operator 24/7
• Optimize process over transient conditions (various loads, fuels)
• Improve operational flexibility
• Improve efficiency
• Reduce emissions
Value Captured for Supplier
• Expand automation/controls solutions to offer higher value
• Leverage process knowledge
• SaaS or shared savings – Recurring revenue stream
A. Vaccani & Partner AG | 2017 | Page 29
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
Value Created for Customer
• Lower capital outlay for equipment
• Transfer risks to those best suited to manage it
• Allows focus on core business
Value Captured for Supplier
• 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
A. Vaccani & Partner AG | 2017 | Page 30
How will the IoT Opportunity Evolve?
We are Moving to Here NOW.
A. Vaccani & Partner AG | 2017 | Page 31
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
A. Vaccani & Partner AG | 2017 | Page 32
If not you, who?
Traditional
GE
Siemens
Schneider
Emerson
ABB
Honeywell
AspenTech
Yokogowa
Rockwell
Non-Traditional
IBM
Oracle
Microsoft
SAP
A. Vaccani & Partner AG | 2017 | Page 33
Domain Expertise
Computer Science
Math & Statistics
Where does the AVP Group fit?
Machine
Learning
Data
ProcessingTraditional
Software
DATA
ANALYTICS
AVP
Group
Knowledge of:
Domain,
Analytics,
Market,
Value Proposition,
Players/partners
A. Vaccani & Partner AG | 2017 | Page 34
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
A. Vaccani & Partner AG | 2017 | Page 35
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
A. Vaccani & Partner AG | 2017 | Page 36
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
A. Vaccani & Partner AG | 2017 | Page 37
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
A. Vaccani & Partner AG | 2017 | Page 38
Thank You.
AVP Group
S.Affelt@AVP-Group.com
+1 303-883-0399