Big Data & Analytics: A. Vaccani&Partner AG Bid Confusion, - PowerPoint PPT Presentation

Performance Monitoring Solution Key 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 26

Remote Monitoring Service Key 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 27

Advanced Controls Solutions Key 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 28

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 29

How will the IoT Opportunity Evolve? We are Moving to Here NOW. A. Vaccani & Partner AG | 2017 | Page 30

Areas to Compete in Big Data  Distinctive Technology – Analytical tools – Connectivity – Data storage, management  Distinctive Data/Knowledge Domain – Unique process knowledge/algorithms Knowledge – Physics-based life assessments Unique to OEM  Platform Providers – GE Predix, Siemens – MicroSoft, IBM  End-to-end Solution Providers – Asset Performance Management Suites – Enterprise Asset Management Suites A. Vaccani & Partner AG | 2017 | Page 31

If not you, who? Non-Traditional Traditional  GE  IBM  Siemens  Google  Schneider  Oracle  Emerson  Microsoft  ABB  SAP  Honeywell  AspenTech  Yokogowa  Rockwell A. Vaccani & Partner AG | 2017 | Page 32

Where does the AVP Group fit? Domain Expertise Knowledge of: Domain, Analytics, Data Traditional Market, Processing Software AVP Value Proposition, DATA Players/partners Group ANALYTICS Math & Statistics Machine Computer Learning Science A. Vaccani & Partner AG | 2017 | Page 33

Digital Strategy Development - Functional Process Elements Phase 1 Phase 2 Phase 3 Implementation Digital Strategy Initialization Digital Business Strategy Preparation of Analysis Formation Implementation External Internal A. Vaccani & Partner AG | 2017 | Page 34

Project Phases Details Implementation Project Initiation Digital Business Analysis Strategy Development  Goals, deliverables,  Deep understanding of:  Digital Vision and roadmap  Final alignment of the digital  Current strategic vision key issues  Digital strategy including strategy with top  What data and value of data in the  Target focus areas to provide  Finalizing project management organization, kick-off business model customer vale  Organizational  Digital SWOT  Digital Value Chain and gaps teams requirements  Customer expectations  Monetizing strategy  Define needs for  Development budget  Competitive landscape  Make or buy internal and external  Roadmap and Millstones for  Mapping of key players  Partnering strategy and interfaces analysis implementation  Capability assessment Activity postponed into Phase 2 A. Vaccani & Partner AG | 2017 | Page 35

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 36

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 37

Thank You. AVP Group S.Affelt@AVP-Group.com +1 303-883-0399 A. Vaccani & Partner AG | 2017 | Page 38

What is Big Data, Analytics and the Internet of Things? Source: Dataconomy A. Vaccani & Partner AG | 2017 | Page 2

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 3

An Explosion of Interconnected Devices A. Vaccani & Partner AG | 2017 | Page 4

Why Now? Cost of Cost of Cost of Cost of Bandwidth Sensors RAM/Storage Computing 45% 60X 40X 20X … can now Solutions Sensors Analytics that were be applied provided to to $1M Enablers $500M assets. assets …. Computing Connectivity It’s All Coming Together! Source: Goldman Sachs Investment Research. Changes over last 10 years. A. Vaccani & Partner AG | 2017 | Page 5

I’M NOT GOOGLE. WHY DO I CARE ABOUT BIG DATA? A. Vaccani & Partner AG | 2017 | Page 6

Challenging Industrial Markets Profit Margins Capital Spending Competition Asia Growth NA Growth Europe Growth Want to Grow Your Business? Status Quo Will Not Work! Safe is Risky! A. Vaccani & Partner AG | 2017 | Page 7

If you don’t figure it out. Someone else will! Old Model New Model Sears, Macy’s Amazon Yellow Taxi Uber, Lyft Don’t Let Kodak/Motorola Digital Cameras, iPhone This Be You! Boiler OEM ?????? A. Vaccani & Partner AG | 2017 | Page 8

Where does Big Data, Analytics and Internet of Things Fit in the Power/Energy Industry? A. Vaccani & Partner AG | 2017 | Page 9

Asset Performance Management Asset Operations Management Management Predictive Optimization Maintenance Value Created Production Advanced Preventive Control Reactive Control Assets A. Vaccani & Partner AG | 2017 | Page 10

Asset Performance Management Asset Operations Management Management Predictive Optimization Maintenance Value Created Production Advanced Preventive Control Reactive Control Current Industry Practices Assets (limited by technology & cost) A. Vaccani & Partner AG | 2017 | Page 11

Asset Performance Management Asset Operations Management Management Predictive Optimization Maintenance Value Created Production Advanced Preventive Control Reactive Control Potential Future Assets Applications (enabled by technology & cost) A. Vaccani & Partner AG | 2017 | Page 12

How Data Analytics Adds Value What should I do about it? When will it Prescriptive Past happen? Analytics Why State did it Predictive happen? Analytics What Diagnostic happened? Analytics Value Descriptive Analytics Current/Future State of Analytics Source: Gartner Difficulty A. Vaccani & Partner AG | 2017 | Page 13

Key Areas of Value Creation Benefits Value Created • Early detection of failures • Reduce downtime by 35% Predictive • Improved maintenance planning • Reduce unplanned outages by 70% Maintenance • Reduce maintenance costs by 25%. Remaining useful life predictions. Advanced Pattern • Reduced capital spending • Increase availability Recognition + • Lengthen maintenance intervals Diagnostics + Prognostics • • Improve efficiency by 2-8% Real-time performance data Performance • Maintain optimum capacity Identify specific components Monitoring contributing to inefficiency Real-time process • Improved thermal efficiency monitoring + Diagnostics • Optimized process control • Improve efficiency by 2-8% Advanced Controls • “Best” operator 24/7 • Maintain process quality Adaptive, Predictive • Optimize over transients • Maintain process stability Controls • Improve operational flexibility Sources: DOE Study on Predictive Maintenance. NETL & EPA studies on Efficiency Improvements. A. Vaccani & Partner AG | 2017 | Page 14

Predictive Maintenance Benefits Direct Value Created Accenture Study – Avoided Cost of Unplanned Outages 10GW Fleet – Lower Maintenance Costs – Higher efficiency Indirect Value Created – Better Risk Management – Real-time Decision Support – Strategic Capital Investment Decisions Duke Energy – Increased workforce effectiveness $31.5M Cost Avoidance – Improved safety  <1 year Return on Investment A. Vaccani & Partner AG | 2017 | Page 15

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 16

Where does the OEM fit? Domain Expertise Data Traditional Processing Controls DATA ANALYTICS Math & Statistics Machine Computer Learning Science A. Vaccani & Partner AG | 2017 | Page 17

Where does the OEM fit? Domain Expertise Data Traditional Processing Controls DATA ANALYTICS Math & Statistics Machine Computer Learning Science A. Vaccani & Partner AG | 2017 | Page 18

Where does the OEM fit? Domain Expertise Process/Reliability Data Traditional Processing Controls DATA ANALYTICS Math & Statistics Machine OEM Domain Knowledge is Computer Learning Science KEY to Maximize Value Extracted from Data Analytics A. Vaccani & Partner AG | 2017 | Page 19

New Business Models Can Capture Value A. Vaccani & Partner AG | 2017 | Page 20

New Business Models Product Driven Model Internet of Things Model  Run-to-failure  Remote Monitoring  Warranty response  Predictive Maintenance  Spares  Operating Performance  Field Service  Design Optimization  Retrofits/upgrades Services Services Products Products A. Vaccani & Partner AG | 2017 | Page 21

Evolution of GE Business Model High Customer Outcomes Optimized Assets & Production Customer Value • Data analytics provides decision support • Predictive Maintenance Contractual • Extended intervals in LTSA LTSA • Share risk • Reduce Total Cost of Ownership Transactional • Long-Term Service Agreement Break/fix • Preventive Maintenance • Sell Parts/Spares • Reactive Maintenance Low 1980 2017 A. Vaccani & Partner AG | 2017 | Page 22

Value Created & Value Captured Product-Based Internet of Things-Based Mindset Mindset Value Created for Customer Customer Reactively solve existing needs Proactively address real-time and Needs emergent needs Offering Stand-alone product that become Continual update of product, features & obsolete over time value created Role of Data Monitoring, Control and Safety Optimization, Improvement, Autonomy Value Captured by Supplier Path to Profit Sell the next product Enable recurring revenue streams Customer Technology know-how, IP and brand Synergies between products & services Control Points Customer reliance of Value Created Capability Maximize use of core competencies & Leverage use of core competencies & Development existing resources existing resources Focus is on Product Create new network/system value Source: SmartDesign Focus is on the System A. Vaccani & Partner AG | 2017 | Page 23

Concepts of New Solution Offerings and Business Models A. Vaccani & Partner AG | 2017 | Page 24

Predictive Maintenance Solution Key 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 25

Performance Monitoring Solution Key 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 26

Remote Monitoring Service Key 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 27

Advanced Controls Solutions Key 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 28

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 29

How will the IoT Opportunity Evolve? We are Moving to Here NOW. A. Vaccani & Partner AG | 2017 | Page 30

Areas to Compete in Big Data  Distinctive Technology – Analytical tools – Connectivity – Data storage, management  Distinctive Data/Knowledge Domain – Unique process knowledge/algorithms Knowledge – Physics-based life assessments Unique to OEM  Platform Providers – GE Predix, Siemens – MicroSoft, IBM  End-to-end Solution Providers – Asset Performance Management Suites – Enterprise Asset Management Suites A. Vaccani & Partner AG | 2017 | Page 31

If not you, who? Non-Traditional Traditional  GE  IBM  Siemens  Google  Schneider  Oracle  Emerson  Microsoft  ABB  SAP  Honeywell  AspenTech  Yokogowa  Rockwell A. Vaccani & Partner AG | 2017 | Page 32

Where does the AVP Group fit? Domain Expertise Knowledge of: Domain, Analytics, Data Traditional Market, Processing Software AVP Value Proposition, DATA Players/partners Group ANALYTICS Math & Statistics Machine Computer Learning Science A. Vaccani & Partner AG | 2017 | Page 33

Digital Strategy Development - Functional Process Elements Phase 1 Phase 2 Phase 3 Implementation Digital Strategy Initialization Digital Business Strategy Preparation of Analysis Formation Implementation External Internal A. Vaccani & Partner AG | 2017 | Page 34

Project Phases Details Implementation Project Initiation Digital Business Analysis Strategy Development  Goals, deliverables,  Deep understanding of:  Digital Vision and roadmap  Final alignment of the digital  Current strategic vision key issues  Digital strategy including strategy with top  What data and value of data in the  Target focus areas to provide  Finalizing project management organization, kick-off business model customer vale  Organizational  Digital SWOT  Digital Value Chain and gaps teams requirements  Customer expectations  Monetizing strategy  Define needs for  Development budget  Competitive landscape  Make or buy internal and external  Roadmap and Millstones for  Mapping of key players  Partnering strategy and interfaces analysis implementation  Capability assessment Activity postponed into Phase 2 A. Vaccani & Partner AG | 2017 | Page 35

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 36

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 37

Thank You. AVP Group S.Affelt@AVP-Group.com +1 303-883-0399 A. Vaccani & Partner AG | 2017 | Page 38

What is Big Data, Analytics and the Internet of Things? Source: Dataconomy A. Vaccani & Partner AG | 2017 | Page 2

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 3

An Explosion of Interconnected Devices A. Vaccani & Partner AG | 2017 | Page 4

Why Now? Cost of Cost of Cost of Cost of Bandwidth Sensors RAM/Storage Computing 45% 60X 40X 20X … can now Solutions Sensors Analytics that were be applied provided to to $1M Enablers $500M assets. assets …. Computing Connectivity It’s All Coming Together! Source: Goldman Sachs Investment Research. Changes over last 10 years. A. Vaccani & Partner AG | 2017 | Page 5

I’M NOT GOOGLE. WHY DO I CARE ABOUT BIG DATA? A. Vaccani & Partner AG | 2017 | Page 6

Challenging Industrial Markets Profit Margins Capital Spending Competition Asia Growth NA Growth Europe Growth Want to Grow Your Business? Status Quo Will Not Work! Safe is Risky! A. Vaccani & Partner AG | 2017 | Page 7

If you don’t figure it out. Someone else will! Old Model New Model Sears, Macy’s Amazon Yellow Taxi Uber, Lyft Don’t Let Kodak/Motorola Digital Cameras, iPhone This Be You! Boiler OEM ?????? A. Vaccani & Partner AG | 2017 | Page 8

Where does Big Data, Analytics and Internet of Things Fit in the Power/Energy Industry? A. Vaccani & Partner AG | 2017 | Page 9

Asset Performance Management Asset Operations Management Management Predictive Optimization Maintenance Value Created Production Advanced Preventive Control Reactive Control Assets A. Vaccani & Partner AG | 2017 | Page 10

Asset Performance Management Asset Operations Management Management Predictive Optimization Maintenance Value Created Production Advanced Preventive Control Reactive Control Current Industry Practices Assets (limited by technology & cost) A. Vaccani & Partner AG | 2017 | Page 11

Asset Performance Management Asset Operations Management Management Predictive Optimization Maintenance Value Created Production Advanced Preventive Control Reactive Control Potential Future Assets Applications (enabled by technology & cost) A. Vaccani & Partner AG | 2017 | Page 12

How Data Analytics Adds Value What should I do about it? When will it Prescriptive Past happen? Analytics Why State did it Predictive happen? Analytics What Diagnostic happened? Analytics Value Descriptive Analytics Current/Future State of Analytics Source: Gartner Difficulty A. Vaccani & Partner AG | 2017 | Page 13

Key Areas of Value Creation Benefits Value Created • Early detection of failures • Reduce downtime by 35% Predictive • Improved maintenance planning • Reduce unplanned outages by 70% Maintenance • Reduce maintenance costs by 25%. Remaining useful life predictions. Advanced Pattern • Reduced capital spending • Increase availability Recognition + • Lengthen maintenance intervals Diagnostics + Prognostics • • Improve efficiency by 2-8% Real-time performance data Performance • Maintain optimum capacity Identify specific components Monitoring contributing to inefficiency Real-time process • Improved thermal efficiency monitoring + Diagnostics • Optimized process control • Improve efficiency by 2-8% Advanced Controls • “Best” operator 24/7 • Maintain process quality Adaptive, Predictive • Optimize over transients • Maintain process stability Controls • Improve operational flexibility Sources: DOE Study on Predictive Maintenance. NETL & EPA studies on Efficiency Improvements. A. Vaccani & Partner AG | 2017 | Page 14

Predictive Maintenance Benefits Direct Value Created Accenture Study – Avoided Cost of Unplanned Outages 10GW Fleet – Lower Maintenance Costs – Higher efficiency Indirect Value Created – Better Risk Management – Real-time Decision Support – Strategic Capital Investment Decisions Duke Energy – Increased workforce effectiveness $31.5M Cost Avoidance – Improved safety  <1 year Return on Investment A. Vaccani & Partner AG | 2017 | Page 15

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 16

Where does the OEM fit? Domain Expertise Data Traditional Processing Controls DATA ANALYTICS Math & Statistics Machine Computer Learning Science A. Vaccani & Partner AG | 2017 | Page 17

Where does the OEM fit? Domain Expertise Data Traditional Processing Controls DATA ANALYTICS Math & Statistics Machine Computer Learning Science A. Vaccani & Partner AG | 2017 | Page 18

Where does the OEM fit? Domain Expertise Process/Reliability Data Traditional Processing Controls DATA ANALYTICS Math & Statistics Machine OEM Domain Knowledge is Computer Learning Science KEY to Maximize Value Extracted from Data Analytics A. Vaccani & Partner AG | 2017 | Page 19

New Business Models Can Capture Value A. Vaccani & Partner AG | 2017 | Page 20

New Business Models Product Driven Model Internet of Things Model  Run-to-failure  Remote Monitoring  Warranty response  Predictive Maintenance  Spares  Operating Performance  Field Service  Design Optimization  Retrofits/upgrades Services Services Products Products A. Vaccani & Partner AG | 2017 | Page 21

Evolution of GE Business Model High Customer Outcomes Optimized Assets & Production Customer Value • Data analytics provides decision support • Predictive Maintenance Contractual • Extended intervals in LTSA LTSA • Share risk • Reduce Total Cost of Ownership Transactional • Long-Term Service Agreement Break/fix • Preventive Maintenance • Sell Parts/Spares • Reactive Maintenance Low 1980 2017 A. Vaccani & Partner AG | 2017 | Page 22

Value Created & Value Captured Product-Based Internet of Things-Based Mindset Mindset Value Created for Customer Customer Reactively solve existing needs Proactively address real-time and Needs emergent needs Offering Stand-alone product that become Continual update of product, features & obsolete over time value created Role of Data Monitoring, Control and Safety Optimization, Improvement, Autonomy Value Captured by Supplier Path to Profit Sell the next product Enable recurring revenue streams Customer Technology know-how, IP and brand Synergies between products & services Control Points Customer reliance of Value Created Capability Maximize use of core competencies & Leverage use of core competencies & Development existing resources existing resources Focus is on Product Create new network/system value Source: SmartDesign Focus is on the System A. Vaccani & Partner AG | 2017 | Page 23

Concepts of New Solution Offerings and Business Models A. Vaccani & Partner AG | 2017 | Page 24

Predictive Maintenance Solution Key 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 25

Performance Monitoring Solution Key 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 26