1. Big data in Maritime and Oil and Gas industries
The new data reality and Industry impact

2. In a challenging world we make businesses better prepared
Low carbon energy
Short-term cost efficiency
Complexity and lack of global governance
Efficient and fast
Businesses operate in an ever more complex context and are faced with many dilemmas: Businesses rely on reliable and affordable energy, but must contribute and adapt to a world, where fossil energy is replaced by cleaner energy. Businesses are measured on short term cost efficiency, but must make complex investment decisions balancing financial, environmental and social aspects in order to stay competitive in the long term. They must navigate in a complex regulatory landscape – with ever more complex national and regional legislation – while operating in a globalized economy lacking effective global governance. At the same time. consumers, investors and other stakeholders demand full transparency.
Business must constantly adapt and enter unchartered territories to find new and smarter ways of working, while stakeholders have zero tolerance of failure, especially when it comes to safety.
We enable businesses to be better prepared for making the right decisions in this complexity.
Reliable and affordable energy
Long-term competitiveness
Trust and transparency
Safe

3. DNV GL – Industry consolidation
Is now a major player in the TIC (Testing Inspection and Certification, including Classification) industry (3rd party), and a large 2nd party (i.e. consultancy/advisor) in our industries.
Broadening of scope taken place through accusitions over the last years. The merger between DNV and GL was the first consolidation among the global classification societies, but each of these two organisations had also consolidated their positions within the power transmission and distribution sector (KEMA), the renewable energy sector (Garrad Hassan and KEMA - DNV GL is now the largest independent renewables consultant), oil and gas (Noble Denton and Advantica).

4. Global reach – local competence
Our global reach and density of offices around the world is another factor that enables us to take a broader view. Our 16,000 employees are spread over 100 countries and we pride ourselves of having the same high standards where ever you meet us around the world.
150
years
400
offices
100
countries
16,000
employees

5. How Big data can transform our industries
How Big data con contribute to make our industries safer, smarter and greener
Some examples from DNV activities

6. Big data–some key identifiers and business value
Sources
Characteristics
New Capabilities
Business value
Volume
(real time)
Behavioural data
Technology
Target marketing
Velocity
(real time)
Sensor data
Interconnectedness and data sharing
Variety
(Sensor,
transaction,
text etc.)
Competence
Health diagnostics
Geospatial data
Veracity
(VERITAS)
Transaction / Contributory data
Data management & Governance
Logistics and optimisation

7. Big data - the new data reality
"The Internet has changed the way we consume information and talk with each other, but now it can do more, by connecting intelligent machines to each other and ultimately to people, and by combining software and big data analytics, we can push the boundaries of physical and material sciences to change the way the world works.“
General Electric CEO Jeff Immelt

8. Impacting DNV-GL’s industries – main trends
Increased number of sensors
Increased ability to exploit sensor data
Cross system data analytics
Applications;
Use of sensor data across systems for performance-, condition-monitoring, predictive maintenance, and system optimization as well as automation of problem solving and decision making
Taleris is a joint venture by GE and Accenture providing intelligent operations services across systems in the aviation industry, the service uses the customers own data streams and facilitate optimizing of asset utilization and predictive maintenance of critical systems
Taleris, a joint venture company of General Electric and Accenture, is operating with progressive technology assets and capability from both Accenture and GE. Taleris is dedicated to providing airlines and cargo carriers around the world with intelligent operations services focused on improving efficiency by leveraging aircraft performance data, prognostics and recovery across all onboard systems.
The business case for Taleris is that they believe that they can save airlines 10 % of their unscheduled cancellations, which, for the big operators, could save them in the order of hundreds of millions of dollars.
Example from Aviation: + =

9. Impacting DNV-GL’s industries
Energy
Grid management
Technical operation
Asset and operational risk management
Energy efficiency advice, assessment and verification
Health
Diagnostics
Patient monitoring and management
Management of patient care processes across system barriers
Epidemiology
Maritime
Technical operation and maintenance
Energy efficiency
Safety performance
Commercial operation (logistics chain optimization)
Automation of ship operations
Management and monitoring of accident and environmental risk from shipping traffic
Integrated operations
Efficient drilling and production
Asset and operational risk management
Pipeline risk management
Oil and Gas

10. Impacting DNV-GL’s industries
Energy
Health
Maritime
Oil and Gas
Optum:
The Optum One intelligent health management platform enables providers and delivery systems to improve health and manage cost.
From:
Clinical data of nearly 40 million patients
20 years of longitudinal claim data
Claims data covering more than 109 million lives
Robust socio-demographic and care management data
Premier:
Unlock your data to make real-time decisions
Data integration, analysis and collaboration as a service

11. Industry impact – What are the patterns?
Automation of insights and decisions
Increased willingness to share/combine data across barriers
New entrants in established industries with new business models and operation modes
Established actors transform their business models and operation modes
New roles like industry specific data aggregators and data stewards emerge

12. How to succeed commercially with Big data enabled services?
Business models
Operation modes
Service delivery
Commercialisation
Capabilities

13. Big data and analytics impacting the future of shipping
Sources
Characteristics
New Capabilities
Business value
Volume
(real time)
Behavioural data
Safer Operations
Technology
Velocity
(real time)
Sensor data
Interconnectedness and data sharing
Variety
(Sensor,
transaction,
text etc.)
Competence
Smarter Operations
Geospatial data
The Big data trend
Increased connectivity, new capabilities for capturing, storing, processing, presenting, and visualizing data, and transmission of large volumes of varied data at high velocity, have developed significantly in the past few years. In addition sensors have become cheaper and more commonplace, and deployment of sensors in all possible settings has increased dramatically and will continue to do so in the coming years.
Systems and components will be connected across systems and locations (the ‘internet of things’) and this will enable analytics and automation across component and systems in new ways. Decisions and knowledge will gradually be automated as data and analytic capabilities are becoming available within our industries.
In maritime and oil and gas industries there are vast amount of data of different kinds, but all arranged in siloes. The new connectivity, willingness (obligation) to share and the new capabilities to mange, process and visualise insights will enable data to be used in new ways across these siloes.
Currently the main trends within all our industries are:
Increasing use of operational data to optimize operations
Use of sensor data for condition monitoring and planned maintenance
Combining real time data with physical models for optimizing operation with respect to safety and environmental impact
What does this mean in practice? New sources of big data from sensors to social media are capturing a wealth of information.
When it comes to safety, such data can be used to augment class services with analytical insights from Class surveys, Port state inspections, Incidents, Fuel quality etc.
Big data can help manage your fuel efficiency, giving you instantaneous information on your fleet performance and benchmarking it against your own fleet and history as well as other players on the market.
And big data can help make the maritime sector greener by integrating information collected by sensors with environmental risk models.
Veracity
(VERITAS)
Transaction / Contributory data
Data management & Governance
Greener Operations

14. Big data and analytics impacting the future of shipping
Improved maintenance levels through increased transparency
Higher reliability through risk based maintenance
Reduce risk related to human error through increased automation and simulation
Increase navigational safety through real time analysis of shipping traffic
Safer
Higher Value chain efficiency through analysis of data across the logistics chain and the environment
Improved Operational Efficiency through the improved value chain efficiency and optimisation of operation
Smarter
Big data also has the power to transform the shipping industry, making it safer, smarter and greener. Our vision for big data is to “bring the power of trusted, refined and combined data to our customers for them to gain competitive advantage through new information and insights.”
Safer;
Increased transparency through analysis and sharing of data across barriers will drive improvement of general maintenance levels
Ability to analyse and learn from vast amounts of data across systems and industry enabling risk based maintenance, will drive towards higher reliability
Reduce risk related to human errors through enabling increased automation and simulation
Increase navigational safety through real time risk analysis of shipping traffic
Smarter;
Higher value chain efficiency through analysis of complex relationships of factors such as weather, port berth availability, cargo availability and Operational constraints
Improved Operational efficiency reducing fuel costs with up to 30% through analysis of complex relationships such as operational parameters (speed, trim, resistance, engine rating and tuning etc.), weather, port availability and cargo availability (improved planning can enable slow steaming)
Greener;
Reduced emissions as result of the reduced fuel consumption (above), reducing emissions by up to 30% on existing vessels
Reduced environmental impact from accidents due to the increased safety (above)
Reduced emissions as result of the reduced fuel consumption
Reduced environmental impact from accidents due to increased safety
Greener

15. DNV GL: Ambitions for the shipping industry
CO2 emissions
900
million tonnes per year
Lives lost at sea
900
ship accident fatalities per year
Average
Freight cost
7-11%
of cargo value
Ambition:
Ambition:
Ambition:
60 % reduction in CO2 emissions
90 % reduction in fatalities in shipping
Maintain or reduce present freight cost levels
Out of these we have selected three indicators and put ambitious targets for reduction. These ambitions are based on acknowlegded climate change targets and current safety levels in land based industries. In our view, meeting these ambitions will have the most profound impact on sustainability, and will help clarify what the industry must do to achieve sustainably.
900 people die in ship related accidents per year. A similar number of crew die in occupational accidents. The crew fatality rate is ten times the OECD average for industry workers. Our ambition is to reduce fatalities at sea by 90 per cent
Shipping is responsible for about 3 % of manmade emissions. In order to reach the 2 degree target, the world has to reduce emission by 60% in 2050, according to the IPCC. Our ambition is to reduce CO2 emissions by 60 per cent
The shipping industry facilitates global trade and development. Over the past decades, shipping freight costs have steadily declined, and are down to seven to 11 per cent relative to cargo value. Providing efficient transportation services is an important part of a sustainable future. Even with the increased cost of reducing emissions and accident our ambition is to maintain or reduce present freight cost levels.
Vision for Big data and analytics as one of many means to achieve these ambitions:
“Bring the power of trusted, refined and combined data to our customers for them to gain competitive advantage through new information and insights.”

16. Analytics and big data already impacting all service lines
14 April 2017
Analytics and big data already impacting all service lines
DNV Safety Insight – benchmarking safety culture
DNV Petroleum services - Benchmarking of fuel suppliers
Ship classification and ship board applications –operational quality
Monitoring emissions to air and managing energy efficiency
Analytic and Big data services
Ship emission monitoring services – monitoring emissions to air from shipping traffic
Predictive maintenance based on Sensor data from components and systems
(COMPASS)
The interesting thing for DNV is that there is a clear indication that there are similar trends in all our core business areas to capture more data, to expect risk and environmental considerations to take the data into account and to deliver analysis and models more as a continuous service as opposed to an ad-hoc service.
The dotted lines are at the stage of tenders, piloting or evaluation (wind)
Wind– field assessment and monitoring and benchmarking operational quality
Environment monitoring, Stewarding role for
Component reliability database,
Electric cars – managing battery life for vehicle fleets
Utilities - load balancing and smart metering
Pipeline risk monitoring

17. Safer - The power of analytics
Augmenting class services with analytical insights from Class surveys, Port state inspections, Incidents, Fuel quality etc.
“This is a tool for us to stop managing on perceptions and start managing on facts”. Customer feedback

18. Safer with smarter class - standardisation

19. Safer with smarter class - Survey Findings

20. Safer with smarter class - Class survey findings – examples Machinery
14 April 2017
Safer with smarter class - Class survey findings – examples Machinery
Main generator F: - Main Stator Lead cables burnt damaged between the Stator Coils and Terminal Box - Terminal bar supporting bracket iwo the Terminal box burnt. - Control transformers & accessories damaged in the fire. - Outgoing cables between the Alternator to the Main Switchboard burnt iwo the Terminal Box.
Fixed Gas Detection System was found out of order. The gas detection function for ballast tanks and pump room was totally shutdown.
Oveboard piping for SW cooling line of IGG scrubber found leaking i.w.o. a flange near the ship's shell (under the ER lowest platform). Jury-rigged bucket found.

21. Safer with smarter class using Big data – What data?
Online integrated performance monitoring with customers management systems
Evaluate and trend barrier performance and report barrier status after survey
Reporting survey findings in barrier format
Analysing survey findings on entire fleet in barrier format
As data gets bigger and richer through direct integrations, it is important to focus on the key questions

22. Smarter and greener through operational efficiency analytics
“You cannot improve what you do not manage.” “You cannot manage what you do not measure”
Know your fleet performance instantaneously
Benchmark against your own fleet and history
Benchmark against other players in the market
Data analytics powered by DNV GL experts

23. Creating services on public data

24. Smarter and greener through ship tracking analytics
Augmenting class services and creating new analytic services from AIS data and environmental models towards existing and new customer groups.
Cutting edge Big data solution
e.g. Energy savings through better voyage and speed management

25. Eco Insight illustration for home page
Engine
Weather
Hull/Propeller
Potential reduction in fuel consumption
(up to 20% of total fuel cost)
Speed
Trim
Total fuel consumption
(up to 70% of total
operational cost)
Basic fuel consumption

26. Vessel consumption and emission rank

28. AIS Business Intelligence
Insights can be used in daily operations, tactical planning or strategic decision making
Daily operations
Tactical planning
Strategic decisions 1
AIS Business Intelligence
Improved basis for strategic decision making
Benchmarking against partners and competitors
Understanding of underlying business performance drivers
Network & Fleet
Fuel consumption monitoring
Charter fleet performance
Off-hire analysis
New fleet operating profile
Carbon footprint profiling 2
Port operations
Berth availability in next port of call
Turnaround time
Anchorage waiting times
Change port of call 3
Voyage operations
Schedule integrity versus peers
Speed profiles
Delay management tactics of peers
ECA zone routing 4
Overall operations
Average trade lane utilization
Pro forma schedules (port/ anchorage/ sea passage)
Cost curve modelling (fuel, asset, port, etc) 5
Purchasing (e.g. port, bunker)
Recent bunkering activity in upcoming ports
Choose experienced repair yard
Competitor bunkering footprint
AIS Business Intelligence
17. October 2014

29. Port Operations – berth availability, turnaround times etc.

30. Big Data can enable transition from rule based via condition based to risk based maintenance also in maritime and Oil&Gas
Monitoring signals
Diagnostics
Prognostics
Condition based maintenance was introduced in aviation 40 years ago.
The green curve represent the exponential growth in passenger numbers in aviation present for a long time.
The fatalities per year however peaked in 1980 and has steadily decreased since then. Prior to this peak, CBM was introduced gradually, represented here by the blue lines marked MSG Maintenance Steering Group.
The same approach can be applied for maintenance in shipping: rule based maintenance procedures where components are replaced after a set number of hours in operation can first be replaced by condition based procedures. Ultimately a risk based approach can be used, where statistical analyses of sensor data, inspection report and other data can further optimize the maintenance procedures. This represents an analgy with the approach by google translate, where on moves from a rule based regime to a statistical or data-driven regime allowing for real-time risk-based maintenance.
First, one does not start with data, but rather a problem to be solved.
Then, given that we can collect the right data, and properly analyze and act upon them, we may make better decisions.
The benefits are many:
Increased safety and reliability
Reduced number and frequency of inspections and repairs
Improved spare parts exchange and logistics
Reduced costs related to maintenance and downtime
Preservation of asset value. As an example, a second-hand ship will be more valuable if one can prove that its current condition is tip-top.

31. Smarter – exploring data collection and diagnostic and prognostic methods
Timely maintenance based on prognosis from real time machinery sensor readings

32. Safer – New Autonomous short sea ship concept
“How far can we go with respect to energy efficiency, emissions and safety and still maintain cost effectiveness?”
Safety!!
Kilder vi har anslår at 85% av ulykker til sjøs er som følge av menneskelig svikt. De resterende tekniske feilene I stor grad er relatert til roterende maskineri. Vi ønsker å trigge en diskusjon på hvordan best mulig eliminere/mitigere disse ulykkene. Og selv om vi anerkjenner at det er en del kontroverser, ønsker vi å strekke strikken så langt som mulig og ønsker derfor å se nærmere på autonomitet I shipping.
Vi ønsker å teste dagens teknologier som:
-CAMERAS
-LIDAR
-RADAR
-GPS
-AIS
-ECDIS
Vi ser for oss at det vil være mange små steg på veien mot det ubemannede skip, med først beslutningsstøtte før man beveger seg mot mer og mer autonomitet. . I “The future of shipping” er en av ambisjonene å minimere antall dødsfall med 90% fra dagens nivå. Vi tror dette ikke vil være mulig uten økende bruk av autonomitet.

33. Greener – reducing environmental impact
Licence to drill and operate in sensitive areas
Managing impact on the environment through sensor based Big Data platform and environmental risk models

35. Safer and smarter - Industry data aggregation – data steward
Component failure data across competitive barriers stewarded by DNV GL .
“The most valuable database in the offshore industry”
OREDA® is a project organisation sponsored by eight oil and gas companies with worldwide operations. OREDA's main purpose is to collect and exchange reliability data among the participating companies and act as The Forum for co-ordination and management of reliability data collection within the oil and gas industry. OREDA has established a comprehensive databank with reliability and maintenance data for exploration and production equipment from a wide variety of geographic areas, installations, equipment types and operating conditions. Offshore subsea and topside equipment are primarily covered, but onshore equipment is also included. The OREDA® data are stored in a database, and specialised OREDA® software has been developed to collect, retrieve and analyse the information.