BIG DATA & ADVANCED ANALYTICS TO IMPROVE SHIPPING PERFORMANCE. Digital Ship Singapore

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1 BIG DATA & ADVANCED ANALYTICS TO IMPROVE SHIPPING PERFORMANCE Digital Ship Singapore

2 Bahri s Vision CONNECTING ECONOMIES AND DRIVING EXCELLENCE IN GLOBAL LOGISTICS SERVICES

3 Introduction to Bahri BAHRI A DIVERSIFIED SHIPPING & LOGISTICS COMPANY, GLOBALLY IN TOP 5 IN ITS SEGMENTS Top 3 owner of VLCC (number of vessels) Top 3 carrier in general cargo (MTONs) China M NIOC Bahri Mosk Line Angelicoussis Friedriksen Euronav N.V. Top 5 owner of chemical carriers ( 000 DWT) COSCO Nippon DHT Grieg Star Hapag Lloyd Bahri HUAL Agromar NYK Bulk cargoes BBC chartering Blue water shipping Mst Gmbh Expanding VLCC fleet will make the company #1 global operator in size by 2017 Diversifying its customer bench across all businesses Scorpio MOSK Lines Sinokor A.P. Moller Bahri Odfjell Hafnia D Amico Sinochem Capital Maritime 1 excludes traffic from North America into MEA/ISC/MED - BTM (ALL BTM) SOURCE: Clarkson (May 2015)

4 Evolution of Technology in Everyday Life REIMAGINING COMMUNICATION THEN NOW

5 Evolution of Technology in Everyday Life REIMAGINING NAVIGATION THEN NOW

6 Evolution of Technology in Everyday Life REIMAGINING DIRECTORIES THEN NOW

7 Evolution of Technology in Everyday Life REIMAGINING CALLING A CAB THEN NOW

8 Evolution of Technology in Everyday Life REIMAGINING READING THEN NOW

9 Evolution of Technology in Everyday Life REIMAGINING PAYMENTS THEN NOW

10 Evolution of Technology in Everyday Life REIMAGINING BREAKING NEWS THEN NOW

11 Evolution of Technology in Everyday Life REIMAGINING HEALTHCARE THEN NOW

12 Evolution of Technology in Everyday Life REIMAGINATION OF DRIVING THEN NOW

13 Evolution of Technology in Everyday Life REIMAGINATION OF DEFENCE THEN NOW

14 Evolution of Technology in Everyday Life REIMAGINATION OF MARINE ENGINEERING THEN NOW

15 Potential Impact of Big Data in the Maritime Industry TYPICAL MARITIME INDUSTRY DATA GENERATION 110M TO 120M ACTIVITIES 60GB of Data per day per vessel 0.05 sec between each motion measurement on a ship 5,000 Data tags on a modern vessel 2,800 Sensors hardwired into the vessel s machinery produces TB 11 TB Data transmitted transmitted Data over satellite satellite over links links 7-8 TB Of fleet sensor data 4-6 TB / sec AIS Vessel location Data 2-3 TB Weather Data

16 Potential Impact of Big Data in the Maritime Industry A WORLD WHERE SHIPS AND THE INDUSTRY AMASS VOLUMES OF DATA EVERY DAY ASSET PEOPLE DATA Merchant Offshore Other Naval Vessel Count 111K 10K 30K 11K $1T in commodities traded / year Maritime Industry $435B+ in M Jobs Diverse Functions 110M-112M Data Points Each Day AIS Data represents 4-6TB* 10+ years of look back operation data 2TB of Market Data *Geo Physical Location

17 BIG DATA VOLUME MEDIA & SERVICES EDUCATION GOVEERNMENT HEALTHCARE PROVIDERS INSURANCE MANUFACTURIN G RETAIL TRANSPORTATIO N UTILITIES WHOLESALE & TRADE MARITIME INDUSTRY Potential Impact of Big Data in the Maritime Industry MARITIME INDUSTRY REPRESENTS AN OPPORTUNITY FOR BIG DATA COMPUTING VOLUME VELOCITY VARIETY VERY HOT (COMPARED TO OTHER INDUSTRIES) HOT MODERATE LOW VERY LOW (COMPARED TO OTHER INDUSTRIES)

18 Potential Impact of Big Data in the Maritime Industry PROFOUND INNOVATION DRIVEN BY TECHNOLOGY WILL IMPACT SHIPPING INDUSTRY S FUTURE TECHNOLOGY OPERATIONAL EXCELLENCE CUSTOMER SERVICE Big Data Internet of Things (IoT) Data Driven Efficiency Door to Door Value Added Services A study by the Massachusetts Institute of Technology (MIT) reported that data-driven firms perform 5%-6% better each year Forbes, May 2015

19 Potential Impact of Big Data in the Maritime Industry ENVISIONING A DATA MODEL CAPABLE OF FUTURE DECISIONS OF THE MARITIME INDUSTRY Q Q A A STRATEGIC COMMERCIAL OPERATIONAL PREDICTIVE SEMI REAL TIME TODAY Most decisions in the shipping industry are made here. At the latest. Not-so-well informed, ad-hoc, non-optimal decision Sporadic use cases REAL TIME TOMORR OW BIG DATA BRIDGE THE GAP

20 Bahri s Experience with Big Data BAHRI S BIG DATA TECHNOLOGY & ARCHITECTURE EVOLUTION INTERNAL DATA BAHRI BIG DATA GRID Operation Data EXTERNAL DATA BAHRI DATA LAKE Weather Information Market Information Port Information Supplier Information Piracy Information Customer Information

21 Bahri s Experience with Big Data INTRODUCING BRISO BIGDATA RECOURSE FOR INTEGRATED SEA OPERATION BIG DATA MODELS PREDICTIONS Vessel Location Vessel Alerts UNSTRUCTURED DATA Voyage Analysis MARKET MOOD INDICATION Drill Down Visualizations

22 Bahri s Experience with Big Data BRISO EXAMPLES PORT ARRIVALS; PORT CONGESTION Scenario 1 How busy is the port on a given day Predict 3rd party ships BIG DATA MODEL Charter: xxx Cargo: Chemicals - Monoethylyne Quantity: 35,000 Destination: Sikka TCE: $28,000 Q Q A How many Ships will be in a port in future? What is the market Sentiment? NCC REEM ETA at Dongguan: 3rd April 2016 Additional Bunker Cost: $16,575 Speed: 13.5 knots BRISO Suggests an alternative as shown to arrive Dongguan 2 days earlier avoiding bad weather in South China Sea to pick up Monoethylyne Parcel to Sikka which will give a laden backhaul with a good TCE. Overall TCE including additional bunker costs - $21,000. Assist in timing of fixture Predict impact of TCE Rate Improve Charter in Margin

23 Bahri s Experience with Big Data BRISO EXAMPLES NEXT BEST CARGO Scenario 2 Next Best Cargo Predictions What is the right cargo mix? BIG DATA MODEL Q Q A What is the next best cargo of the day? What is the Cargo you should carry? Improve TCE Rate NCC REEM Cargo Information Assignment: Port A to B, committed ETA x Empty tanks:

24 Bahri s Experience with Big Data BRISO EXAMPLES MARKET SENTIMENT ANALYSIS Scenario 3 How does the market sentiment look a week from? BIG DATA MODEL Q Q A What will the market sentiment be next week? Should I fix my ships earlier or later? NATURAL LANGUAGE PROCESSING ARTIFICIAL INTELLIGENCE MACHINE LEARNING

25 PREDICT PORT CARGO MIX OPTIMA CUSTOMER EXPERIENCE / SALES LOCATION & NETWORK CONSUMPTION (FUEL & OTHERS) OPTIMAL SPEED SUPPLY CHAIN / LOGISTICS CREW PRODUCTIVITY RESOURCE OPTIMIZATION SAFETY / QUALITY / RELIABILITY PERFORMANCE MANAGEMENT CAPITAL EMPLOYED END OF LIFE COST Bahri s Experience with Big Data BIG DATA SCIENCE TO DRIVE BETTER ASSET UTILIZATION, IMPROVE TOP & BOTTOM LINE REVENUE YIELD OPEX EFFICIENCY PROFITABILITY MAINTENANCE EFFICIENCY SG & A EFFICIENCY RETURN ON CAPITAL EMPLOYED CAPITAL PRODUCTIVITY ASSET UTILIZATION THOUGHT-PUT /ASSET FUEL EFFICIENCY ASSET AVAILABILITY ASSET USAGE / AVAILABLE HR PAYLOAD / USE ASSET CYCLE TIME ASSET COST CONTINUES

26 Potential Impact of Big Data in the Maritime Industry BIG DATA ECO SYSTEM FINANCE INDUSTRY MARITIME INDUSTRY PAYMENTS & TRANSFERS LENDING & FINANCING SHIP BROKERS SHIP OWNERS SHIP MANAGERS PORTS & TERMINALS RETAIL BANKING PORT AGENTS SEAFARERS CARGO OWNERS NEWS / MEDIA FINANCIAL MANAGEMENT INSURANCE MARKETS & EXCHANGES MARKETS & EXCHANGES CLASSIFICATION SOCIETIES TRADERS SHIPYARDS / BUILDERS DATA SERVICE PROVIDERS SHIP SUPPLIERS

27 Evolving the Maritime Industry through Big Data BUILDING A COLLABORATIVE ECO SYSTEM IS KEY TO FASTER PROGRESS MARITIME INDUSTRY EXPOSURES Market Fluctuations Mood of the Industry Trade Imbalance & Arbitrage Demand for commodities Freight Rates Fluctuation Geographical Concentration of Trade Demand of Oil and Dry Bulk Threats of War Piracy Safety Quality / Reliability Weather & Environmental Policies Emission Regulations BIG DATA ECOSYSTEMS Revenue Centric Bunker Data Cost Centric Port / Terminal Operators Yards Maritime Institutes Customer Centric AIS Information Port Agents Data..and many more MARITIME BASED BIG DATA Maritime based big data is hard to aggregate, structure and utilize as visibility and intelligence on ship activity does not exist today, however with a cohesive ecosystem this process will be smooth.

28 WE LOOK FORWARD TO CONTINUE WITH YOU THANK YOU