A 2011 study by MIT and the University of Pennsylvania found that data-driven firms performed 5-6% better each year than similar non-data-driven firms. Ship management has always relied on data, but data alone has never been enough: it’s useless unless we can understand it and use it to support decision making.
Recently, big data has given us a better way to manage, use and analyse vast amounts of data to improve all aspects of ship management. Unsurprisingly, companies have been springing up to assist with the technical aspects of data collection, processing and analysis.
Ship management includes the myriad services that, directly or indirectly, allow ships to operate. Crewing, training, surveying, stores and bunkers, accounting, technical support, and many more services fall under the umbrella of “ship management”.
What is big data?
The massive volumes and types of data available today overwhelm conventional data processing and management methods. “Big data” doesn’t refer to a single technology, but to the growing array of techniques to manage and extract actionable information from data.
The three goals of big data analysis are hindsight, insight and foresight. Hindsight focuses on understanding what’s happened in the past, for example analysing fuel consumption and engine efficiency over previous voyages. Insight focuses on understanding what’s happening now, and foresight focuses on using your data to predict what will happen next.
Our brief guide to big data in shipping goes into more detail on big data and its implications for the wider shipping industry.
How can big data help with ship management?
With big data, the world-changing factor isn’t the data, it’s the ability to use it to make better decisions. But first, we have to collect the data, clean it up, standardise the format, process it, and interpret the results.
Broadly speaking, we can split ship management into those activities directly related to operating the ship, and those related to supporting the ship.
Big data’s goals of improving hindsight, insight and foresight are well-aligned with ship managers’ goals of improving safety, efficiency and earnings, and reducing costs.
Energy efficiency optimisation
Since 2013, Shipboard Energy Efficiency Management Plans (SEEMPs) have been mandatory under the International Convention for the Prevention of Pollution from Ships (MARPOL). As part of SEEMP implementation, ship managers have been collecting data related to engine efficiency. But what do they do with it?
Big data provides methods to analyse the data to gain actionable insights into fuel and energy efficiency, leading to cost reductions.
Planned maintenance systems traditionally run on a timed basis: we replace parts or carry out maintenance after a set period, whether it needs it or not. But it doesn’t have to be that way.
Predictive or condition-based maintenance compares detailed data of components or equipment with similar data from before a known failure of that equipment. Patterns in the data warn us of wear or impending failure, so we can carry out predictive maintenance when required rather than planned maintenance at fixed intervals.
Using internet of things (IoT) components simplifies, automates and expands data collection, while big data techniques automate analysis, insight and foresight. Engine components with built-in sensors, for example, can transmit more detailed internal information than surface-mounted sensors.
This enables ship managers and on-board crew to plan maintenance and order spare parts with more confidence. When combined with appropriate analysis, it could even reduce insurance-related costs.
For as long as ships have gone to sea, route planning methods have been used to take advantage of weather and currents and avoid ice and storms. Today, the difference is the sheer volume of data available.
Big data enables more routing options. Rather than traditional shortest route/fastest route/best weather options, we can route for a growing range of options including minimum traffic and least fuel. Adjusting plans en-route is considerably simpler. If the ship has a reliable internet connection the master and the ship manager can even access the same data and tools and work from a shared understanding of the situation.
Big data is an enabling technology for marine autonomous surface ships (MASS). In the long-term, this has the potential to improve safety and reduce costs. Even in the medium term, the stepping-stone of decision-support systems have the potential to improve on-board safety by providing clear real-time navigational and traffic information and analysis to watchkeeping officers.
Ship support services include crew management, safety management, provision of bunkers and stores, logistics, chartering, and voyage estimations. These all rely on using what’s happened in the past to predict what’s likely to happen in the future, then acting on that information. Coincidentally, that’s exactly where big data excels.
Everything in shipping centres around the ship’s estimated time of arrival (ETA) at the next port. Traditionally, shortly after noon each day, the navigation officer and the master use their best professional judgement to estimate the ETA. The master informs all interested parties by email. Every change causes a series of knock-on effects, as bunkers and stores are rescheduled, changes in arrival draft and the state of tide on arrival change under-keel clearance, flights and accommodation bookings for crew changes need to be amended, and many other factors.
With data synchronisation, vessel tracking, weather forecasts and analytical dashboards, ship managers can stay on top of these changes. They can predict with more certainty what’s likely to happen, and balance speed, ETA and fuel consumption against freight rates and the costs of delays.
As a bonus, when you automate the data transfer rather than constantly emailing and phoning the ship for updates, the workload of the crew on board drops. This allows the crew more time to focus on the parts of their job that a computer can’t do (yet), and could reduce stress, fatigue, frustration – and the human-factor-related accidents that result.
As with all emerging technologies, particularly in the shipping industry, implementing big data isn’t a plug-and-play solution. Travelling data sources, the lack of onboard IT staff, limited internet access, incompatible data formats, and the difficulty in updating hardware and software on a ship make even collecting the data a challenge. Variable data quality from legacy systems, data protection in transit, privacy issues, and cybersecurity only add to the problems.
Many startups address these barriers and challenges head-on, simplifying the adoption of big data systems across the shipping industry.
Supporting and related technologies
Big data doesn’t rely on internet connectivity, but connectivity definitely makes it more versatile. In the absence of reliable internet, data can be collected and processed on board, or collected on board and transferred ashore later for processing. However, the ability for ship’s crew and ship managers to collaborate on real-time data would be very limited.
Machine learning is a sophisticated data analysis tool that uses data to train a model, which is then used to make predictions. It works well with big data and is itself a supporting technology for artificial intelligence (AI).
Are companies already using big data in ship management?
Yes, in many ways. Some startups simplify the process of data collection, preparation and transmission, while others focus on analytics and presentation. Here are a few notable companies working in big data for ship management.
Propulsion Analytics specialises in energy efficiency and performance monitoring for the maritime industry. Their primary service revolves around the pioneering use of simulation models and machine learning for vessel/engine performance evaluation and fault diagnosis. Using this technology, they develop a customized “digital twin” of an engine.
By comparing routine measurements from the vessel with the reference values from the “digital twin”, they provide performance assessment information, fault diagnosis and fuel consumption optimisation in service. This minimises unplanned downtime and reduces operating costs.
Spire offers data and analytics that deliver insights into the parts of the world where collecting data is notoriously difficult. Ships, planes, and weather often go untracked in remote regions. Spire’s satellites excel in covering these areas and its products are unmatched in empowering users to take advantage of this unique data.
Spire’s mission is to inspire, lead, and create the business of Earth observation for the benefit of all. With operations all over the world, Spire maintains 24/7 coverage of the entire Earth.
Stratum Five offers combined high-definition vessel tracking, security management, route planning and weather monitoring, with a wealth of additional tools in a single interface. Seamlessly correlating high-frequency sensor data, weather and environmental data, multi-source positional data, voyage reports, and additional output metrics from proprietary performance algorithms, they provide services to over 12,000 vessels, processing more than 7.8bn data values per day.
Transmetrics’ solution provides cargo companies with data cleansing, demand forecasting and predictive optimization based on data science and AI to help companies optimize their operations.
Transmetrics works with some of the leading logistics companies in the world, including three global Fortune 500 companies. Their products have brought significant benefits to clients who have experienced an up-to-25% reduction in linehaul costs. The company’s solutions have achieved international recognition in the form of various awards such as the Forbes Business Award.
HiLo Maritime Risk Management
HiLo Maritime Risk Management is saving lives in the shipping industry by changing the way companies address risk. HiLo has created a sophisticated statistical model which translates near miss, accident and incident data from their subscribers into a comprehensive risk profile for each company and the HiLo fleet.
Armed with this analysis, companies using HiLo analysis can stop catastrophes in their tracks. All reports are anonymised, meaning each customer gets the opportunity to learn from others’ experiences without putting their information at risk. HiLo works across multiple asset types, with models currently available for tankers and bulk carriers.
So, what’s next?
With the spread of accessible internet at sea and the wider availability of IoT components, the barriers to more widespread adoption of big data in ship management will fall. The increasing awareness of the power of big data analytics and the impact of big data on all aspects of ship management will continue to increase.
The ongoing development of novel data analysis techniques, including machine learning, neural networks and artificial intelligence will doubtless lead improved safety, efficiency, and cost savings – a win for all involved in shipping, whether at sea or ashore.
If you want an in depth look at ship management technologies, our ship management technology market report profiles 140+ companies innovating in the sector.
Nic Gardner is a Maritime Technology Analyst at Thetius. She is a master mariner who holds an unlimited UK CoC and has seagoing experience on capsize bulk carriers, ro-pax ferries, sail training ships, hospital ships, general cargo tramp ships, container ships and fisheries protection boats. When she is not at sea, Nic writes about a range of topics including technology and the maritime industry. Nic is also the author of “Merchant Navy Survival Guide: Survive & thrive on your first ship”, a book to give aspiring seafarers the knowledge and tools they need to make a success of their first trip to sea.