SHIPPING ANALYTICS: improving business growth, competitive advantage and risk mitigation

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    SHIPPING ANALYTICS: improving business growth, competitive advantage and risk mitigation

    Data analytics is driving incremental value for ship owners and charterers by influencing decisions across the various business functions of the marine business—such as voyage management, vessel operations and manning, as well as chartering and third-party risk assessment. As information collection and integration throughout the shipping value chain continues to evolve, shipping companies are beginning to harness data to make a range of decisions, from managing routine activities to improving operations and driving strategic decisions focused on transforming the business. In this article, Kunal Bahl presents analytics use cases that show how charterers and ship owners can utilize the power of data and analytics to improve decision making.


    Over the last two decades, technological advancements such as electronic trading have reduced the cost of transactions while increasing competition and transparency in the trading industry. Similar technological advancements have made other industries such as insurance, healthcare, transportation and retail more competitive. However, the marine transportation business has not yet seen such large-scale transformation, which has resulted in a largely outdated and burdensome decision-making process.

    Finding the right ship for cargo at the most economical price is a key function performed by charterers. However, charterers’ access to this information is limited to what is provided by known brokers and ship owners. Since the information is shared “selectively,” it may or may not be most efficient. Charterers who have established relationships with many brokers will most likely be able to find a suitable ship to transport cargo, but the same is not true for small ship owners and charterers who lack access to timely information. In such a situation, how can charterers ensure they have made the right decision if the information provided is incomplete or suspect?

    There is an opportunity to utilize readily available, accurate and actionable information to improve decision making. Consider a charterer who is looking for a third-party vessel to move cargo from the Arabian Gulf to South East Asia for a certain cargo size and date. Rather than relying on the ship brokers for options, freight rates and other information, a simple information portal (see Figure 1) can provide alternatives. The charterer can provide pertinent inputs, such as load area (Arabian Gulf), cargo size (280,000 MT) and trade dates (October 26 to October 30). The information portal will then provide a list of suitable vessels available in the Arabian Gulf around that time.

    This is made possible by integrating Automatic Identification System (AIS) information, position reports, estimated times of arrivals, vessel particulars (such as size) and market information into an exchange portal used to find all available alternatives as well as the freight forecast. This type of portal can give charterers and ship owners access to more options thus improving transparency and competiveness. The charterers can further improve decision making by integrating vessel availability data with their internal or external vetting information. If a vessel does not meet the required standards and has below-par feedback, then the ship can be removed from the selection process early on, saving time in selecting the best available vessel for the cargo.

    Figure 1: Alternatives analysis page for charterers.


    A lion’s share of marine transportation of bulk oil and gas is enabled through third-party ships. Unlike other types, in marine transportation, charterers are responsible for the quality of the vessel and its operator. For risk-averse charterers, the viability of the vessel and its operator is as important as the charter hire rate.

    Understanding the importance of quality, vessel owners and operators are focusing more attention on ensuring that their fleets are deemed acceptable for use by charterers—and they do so as efficiently and cost effectively as possible. Instead of improving the vessel quality, their focus is on meeting or passing the acceptance criteria. The vetting process, as illustrated in Figure 2, includes feedback from various entities such as inspectors, terminals and port state authorities, as well as operator self-assessment. Some of this information is subjective in nature and can result in either extremely slow and/or bad vetting decisions.

    Figure 2: Typical information used in the vetting decision process.

    Data analytics can help charterers, along with integrated oil companies and vetting organizations, analyze the different sources of information and select the right vessel with the least amount of risk. While evaluating a vessel or an operator’s entire fleet, it is important to look at granular information by slicing it into different risk categories. Risk categorization and comparison to the industry average or averages for certain types of fleet can provide valuable insights about vessel performance to charterers. Figure 3 shows a much more objective representation of the risk rating for a vessel compared to the rest of the fleet or other hired vessels.

    Figure 3: Risk categorization based on all inputs.

    An organization’s approach to vetting needs to be nimble enough to respond to changing regulatory requirements and market dynamics. Although nearly all voyages happen without any serious incident, safety cannot merely be classified as the absence of accidents or incidents. A good test of an organization’s vetting model can be performed by simulating events, such as an incident, a detention or a casualty, one day prior to such an occurrence. If the model gives the right answer, such as recommending that the ship not be hired, then the charterers can expect the model to be reliable.


    Operating a vessel at its optimum speed is difficult. Like automobiles, ships have an optimum speed (by design) and at the time of delivery of vessels, tests are conducted to determine the optimum speed for fuel consumption. Over time, the optimum speed for vessels changes due to a variety of factors such as engine wear and maintenance. It is very important for ship owners to always know the fuel consumption of their fleet at certain speeds. Ship operators can use analytics to determine the optimum speed, taking into considering such factors as bunker cost, freight rates and schedule.

    Apart from optimum speed, fuel consumption data can be used for cost-benefit analysis of vessel maintenance such as hull cleaning and propeller polishing. Traditionally, these types of decisions are based on intuition or a schedule rather than empirical evidence of a vessel’s performance. Data analytics can make it easier for operators to decide the timing and the benefits of performing maintenance at those times.

    In Figure 4, the normal curve shows speed and fuel consumption data from all voyages while the maintenance curve provides speed and fuel consumption data within certain days of the maintenance being performed. In this example, at 14.5 knots, there is a difference of 19 MT/day of fuel consumption before and after a certain type of maintenance. At current bunker prices ($350/MT), this translates into a difference of $450,000 for a single US West Coast-to-Arabian Gulf round trip voyage. If the total cost of maintenance and vessel downtime is less than $450,000, then it warrants the maintenance to be performed regularly.

    Figure 4: Speed and fuel consumption curve before and after maintenance.


    Data analytics also helps voyage partners access information in a more efficient manner. From time to time, terminal operators, voyage managers or port agents need to know certain information, such as a ship’s estimated time of arrival (ETA) and cargo information. Instead of relying on notes, emails or phone calls, they can track vessels using dashboards. This helps them make more effective decisions about terminal and berth allocation, cargo handling and route tracking. It also helps to improve situational awareness regarding the crew onboard as well as upcoming maintenance and inspections.

    Figure 5 shows the current position of a vessel’s voyage from Ras Tanura to Long Beach showing the ETA, cargo quantity and discharge window details. This information is even more valuable for short-haul voyages (e.g., US Domestic, inland barges, Black Sea, etc.) with shorter turnaround times.

    The voyage operations dashboard can also provide information about any deviations from optimum performance. The ideal route, the weather service-provided route and the actual route can be tracked as the voyage is underway rather than after the fact. Any operational changes to speed, ETA and other factors can also be managed in real time, thus ensuring that the voyage performs to its plan and remains profitable.

    Figure 5: Latest position of a vessel en route from Ras Tanura to Long Beach.


    Today’s companies in the marine transportation industry may not always fully utilize the power of the data at its disposal—data that is simple to collect, store and integrate. The use cases discussed in this article are just a few examples of how the marine transportation business can use sophisticated data analytics techniques to improve opportunities for business growth, competitive advantage and risk mitigation. The technologies that enable data integration, analytics and discovery have greatly matured in the last decade and offer a way to build a foundation for a long-term, sustainable and analytical approach to improve decision-making and ultimately, the business itself.

    The Author
    Kunal Bhal

    Kunal Bahl
    is a Senior Manager in Sapient Global Markets’ Midstream Practice based in San Francisco. He is focused on Marine Transportation and his recent assignments include leading a data integration and analytics program for an integrated oil company, process automation for another integrated oil company and power trading system integration for a regional transmission authority.

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