Reducing Demurrage Fees with Higher Accuracy Container ETAs

Container detention and demurrage fees are a frustrating cost for shippers. Over the past few years, average fees reached record levels, prompting more interest in how technology can be used to help reduce these costs. Real-time transportation visibility now offers shippers more consistent, accurate and reliable berth ETAs for containers, unlocking significant benefits.

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What is demurrage?

Demurrage is a fee that shippers and Beneficial Cargo Owners (BCOs) are charged when a container has been sitting at the terminal beyond the cost-free time period. For conventional shipping, the cost-free window is usually around 3-5 days after the container has been discharged from the vessel. After that, shippers are charged for each day until the container is picked up and gated from the terminal.

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Boosting profitability by reducing extra freight costs

Demurrage is a common frustration for many of our customers, and fees can quickly spiral out of control, especially at some of the world’s busiest ports (see graph below). Reducing these extra costs requires new levels of ETA accuracy, to allow better planning for container pick-ups, minimizing demurrage.

Increasing supply chain efficiency by improving planning

A primary requirement of supply chains is to ensure the optimal flow of goods so that the right inventory levels are maintained in warehouses, DCs and stores. Where a shipper's supply chain relies on ocean moves, it is critical to focus on the following two areas:

1. Plan container collection in advance to ensure on-time pickup
2. Carefully manage warehouse storage space to avoid capacity problems

Accurate and reliable ocean freight ETAs are critical enablers of success when carrying out these tasks.

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Elevating customer satisfaction levels by increasing visibility

In order to offer a greater delivery experience to end customers, shippers and BCOs need to offer proactive alerts about delays and exceptions. Visibility data should also be shared among internal teams to make it easier for them to collaborate and mitigate the risk and impact of any delays. The more information customers have, the more in control they feel, which directly impacts their overall satisfaction.

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Challenges to Accurately Predict an ETA in Ocean Freight Transportation

The ocean freight industry faces a number of challenges impacting supply chain operations around the world. From blank sailings to vessel slow steaming and port congestion, these factors introduce a lot of uncertainty and make it difficult to obtain accurate ETA predictions.
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Ocean freight challenges

Blank and skipped sailings

When demand for sailings reduces, such as when inflation is high in the world’s economies, it’s common for ocean carriers to cancel scheduled sailings at short notice to consolidate and optimize vessel capacity. There is also a seasonality to some blank and skipped sailings, where regional holiday celebrations, such as ‘Golden Week’ in Japan or the Chinese New Year, cause last minute scheduling changes due to such things as personnel shortages.

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Higher intermediate stops for vessels

A higher number of intermediate stops, which determine where and how often a ship stops along a shipping route before a container reaches its port of discharge, also creates uncertainty, since each stop could expose the vessel to delays due to port congestion and operational slowdowns.

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Vessel slow steaming

In general, ocean carriers try to minimize the fuel consumption of their vessels. By reducing speeds, cargo ships can consume less fuel. It’s a practice that has been adopted by many shipping companies to cut costs. It also helps carriers meet tougher environmental regulations. For example the carbon intensity index (CII) calculation for a ship is based on its annual fuel consumption, which now applies to all ocean carriers since launching in January this year. Speed changes result in longer transit times compared to those planned, increasing uncertainty and making it more difficult to predict accurate vessel ETAs.

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Maritime route congestion

Longer transit time and delays can be observed on specific lanes, caused by global freight volume growth and limited route capacity. A good example of this is the Evergreen incident in the Suez Canal, which had significant impacts on maritime routes across the globe.

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Port congestion

Caused by a range of factors, such as increased demand, labor disputes, and infrastructure limitations, port congestion has become a significant challenge for businesses that rely on ocean freight. This can result in delays, demurrage charges, and increased costs, as well as a range of other operational challenges.

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Ocean data quality

Ocean carrier and network data underpins real-time and predictive visibility. For this reason, improving data quality remains a key focus for the industry. Data that is complete, accurate, and available in a timely manner, is essential for offering best-in-class visibility. Common data quality issues faced include:

• Missing container milestones: These are generally categorized as gate or load/discharge events, captured in every containerized global supply chain. When these milestones are missing it becomes challenging to track a container.
• Inaccurate or incomplete container transport route: This refers to the sequence of ports and vessels involved in the container’s journey. An example of incomplete or inaccurate data could include when an ocean carrier fails to communicate which vessel will offload a shipment at its port of discharge.
• Missing or inaccurate vessel schedules: Also known as shipping schedules, these provide information about the planned arrivals and departures of vessels at various ports along their route. They outline the itinerary of a specific vessel, and are published and updated by ocean carriers. Unfortunately, planned arrivals are often inaccurate when compared to actual arrivals, and sometimes carriers fail to publish  schedules for their own fleet resulting in sometimes contradictory itineraries for the same vessel.

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Boosting Accuracy with Shippeo’s Proprietary ETA

The performance of ocean shipment ETAs has advanced significantly thanks to considerable investments we’ve made in the development of new machine learning algorithms. As a result, shippers and BCOs are seeing improved accuracy and reliability of ETAs of their cargo shipments, leading to savings, efficiency and predictability for their operations.

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We recently made a new version of our predictive berth ETA available to all our customers and we believe this will be a game changer for the ocean shipping industry. Our models provide predictive ETA for both vessels and containers, giving our users clarity about a vessel’s arrival at the berth of discharge. The reliability of these predictions gives users greater confidence when making decisions on their operations and at the same time have a significant positive impact on customer satisfaction.

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By drawing on Shippeo’s multi-year research and development program on ETA algorithms and geofencing technology, our predictions outperform what you typically receive from ocean carriers by significant margins. Using advanced algorithms and machine learning technology, our models are currently combining historical data with a wide range of live data, collected through the continuous tracking of around 5000 vessels and 3000 ports and terminals. Data collected includes shipment data, container visibility data, vessel schedules, AIS satellite data and terrestrial positions.

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Avoiding the visibility lag when vessels arrive at port

Shippeo also offers users ETA information in a more consistent way. Some carriers offer ETAs that refer to the moment a ship arrives within the vicinity of a port, rather than the moment a vessel is berthed and ready for unloading. The problem with this is that some ships don’t berth straight away, creating uncertainty and a potential visibility lag of up to 3-4 days. It’s especially important to distinguish between them at busy ports where there can be significant congestion. Shippeo’s ETA always refers to arrival at berth, so users know the vessel is starting the cargo-handling process.

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Historical data collected is combined with machine learning algorithms to overcome the uncertainties mentioned above to make accurate predictions. Examples of data intelligence leveraged to increase accuracy includes:

• Detection of blank sailings to identify vessel rotations impacted and build the right representation of a vessel's itinerary and its intermediate stops.
• Detection of vessel slow steaming by building a historical profile on various metrics for each vessel

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• Detection of route congestion by monitoring the evolution of route transit times and distances over time. Shippeo has put in place advanced algorithms to build maritime route intelligence, grouping all vessel movements.

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• Calculation of real-time and predictive port congestion levels to accurately predict an ETA at the berth of discharge. The ability to evaluate in real-time dwell and waiting times, as well as the total volume of vessels currently at berth and at anchorage, for every port and berth in the world, is key for identifying unexpected events impacting port operations.

Summary

With all of these innovative uses of real-time visibility data and data science for better quality insights on ocean freight, it’s easy to see how Shippeo offers organizations significant benefits. We estimate that more than 90% of our ETA predictions will help Shippers and BCOs stay within their ‘cost-free’ window, allowing them to reduce demurrage fees. And of course, beyond lower demurrage and detention, the higher caliber visibility data improves planning abilities and has a positive impact on customer satisfaction.