AI in Maritime Industry: How Artificial Intelligence Solutions Benefit the Shipping Sector

The shipping sector is a vital part of the global economy, responsible for transporting goods and materials to and from different parts of the world. It is a complex and challenging environment, where even minor improvements can result in significant benefits. In order to stay competitive, it is essential for businesses in the shipping sector to invest in artificial intelligence solutions. AI can help companies automate tasks, optimize operations, and make better decisions.

In this article, we will explore how AI can be used in the maritime industry and how it benefits the shipping sector.

How AI Is Changing the Maritime Industry

The presence of AI in the logistics sector has been increasingly visible in recent years. Just like in the case of manufacturing, the potential of artificial intelligence in this field is impressive. AI-based solutions can streamline overland transport, but it also finds applications in the maritime sector.

Shipping goods is a fundamental aspect of the globalized economy, and growing customers’ expectations worldwide enforce constant optimization in this field. In a nutshell, AI is changing the face of the maritime industry in three particular ways — by providing partial autonomy to the automatized units, evaluating processes and optimizing them, and forecasting future trends. Taking advantage of all these three opportunities is a way to outperform the competition and reach sustainability goals.

AI Use Cases in Maritime

From forecasting to equipment automation — AI’s applications in the shipping sector are vast. Let’s take a closer look at how AI is revolutionizing the maritime industry based on particular use cases.

Planning Shipment of Containers — Predictive Scheduling

Predictive analytics enables shipping companies to optimize their vessel scheduling. They use the port calls data like destination, arrival time, trajectory, and trip duration provided by the port community systems to manage their trips most efficiently. Using the data on the vessel traffic, the carriers schedule and reschedule arrivals to avoid delays and downtimes. Machine learning helps them deal with the unpredicted scenarios caused by emergencies and enforced route changes. Since vessel scheduling predictions depend on many input variables, ML is the best way to handle it, contrary to the traditional, rule-dependent algorithms.

Predictive modeling enables production and distribution optimization through better throughput, quality, safety, and yield improvements. The end-to-end custom implementation of a solution that interprets data provides visualization and enables custom automated actions to streamline logistics and supply chain networks is essential to take SCM to the next level.

Related case study: Developing a logistics platform offering real-time visibility and integrations with different carriers

One of our clients was seeking to improve the global supply chain optimization product

Our challenge? Providing visibility and data transmission for maximum efficiency and control. We supported solution development for end-to-end execution of logistics activities in Supply Chain Management at the PO/SKU level, including PO creation, stock management, suppliers and distributors management, consolidation and load planning, carrier allocation, documentation, and final delivery.

Read more about this case study.

Organizing Containers Positioning

As we’ve mentioned, granting partial autonomy to automated robotic equipment is one of the core functions of AI in the maritime industry. AI-fuelled machinery can optimize the container positioning to make the best use of the available space.

The machines position the containers using computer vision, making autonomous decisions after learning through unsupervised methods. What does it look like in practice? Without getting into details — the monitoring device transfers an image to the interpreting device that classifies the container recognizing such variables as size and shape. Then it evaluates the existing storage configuration to identify the most appropriate space for the new container.

Aside from that, it can detect wrongly positioned containers based on already identified patterns and rearrange them. Depending on the carrier’s preference, these operations can be either supervised or autonomous.

Voyage Planning and Route Forecasting

Route forecasting based on real-time data enables companies to optimize their routes depending on variables like weather and react to unexpected events. The 2021 incident in the Suez Canal has shown how critical are these forecasting models to the shipping sector — with the most frequented maritime transport route entirely blocked, the shipping companies had to improvise, searching for the shortest and most time-effective alternatives. AI technology could provide them with fast estimations.

On the other hand, the Covid-19 pandemic has proven that even though the sector has already gone through digital transformation, it requires further innovations in terms of route forecasting. To create the optimized route, the AI algorithms need to consider the changing variables, including wave frequency, tides, and winds. With a significantly reduced number of ships circulating globally, the data collection capabilities have lowered, resulting in inaccurate forecasts.

That made the industry rethink its data collection methods. Relying on ships isn’t the most effective strategy for collecting data — on the other hand, the satellites, which are a relatively stable data source, do not provide high accuracy. Improving it while keeping the data influx independent of the market fluctuations is possible with maritime data buoys and virtual buoys that are becoming increasingly common globally.

Optimizing Fuel Consumption and Emissions Reduction

Road transport is responsible for the majority of CO2 emissions in the logistic sector, but the shipping’s share has been exponentially increasing in the last decade. Considering the dynamic growth of e-commerce, the demand for global maritime transport will be rising — thus, we need AI solutions facilitating the ship’s carbon print reduction, such as route forecasting involving fuel consumption factors. Aside from lowering the emissions, they can help the carriers reduce their polluting impact. In order to achieve these goals, many shipping companies migrate from linear to circular supply chain structures using artificial intelligence.

Autonomous Ships and Port Operations

Machine learning algorithms can generate moves of the automated machinery, enabling partial autonomy of the units like ships or ports. That makes them less susceptible to human errors and reduces workforce demand, cutting costs as a result. Automatized cargo processes are also faster, enabling the carriers to save a lot of precious time. The shipping companies can automatize the container vehicles, cranes, and other elements that manage the cargo.

Automatized vehicles are a stepping stone for the whole logistics sector, and the maritime industry is not an exception. The ports worldwide have already started using such equipment within their facilities to load and offload cargo and distribute the containers most effectively. China is a precursor of these changes. According to ReportLinker’s Port Automated driving report 2021, the country is expected to introduce up to 7000 autonomous container trucks in its ports by 2025.

Since the conditions in the ports are rather stable, automating self-driving vehicles is relatively easy. Contrarily to the regular roads, there is no unpredictable traffic in ports. At the same time, in such controlled conditions, the computer vision solutions that fuel automated vehicles deal with a limited set of predictable elements, which speeds up the machine learning processes.

Autonomous shipping is another rapidly developing field in this context. The self-driving autonomous ship control systems reduce the likelihood of human error — the most common cause of safety alerts and accidents. With the meteorological, oceanographic, satellite, and proximity sensor-derived data, the self-aware machines handle the navigation, supporting the ship crew in decision-making.

Predictive Maintenance

Just like in the case of the manufacturing industry, the shipping companies and port management companies use machine learning algorithms for the purposes of predictive maintenance. The AI allows them to identify machinery issues before they escalate, causing downtimes and affecting the whole supply chain.

Predictive maintenance is crucial for the ships themselves due to the nature of the shipping industry. The standard Asia-Europe shipping route via Panama Canal takes at least 22 days, and during this time, access to maintenance support may be limited. AI-based predictive maintenance can identify the issues before the route launch, saving the shipping company expenses. Regularly scheduled maintenance is not an effective method for such large systems being subject to inspection. Artificial intelligence allows the carriers to react right on time instead of relying on traditional preventive measures and therefore extend machine life.

Dynamic Pricing for the Shipping Industry

Dynamic pricing is not a new concept, but the shipping industry is still far from fully embracing it. However, the decreasing predictability of the market makes the idea receive an increasing amount of attention globally.

The dynamic pricing algorithms estimate the revenue-optimal price using the demand function, built based on historical data. Contrary to the traditional equations, the dynamic model should also incorporate the fluctuating market tendencies, including such aspects as:

• vessel capacity
• fuel prices
• sales peaks
• supply-chain delays

How dynamic pricing strategy works? The model estimates how changing variables impact the price and possible demand for profit maximization.

The model can process dozens of price-influencing factors at once — their choice depends on the frequency of the repricing processes and the available data. Working continuously, the dynamic pricing algorithm evaluates the results of the most recent repricing and adjusts the processes in the following cycle. This way, instead of setting up a fixed rate every season, the carriers can update the prices even a few times per day.

To dig deeper into these subjects, we recommend our article on dynamic pricing in logistics with recommendations on building a dynamic pricing strategy with artificial intelligence.

Demand Predictions

Considering how complicated is the structure of the maritime supply chains and how long it takes to ship goods, every mistake is very costly. Since standard transport routes are counted in days or even weeks, it’s impossible to react to real-time changes in demand the same way the overland transports responds to them. In shipping, it’s essential to plan ahead — and predictive algorithms are perfect for that purpose.

Predicting sales based on past patterns in demand to optimize the production and transportation processes.

The recent events have reminded the shipping companies that radical changes in demand can be rapid. Only accurate predictions can save them from their financial and operational consequences. Depending on the past data they have at their disposal and the expected accuracy, they can use regression or clustering analysis methods for that purpose.

Streamlining Backoffice Operations with NLP

AI enables autonomous shipping and other impressive innovations, but let’s not forget about what happens behind the scenes — it’s equally important! With NLP (natural language processing), the companies can streamline invoice management by automatizing the information collection, generation of documents, or introducing digital assistants. You can delve into this particular topic with our article on NLP in Logistics!

The Benefits of Using AI in the Shipping Industry

The globalized world depends heavily on the shipping industry today — thus, the optimization of its processes is crucial for the economies. As you can see, based on the use cases listed above, artificial intelligence can have a positive impact on all the stages of the shipping process — from containers distribution planning, through route forecasting, to unloading in ports.

In fact, all the sides get something out of the AI’s implementation. The shipping carriers and freight forwarders:

• improve the cost-effectiveness and productivity of their processes with advanced planning and scheduling tools
• adjust to the changing market realities, fluctuating demand, and unpredicted events with dynamic pricing and route forecasting algorithms
• calculate the most efficient route in terms of fuel consumption
• increase safety with predictive maintenance and automated responses
• maximize the usage of ship’s capacity with computer-vision fuelled positioning systems

AI benefits for shipping carriers, logistics providers, and freight forwarders

The port operators, on the other hand:

• streamline their loading and offloading operations with the support of AI-managed cranes and other equipment
• reduce the costs of hiring and training additional staff with robotics and automatic vehicles
• reduce the risk of human errors with automated planning, positioning, and calculation
• speed up the port scheduling and rescheduling with automation tools
• automatically scan and separate the damaged shipments

AI benefits for port operators

All these improvements translate into benefits for the clients, which receive their orders faster and in a perfect state. Since both carriers and port operators save with the AI, the customers can also enjoy better pricing.

Why Is It Important to Invest in Machine Learning Solutions?

The business benefits of artificial intelligence in the maritime industry are undeniable, fuelling business growth and making space for development and new investments. By giving up these technologies, the companies lose their advantage over the competition that will be embracing AI in the nearest future. So, from the business perspective, implementing them is a fundamental prerequisite for survival in the future market.

However, that’s just one side of the coin. On the other hand, we have environmental issues that AI technology can also solve. The world is not very likely to step away from globalized trade — thus, it’s essential to make the shipping industry greener. Its significant impact on the environment due to CO2 emissions and pollution can be reduced with machine learning algorithms that suggest the most sustainable usage of resources, the least fuel-consuming routes, and automated container configuration that brings space management to the next level. That makes AI even more promising in the context of the maritime industry. No wonder that the International Maritime Organization, responsible for preventing marine and atmospheric pollution by ships, contributes to promoting autonomous ships and other AI-fuelled solutions.

Are you interested in implementing AI technology for your business? We’d love to help you with that. nexocode is a leading provider of software solutions for the logistics industry with a portfolio including artificial intelligence-powered fleet management software, intelligent route optimization solutions, dynamic pricing models, real-time monitoring systems for transportation providers, and other cutting-edge technologies explicitly designed for this sector. Just reach out to us, and we’ll address all your questions.

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Originally published at https://nexocode.com on March 13, 2022.