Request demo

How can Artificial Intelligence support your berth planning?

Share on

New sophisticated Artificial Intelligence (AI) berth planning tools are finding their way to market. Many in the maritime industry ask how they can trust a complex algorithm to help make their hardest and most important decisions. In our work to improve core planning processes in liner shipping with AI, we have developed a method to help limit the uncertainty about new technologies and take control of the process. This article explains how.

AI berth planning
Photo by kinsey on Unsplash

Artificial Intelligence in berth planning

Every day, experienced carrier and terminal planners make tough decisions to move around ships, containers, and cranes when plans are adjusted. Such decisions often have thousands of possible outcomes. In addition, picking the optimal solution is virtually impossible. Planners therefore spend a great deal of time manually calculating and comparing different scenarios. As one vessel operator explained, “I feel like a calculator, and there is never enough time to evaluate all options.”

Fortunately, recent advances in processing power and algorithms have made it possible to identify the best solutions among all possible options. With its track record of tackling some of the hardest analytical challenges known to man, Artificial Intelligence — computer’s mimicking and vastly outperforming human problem-solving capabilities — is particularly well suited for this task. For instance, our AI-powered Berth Optimization Engine can assign key terminal resources to increase the utilization and handling capacity of a typical sea port. Usually, by a 5–10% without any new investment.

The industry has been remarkably accommodating to digital innovation in recent years. Just think about the rise of terminal automation. Digital incubators like Ze Box (CMA CGM) and unboXed (PSA), or the Maersk/IBM blockchain joint venture. Yet, it is usual for new AI planning software to be met with some initial suspicion.

What many find worrying about technologies like AI is the apparent lack of user control. Planning processes may be manual today, but they are guided by the experience and human ingenuity of the planner, who can step in at any time to resolve unexpected problems. This provides a lot of control and transparency for the user, while the sophistication of AI is such that the human mind cannot aspire to trace all steps in the computation process.

AI berth planning
Photo by chuttersnap on Unsplash

Future AI implications in berth planning

There can seem to be a trade-off between control and AI in industrial planning tools. In fact, the limited usability of first-generation AI models has held back their application in other industries such as health care and finance. However, when done right, AI can significantly increase the power of an organization to control and deploy its resources effectively. The key question is whether the algorithms are used only to speed up an existing process — think of a hyper-powerful calculator — or if they provide such options to configure the input parameters and choose between different scenarios. This way, the user remains in charge of the process and gains entirely new decision-making abilities.

Coming back to the main question, there are two criteria one can use in deciding whether to trust a particular Artificial Intelligence berth planning tool:

  • Performance: Does it allow to execute your planning tasks with better results and faster?
  • User-friendliness: Is it easy to use with configurable inputs, clear outputs, and ability to tweak the plan in any way you see fit?

If both criteria are satisfied, the new instrument will not only improve the KPIs of the organization. It will also empower the planning team to help shape business priorities very hard to influence before. To use our Berth Optimization Engine as an example, the performance of the algorithm depends on its ability to allocate terminal assets. Moreover, its user-friendliness is determined by the planner’s ability to manipulate, monitor, and interpret key elements in the process. For example, planners can select specific optimization priorities that have not been available before. As such, improve the competitive position and customer service of the port.

Steps to successful implementation

It may not be obvious whether a given Artificial Intelligence berth planning tool meets these criteria. We have developed a simple 4-step approach to help evaluate the promises of new, intelligent software products. Here’s how to do it:

1. Define your metrics for Artificial Intelligence berth planning:

First, you should decide exactly what you want to improve in the organization. It could be KPIs such as labor utilization or vessel idle time. It could also be something more immediate like the time it takes a planner to manually revise the full plan. Specifying your objectives at a granular level initially will help you make an informed decision later.

2. Set a baseline:

To understand if the new software results in an improvement in your metrics, it is helpful to first define their pre-trial level. In today’s data-driven operations, carriers and terminal operators can quickly tease out most KPIs of interest, such as fleet utilization in a specific trade lane or service. You may also ask the software vendor to help you establish the baseline. For example, we offer our terminal customers a diagnostic free of charge.  It determines the current level of their operational KPIs and estimates the probable impact of our collaboration.

3. Take control: 

Once you start testing the software application, you can immediately begin to explore its user-friendliness. Like an airplane needs a pilot, a terminal or service lane requires an operator to make the last call when ranking important operational goals. Verify that the tool lets you select and rank multiple objectives for each optimization cycle. As well, that you can assess the feasibility of each optimized solution before you implement it in the plan. It is not enough that a plan optimizes your KPIs, it also needs to work under real-life operational constraints. Relevant quality metrics include the number of changes committed and any violations detected.

4. Evaluate the implementation of Artificial Intelligence berth planning: 

Finally, sit back and review the results of the trial. To evaluate whether you should trust your new AI decision support tool, go back to your baseline. Check if the trial resulted in an operationally meaningful improvement in your metrics. Confirm that the tool gives you the ability to select its objectives, assess its recommendations, and even control elements of the operational environment that were not accessible before.

Industrial planning tools that use AI to augment live decision-making may be hard to trust at first. But if you break down their promises and evaluate their performance and user-friendliness, they can result in big operational improvements. To learn more about how Portchain uses AI to help container carriers and marine terminals take planning operations to the next level, please contact us.

Previously published on Medium

Subscribe to PDF