The term artificial intelligence or ‘AI’ has become quite the buzzword in many industries worldwide, the maritime industry included. However, this is understandable given the sheer volume of data that is now part of our personal and professional lives. This increased interest in the potential of AI comes as a result of the introduction of various regulations like the Carbon Intensity Indicator (CII) and Energy Efficiency Existing Ship Index (EEXI), along with the global drive for greater efficiency in supply chains and stream of sanctions.
These factors and more have prompted the shipping sector to search for ways to quickly process the sea of data at their disposal and turn them into invaluable insights that enable smarter decision-making. Today, there are many current and planned applications for AI across the maritime industry, with one of the most important being AI-based route optimization.
An Overview of the Benefits of AI-Based Route Optimization
The maritime sector is one of many industries that heavily rely on humans for work processing and operations and the decision-making that sets them in motion. With the adoption of AI, shipping companies are more empowered to change their traditional business practices and wholly embrace digitalization, which ultimately improves and streamlines many of their processes, like route planning and real-time navigation.
Predictive AI is particularly indispensable in this regard and is already increasing in use across the industry as it can analyze large amounts of industry data and allow businesses to better identify risks that humans may overlook and predict the likelihood of future events, weather forecasts, incidents, and sustainability scoring for CII purposes.
The forecasting ability of predictive analytics is also complemented by prescriptive analytics, which can provide clearer and more attuned guidance regarding certain decisions and actions, primarily in supporting improved route planning based on environmental performance and efficiency.
Top Challenges of Implementing AI-Based Shipping Route Optimization
Although there is much that AI brings to the table concerning shipping and other logistics operations at sea, the maritime industry still struggles when facing big data for analysis and implementing it to enhance work productivity. Not only that, but many of the professionals in this sector have limited to no knowledge of what AI can and cannot do, causing the inaccurate and overabundant use of the term, which often results in challenges in implementing its applications like shipping route optimization. Nonetheless, putting this capability into practice in the real world comes with more objective hurdles, such as:
1. Initial investment and implementation challenges
AI systems require considerable computational resources to perform their calculations and will warrant a high initial investment in software development, technology acquisition, and employee training. Such a steep upfront cost will inevitably pose a barrier for most businesses, especially as more pressing matters like meeting sustainability goals may take precedence.
In addition, integrating AI systems into existing workflows and infrastructure will require a new set of technical expertise. It may also face resistance from workers (particularly those who are not tech-savvy) who may not be too keen on adopting new technologies and get the wrong idea that they are being replaced. Overcoming these challenges inherently requires stakeholder engagement, careful planning, and effective change management strategies.
2. Data size and data quality
AI systems need enormous amounts of historical data to learn valuable insights. Not only that, the quality of this data should also be top of the line, as inconsistent, incorrect, or incomplete data will naturally lead to inaccurate solutions.
3. Problem complexity and need to learn
Ship routing can be a very complex optimization problem given the plethora of real-world constraints that must be taken into account, such as weather, ship capacity, and the occasional traffic in shipping routes due to bottlenecks. This complexity may, at times, limit the effectiveness of the AI models employed. Still, as the technology improves and becomes increasingly better at solving more difficult cases, AI route optimization is sure to hold much promise for future evolutions.
Apart from complexity, route optimization systems also involve a deep understanding of complex AI technologies and how to manage them, something that requires external expertise or specialized knowledge. In the long term, training employees on AI concepts will be necessary to effectively utilize their vessels’ AI systems and get used to new workflows. Fostering a culture of upskilling and overcoming the learning curve is crucial to the success of implementing and utilizing any sort of AI route optimization system.
4. Trade-offs between computation time and solution quality
A common misconception regarding AI algorithms is that they can output the desired solution in a snap. The reality is that AI systems have to balance computation time with solution quality, especially in real-time applications. Hence, there can be times when the AI algorithm cannot find the best and most optimal solution within a certain time frame.
5. Privacy and security concerns
As mentioned, AI systems like those powering route optimization systems rely on collecting and analyzing extensive amounts of data, which almost always raises concerns about data privacy, security, and protection. Thus, businesses must further ensure the robustness of their data protection measures to safeguard sensitive information and deter the increasing number of maritime cyber-attacks.
Conclusion
AI-based systems like route optimization remain potent tools for the maritime industry and other sectors looking to enhance their logistics operations despite the challenges involved in their implementation. As development continues, these applications and use cases will come fully online and hopefully become the norm across all maritime operations. But for now and well into the foreseeable future, human expertise and knowledge will still play a crucial role in maritime AI because of the need to build and program it in the first place.
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