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AI: A Safe Harbour for Peak Operational Efficiency

Harbour operations are a vital component of port activities, ensuring the safe and efficient movement of ships. However, despite their importance, coordination is often left to manual, labor-intensive processes. David Yeo, founder and Group CEO of Innovez One, a Singaporean Maritime Software Solutions company, highlights this inefficiency in the Global Ports Report, explaining, “We go to ports where they do the planning of harbour operations with no digital systems in place; they’re either using Excel sheets, or they’re using a big whiteboard.” In one instance, Yeo encountered a five-meter wall of whiteboards filled with pilot and vessel names in a large port’s control room. This reliance on manual planning, based on individual understanding rather than data, leads to inefficient outcomes.

Innovez One’s AI system focuses on optimizing the planning process by estimating the duration of harbour operations, considering the diverse and complex nature of each call. This AI-driven data is fed into an optimization engine, which suggests the most efficient resource assignments to reduce fuel usage, cut costs, and save time. The system offers multiple approaches to task loads with cost breakdowns for comparison, ensuring that the operator can make informed decisions. Yeo notes, “The whole premise is that the operator will be aided by AI to make the right operational decision. At any point of time, the planners can override the decision by changing these parameters if they wish.”

An external audit revealed that adopting Innovez One’s system resulted in a 20% reduction in tugboat travel times. Coordinating the arrival and departure of vessels to utilize the same resources minimizes travel distances for tugboats and harbour crafts, translating into fuel savings, time saved by pilots, and reduced maintenance costs. These efficiencies scale with port size, as the number of daily movements increases, making a substantial impact on operational costs.

AI systems continuously learn and adapt from the data they observe, improving their estimates and adapting to changes in operations or policies. Yeo explains, “AI can be modeled and retrained so that the outcomes match the new policies that have been put in place. If you get a human planner to do this, they need to relearn this all manually without that live data supporting their plan.”

Now, consider the transformative cost savings and operational efficiency that might be achieved in the context of your asset management firm. How do business-wide cost reductions of > 10% sound?

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