How can we develop an application that replaces the current manual way of consolidating information from different sources; and instead, streamline information to provide insights and support commercial decisions on vessel deployment?

BACKGROUND

Many of the data gathering and information processing activities needed to make trade decisions today are highly manual in the maritime industry. Not all data is available publicly, and the team has to rely on their networks to get the desired information/data, which may not be entirely accurate/updated. At the same time, the recommendations are usually subjective and depend on different members’ assessment of the current situation.

The ”ideal state” would be to have an automated decision recommendation engine, that takes into account past relationships across the different variables and expected future trends/trajectories to predict trade activity for a specific commodity.

REQUIREMENTS

It is currently difficult and time-consuming for the commercial teams to obtain the information needed to decide on where and how to deploy our vessels. This is because the decision is dependent on many factors – including, but not limited to, demand and supply situation, as well as the current positioning of vessels and committed contracts. The information is obtained through a mix of both public and informal data sources.

The assessments by individual members also tend to be highly subjective (50% experience, 50% gut feel), and there is no way to consistently replicate the decision-making process. This also results in potential loss of revenue as sub-optimal decisions may be made. The solution must include automation of the data gathering process, machine learning models that predict future trade activities for a specific commodity and a recommendation engine that suggests the various options for vessel deployment.

The minimum viable product (MVP) should be able to:

• Provide a platform that provides options to maximise the revenues based on forecasted demand-supply imbalances in the next 60-120 days for major locations and commodities, and freight rates trends.
• Demonstrate the automated data gathering process for publicly available data.

DESIRED OUTCOME

This will be a collaboration between IMC and the startup and primarily for use in-house. We will provide guidance to the selected startup on the factors and different scenarios to consider, as well as the data needed to make the decisions to build the machine learning model. Hence, the Intellectual Property (IP) of the model will belong to IMC. If the paid pilot is successful, we do not preclude the possibility of setting up a joint venture (JV) with the startup to commercialise the product to other companies