The new era in chartering
VesselBot leverages Spire’s satellite data, Big Data and Artificial Intelligence to provide the dry bulk shipping market with a digital platform that optimizes the chartering process.
VesselBot Case Study
VesselBot – The New Era in Chartering
Transporting the chartering brokerage into the 21st century
VesselBot is a leading technology company that leverages Spire’s satellite data, Big Data and Artificial Intelligence to provide the dry bulk shipping market with a digital platform that optimizes the chartering process. VesselBot’s platform digitizes the whole chartering process and enables the end-user to conclude a fixture in a more efficient and effective way while optimizing profitability.
Charterers, cargo owners, shippers, buyers, and ship owners needed a more efficient way to conclude a fixture because the current process was time-consuming and didn’t provide enough market visibility.
Securing proposals required screening hundreds of emails and telephone calls, as well as integrating data from multiple emails, Skype, and WhatsApp accounts. As the market changes, new tonnage that might be available and a good fit for cargo could fluctuate in availability. Keeping up to speed with these changes was impossible and resulted in missed cargo and open tonnage opportunities.
VesselBot’s platform solves this challenge by offering the dry cargo sector advanced technologies that match charters in seconds.
VesselBot’s platform layers satellite and terrestrial bulk data via Spire Maritime’s Vessels API with other data to provide up-to-date results allowing users to optimize their cargo and vessel assignments globally. By merging Spire’s AIS data, port information, bunker, cargo, route, and distance data VesselBot eliminates data silos to provide near real-time market visibility. Chartering department professionals can now make more accurate, data-driven decisions with minimal effort and without having to manage mundane, non-value adding tasks.
Increase in operational efficiencies
Average transportation cost-saving
We compared the manual process vs our digital selection tool using the following real cargo example:
Cargo looking for a charter:
- Cargo: 60.000mt +/-10 MOLOO Grains
- Loading/discharging port: Santos/Spore Jpn Rng
- Lay can period: 31-4 October
- Loading Rate: 30.000 MT SHINC WWD
- Discharging Rate: 25.000 per SHINC WWD
- Freight Idea: USD41-42 pmt//
Our state-of-the-art technology, maps available ships using, among others AIS data and develops new optimal matches.
How VesselBot matches this cargo to a vessel:
The algorithm produces 75 optimal vessel matches based upon the following calculations.
- Screened ~11.000 combinations of active vessels in the Dry Bulk segment with more than 10.000 DWT capacity
- More than 100 different parameters were taken into consideration
- Number of calculations done to reach the 75 matches: 163.200 calculations
- For the 75 matches, 134.679 calculations generated a ranking and provided optimal vessels
- In just a few seconds, the entire Dry Bulk fleet was processed (~11.000 vessels)