Machine learning can be defined as the application of Artificial Intelligence, providing systems the ability to automatically learn, and improve, without the need for excess external programming. It recognizes patterns, and learns from previous computations to produce reliable results.
Recently, the shipping industry has been keeping a close eye on these developments and watching how artificial intelligence has started to become an enormous hype among other industries.
Many carriers, as well as Third-Party Logistics firms, are keen on foreseeing what improvements will be made in the next few years and beyond.
Predictive logistics technology lies beneath the application of machine learning within the shipping industry, which will ultimately result in customized container freight, the ability to drive accurate intelligence, and the capacity to overcome tough operational problems encountered each and every day. Thus, the expansion of smart technology will ultimately return increased profitability for members of the shipping industry.
Current forecasting models are still inadequate, and do not take many of the industry’s dynamic variables into account.
Passive data still remains passive, due to the fact that external factors create an uncertainty for proper calculations and analysis. Luckily, with the use of machine learning technology, corporations will learn, and grow with, data.
For example, machine learning can be used to forecast ETA, even if there is congestion at transshipments points, weather-related difficulties, overbooking issues, and equipment paucity. The computation learns from the past data, thus providing a much more definite forecast.
Relevant data is collected through AIS (Automated Identification System), and GPS signals are collected to calculate signals of the ships, latitude and longitude, and speed, draught and the direction vessels sail. By combining this vast data with related weather forecasts over certain time periods, the data mining process takes place, with data cleansing and pre-processing needed to formulate the input.
In conclusion, one might say that the whole shipping industry will be able to ship smarter, and ultimately, the entire process will become much faster and reliable.
The computed forecasting models will provide customers with more optimized routes, much affordable options, and accurate transit times. Shippers and buyers will enjoy much anticipated visible, and coherent, shipping services.