Machine learning (ML) is an application of Artificial Intelligence (AI). It can be looked at as a subdivision of AI. In ML, software algorithms improve automatically as they complete tasks. In simpler words the machine is “learning” on its own as it completes tasks.

In order for Machine Learning to be useful, there should be large and preferably growing sets of data to learn from. The more “practice” that the ML algorithms have and the more “data” that the ML has to learn from, the better it can help. ML has several applications in the shipping industry. Let’s explore a few ways in which it is already changing the industry:

More Predictable Shipping Rates and Times

Machine Learning is enabling shipping software to make predictions off of historical and current supply chain data. For example, it is helping forecast future demand and prices with greater accuracy than technology without ML can. This is resulting in shipping software companies and companies that send out a lot of shipments saving both time and money. When it comes to carriers, ML is helping them improve their capacity utilization. This is resulting in more consistent carrier rates.

Faster Delivery

Forecasting models made possible through ML are affecting delivery speed. This is because the models are helping carriers and shippers have better shipment route optimization and choose the right methods of delivery. The result of this is faster and more reliable delivery. This is a trend that has already proven itself to be true over the last few decades and the trend will continue over the next few years propelled by ML.

Decision Making

ML is helping humans make more insightful decisions; however, it is valuable to know that ML is also reducing the amount of decisions that humans have to make. ML is able to reduce the amount of manual decisions that carriers and shippers need to make. This is especially true for decisions that require the manual and tedious analysis of large sets of data. ML’s ability to help with decision making is leading companies in the shipping industry to save time, money, and improve the productivity of their workers. Workers can now prioritize their time on more creative and innovative tasks.

Software Health

Machine Learning (along with Artificial Intelligence) is helping alert developers to any abnormalities or errors happening within their organization’s software faster than the same errors were noticed before ML. This is advantageous to the entire industry. Shipping schedule errors, route errors and code-specific shipping software errors are reducing. The result is a higher quality level of service to many types of customers within the industry.

Summary – Get the Right Multi-Carrier Shipping Software

Machine Learning is without a doubt playing an increasingly important role within the shipping and logistics industries. Always remember that ML is implemented in the backend of a technology stack which means that the average customer is typically not made aware that ML is being used in a given software. Just because the nature of ML is such that it can’t be seen easily does not mean that it isn’t being implemented. Organizations within the shipping industry who do not implement ML technology at all over the coming years will place themselves at a competitive disadvantage to those who do.