Machine Learning – How It Works in Transportation and Logistics

The utilization of machine learning and artificial intelligence has become increasingly popular in the field of transportation and logistics. With the surge of available data for companies to use both internally and externally, as well as the advancements in scalable computational power for large amounts of data processing, the possibilities for providing insights to enhance decision-making processes are endless. Today, many industry leaders are investing large amounts of resources into these technologies to optimize processes, improve human decision-making and predict future opportunities. Machine learning has the potential to help businesses better understand their customers’ wants and needs so they can provide the best possible experience.

Here are a few cases in which machine learning has helped in the space of transportation and logistics:

Demand Forecasting

Logistics providers can be much more productive and efficient when they can forecast future demand.  Whether it be detecting customers that will have freight to move or carriers that are willing and able to haul that freight, machine learning can aid in the decision-making process of which customers and carriers to reach out to.

Natural Language Processing

There are many use cases in which it is beneficial for computers to analyze and process the human language through various mediums, such as emails, phone calls and invoices. By automating this, companies can help their employees save time and effort in tedious tasks that would otherwise have to be performed manually.

Predicting Peak Times and Seasons

Similarly to how it is beneficial to understand who to contact, it is equally as beneficial to know when to contact them. Customers and carriers may only want to be contacted during certain periods of the year or times of the day. It is to a company’s advantage to gather, analyze and provide these insights to its employees using both historical and external data sources.