The shipping and logistics industry relies upon dozens of different documents to keep track of shipments and freight. But emerging technologies such as machine learning (ML) are going a long way toward improving and even automating decades-old processes. The result: improved efficiency and more accurate cost calculations. This leads to more accurate billing, which is good news for a shipper’s bottom line, especially when you multiply the impact across multiple freight loads and multiple weeks, months and years.
What is Machine Learning? How is ML Being Used by Shippers?
Machine learning is a technology that is related to artificial intelligence (AI) and it is particularly effective for identifying trends and patterns that humans would fail to notice. Machine learning is extremely versatile and this tech is used in a variety of different applications.
ML is unique in its ability to self-improve its algorithm over time. This means that software that leverages machine learning technology will become more and more efficient with every usage.
Shippers are using machine learning technology to automate freight bill creation, saving time and money, while also improving accuracy and efficiency.
How Do You Automate Freight Bill Creation With Machine Learning Tech?
A freight bill is another name for the invoice that is derived from the Bill of Lading or BOL. Automating freight bill creation can be a tremendous time-saver for shipping companies, primarily due to the nature of BOLs — the document from which freight bills are derived. A Bill of Lading is an unstructured document with no universal format. Processing BOLs can be time-consuming — especially when they are processed manually — and there is a high risk of errors and omissions. This has prompted many shippers to turn to technology such as machine learning to automate the process.
To understand how machine learning is transforming the freight bill creation process, it is important to understand the nature of the document that serves as its basis: the Bill of Lading. A BOL is a legal document that travels with the freight from origin to destination point. The BOL includes information such as the type of goods contained within the shipment, the quantity of each item, the shipment’s final destination and the costs that have been incurred during the most recent leg of the journey. At each stop along the way, this information is added to the Bill of Lading and an authorized representative at the location signs off on the document.
Once the freight reaches its final destination, the Bill of Lading — a rather “messy” document that has passed through many hands by this point — is used to create the freight bill. Until recent years, the industry standard has been to capture BOL data manually in an attempt to create an accurate freight bill invoice. As mentioned above, this process is highly prone to errors and omissions — a reality that has led to the development of a new application for machine learning technology.
Instead of burdening human resources with the freight bill creation process, software can be outfitted with data capture technology and machine learning algorithms that process the Bill of Lading. The BOL data is captured with a high degree of accuracy and speed.
Machine learning-powered data capture software can process unstructured data, such as that found on a Bill of Lading. This unstructured data would confound less advanced technologies, resulting in an output that requires extensive human review to ensure accuracy and consistency. But ML technology does quite well with unstructured data. It gets even better, though, because machine learning-powered data capture software can learn how to recognize and handle unstructured data, and then, it can repeat processes in a matter of seconds. Once all is said and done, the BOL information is captured and then relevant data is extracted to automate the creation of a freight bill.
This use of machine learning allows for a much lower cost to process this critical shipment information and you’re also freeing human resources to focus on more important, higher-level tasks. Many shippers are opting to outsource this process to third-party service providers, while others are choosing to implement new ML-powered software platforms in-house.
Improving and automating processes — especially those that are so closely tied to a company’s financials — can be a challenge. But the right technology partner can make all the difference. At iTech Data Services, our team is available to consult with your business, offering cost-effective, high-tech solutions that will boost your bottom line. We are experts in machine learning and its uses for freight bill automation. We invite you to reach out to iTech today to discuss the possibility of making machine learning part of your digital transformation strategy.