Revolutionize Your Logistics Operations with ML-Powered Freight Invoice Processing

Machine Learning technology — better known as ML — has seen a dramatic rise in popularity in recent years as more and more logistics companies pursue Digital Transformation projects to boost efficiency and bolster their bottom lines. Over time, machine learning improves efficiency and overall performance as ML allows a platform to “learn” and improve using complex algorithms.

When machine learning is used with freight invoice processing software, a third-party logistics (3PL) business or shipping company can see improved productivity, efficiency, and profitability. This is achieved through more accurate freight invoice processing, which, in turn, leads to greater efficiency in the actual shipping and logistics accounting operations.

The Importance of Freight Invoice Processing for 3PL Companies

Freight invoice processing is essential for ensuring success in the shipping and logistics industry. Companies are investing fairly aggressively in machine learning-powered freight invoice processing software. This is because freight bills must be processed accurately and promptly to keep a company’s operations moving forward. Freight invoices contain a variety of information to correct, including:

• Invoice number, PRO number, and other numbers used for tracking shipments
• Bill of Lading identification code or number
• Freight load description
• Freight weight, quantity, and size
• Origin and destination information
• Transport methods and transfer info
• Delivery information and instructions
• Freight transport costs
• Fees and ancillary expenses incurred over the course of the transport

The information contained within a freight bill is mission-critical, which is true in an operational sense and for proper accounting.

Freight invoice processing and auditing processes are critical for verifying the accuracy of a third-party logistics and freight transport company’s billing and accounting processes. Notably, a freight bill contains virtually all the same information you’ll find on a Bill of Lading (BOL). The key differential is the formality of the documents. A freight invoice is an essential but informal document, whereas a Bill of Lading is a more official, formal document.

Companies invest significant time and effort in developing these documents and performing related processes such as freight invoice auditing and freight bill processing. Speed and accuracy are essential to success, and this justifies an investment in ML-powered software for freight invoice processing for most companies.

Traditional Freight Invoice Processing Methods and Challenges

Traditionally, companies used manual freight invoice processing methods — inherently flawed methods with in-built inefficiencies that are virtually impossible to eliminate. Staff are tasked with collecting the relevant information and data, which is then manually entered to generate the freight invoice. The same is true of freight invoice auditing — a manual process performed by staff in the accounting division.

Human error is a very real challenge for manual freight invoice processing. By automating processes and using machine learning-powered freight bill software, human error can be largely — or entirely — eliminated from the equation. This business process automation can be used in conjunction with integrations to autofill freight bill fields. Automation simplifies and streamlines the process in a big way, leading to greater speed, accuracy, and efficiency.

Beyond human error, there are a variety of additional challenges associated with manual invoice processing, including the following.

• Speed – Manual freight invoice processing and auditing are slow, tedious processes that require data pulls from backup documents. You’ll see significant improvements in speed if you can automate some or all of this process flow.
• Efficiency – A machine learning powered freight bill solution will improve over time as the algorithm is refined. This leads to increased efficiency in a company’s invoice processing practices. Moreover, the staff who previously handled these tasks are now freed up and available to focus on higher-level projects that deliver a greater ROI leading to greater productivity.
• Cost-effectiveness – Cost is always a concern, and few things are more cost-effective than ML. This is a direct result of improved speed and accuracy. Additionally, human staff resources are free to focus on more profitable tasks.
• Accuracy – Accuracy is always a concern for freight bills. Inaccuracies aren’t just problematic from an accounting perspective; they can also alienate clients, damaging a company’s business operations for obvious reasons. Machine learning will improve accuracy and precision allowing the ability to pull data directly from BOLs and other backup documentation.

Freight invoice inaccuracies hold the potential to harm carrier relationships. When a company’s freight invoices contain errors that result in under or overpayments, it places the business in an awkward position where they must approach either the client to request additional funds or the carrier for a refund of funds.

The Benefits of Automated Freight Invoice Processing With ML

ML-powered freight invoice processing is highly-efficient, allowing a business to process and audit freight bills quickly, accurately, and with minimal human intervention.

Automated freight invoice processing with machine learning technology extracts information from various integrated data sources. The data extracted varies, and this is where machine learning capabilities becomes extremely useful. The ML technology evaluates the source material to determine which values align with the fields on your freight invoice. And with ML technology on board, this tool will become increasingly fast and efficient. The benefits include the following.

• Client relationships see a benefit since you won’t need to deal with awkward discussions about freight invoice errors.
• Cash flow and accounting processes are improved with more accurate freight bills.
• Staff is free to focus on other high-level tasks because they’re no longer spending their time working on freight invoices.
• The business sees improved cost-effectiveness, which bolsters the company’s bottom line.

The Future of ML-Powered Freight Invoice Processing

In the coming years, we’ll continue to see advances in the realm of artificial intelligence, machine learning, deep learning, and natural language learning technology, with these technologies getting even “smarter” and more advanced. We could see some portions of freight invoice data captured from conversations without the need to scan documents or manually enter data. This would reduce the burden on staff who would otherwise need to capture documents in order to pull data into the system.

Logistics operations managers can expect to see better, more complete automation for multiple aspects of the freight bill creation process and the actual transport and logistics processes as a whole. This means that human error will decrease even further, and cost efficiency will increase, leading to a positive impact on a company’s bottom line and overall operational efficiency.

In the future, third-party logistics companies, freight shippers, and carriers may also find additional opportunities for process automation in other operational areas, resulting in more improvements to their overall productivity and profitability levels. Business process automation delivers a great ROI by completing tasks with greater accuracy, faster speeds, and lower costs.

Each logistics operation is unique, and as a result, each business has unique automation needs when it comes to its freight invoice processing, integration, payment, and auditing needs. We invite you to contact iTech to discuss your freight invoice processing challenges, and we’ll work to develop an innovative solution that meets your needs.

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IDS Commander iTech2021

http://www.itechind.com