Data is a vital resource in the logistics industry, and much effort is spent on collecting quality data. Collecting data, however, is only the first hurdle. Data analytics is the next critical step for your logistics data to offer any value and insights.

Regarding data analytics in logistics, structured data is much more valuable than unstructured data. However, transforming unstructured data into structured data is a challenge in its own right. This obstacle is not nearly as substantial as it used to be, thanks to artificial intelligence (AI) and machine learning advancements.

To help you get the most value out of the data that your organization collects, this blog explores:

• The role of data analytics in the logistics industry.
• The important differences between structured and unstructured logistics data.
• How Expedock's AI-powered solution makes transforming unstructured logistics industry datasets into structured data easier than ever before.

The Role of Data Analytics in Logistics

Data analytics plays a powerful role in modern logistics. Before we discuss how AI can help transform unstructured data into structured data, let's explore what logistics analytics entrails, it’s benefits, and how data analytics is changing the logistics industry.

What is Logistics Analytics?

Logistics analytics describes the process of analyzing data to gather insights that can be used to inform models and predictions and create an optimized supply chain that achieves efficiency and profitability.

Benefits of Big Data Analytics for Supply Chain Management

Big data broadly pertains to extensive amounts of data that may be classified into structured and unstructured and provides the information that logistics organizations need to form enhanced business strategies and improve operational efficiencies.

Besides providing valuable insights, big data analytics also plays a crucial role in optimizing supply chain management by improving delivery lead times and minimizing information asymmetry between manufacturers and suppliers. Data analytics allows businesses to keep up with relevant metrics, information, and updates that influence decision-making, leading to benefits such as cost reductions and service level upgrades.

Analyze and Act for Benefits

Data analytics turns logistics data into actionable insights that freight forwarders can use to improve their businesses in several ways. A few examples include the ability to:

• Conduct a detailed cost-benefit analysis.
• Estimate future bottlenecks and peaks with predictive analytics.
• Make crucial business decisions based on real-time data, and much more.

How is Data Analytics Changing Logistics?

Big data analytics is changing logistics for the better at every level. Warehouse management uses data analytics to track the location and movement of stock to streamline operations and inform inventory allocation strategies. Transportation management and service providers leverage data analytics to optimize routes based on GPS, road conditions, and weather data. For companies shipping perishable products, data analytics allows processing data from in-container sensors to track shipment conditions in real-time. Even at the level of customer support, data analytics is changing the logistics industry by enabling real-time or near-real-time shipping updates and providing accurate insights from customer data that can further improve a logistics organization's customer support services.

Understanding Unstructured Logistics Data vs. Structured

Structured data is defined as highly systemized data found within a fixed field in reports and documentations and typically stored in a logistics data warehouse or relational database (RDBMS). Structured data creates patterns that make relevant information easily searchable, making it much easier to process and analyze.

Unstructured data, on the other hand, is unrefined, unfiltered, and lacks a pre-defined format generated from qualitative sources or elements kept on data lakes. Therefore, unstructured data pose far more challenges when it comes to data processing and analysis. Not only is unstructured data more time-consuming to process, but it also tendsto create costly errors while at the same time burying otherwise valuable insights beneath the muck.

How to Classify Structured vs. Unstructured Logistics Data

Classifying structured and unstructured data within your organization starts with an in-depth understanding of your process from order to cash cycle. From there, you can identify the data that fuels your process and begin to categorize it into structured and unstructured data.

When tackling the unstructured data that might be holding your organization back and transforming it into structured data, it is a good idea to start with the data that has the biggest impact on your business. Analyzing how unstructured data affect your finance, sales, and operations teams is one effective way to prioritize which data you should structure first.

The Role of AI in Logistics

Generally speaking, AI is used in the logistics industry most prominently to organize and analyze massive amounts of data. AI is crucial in transforming unstructured logistics data into structured logistics datasets. You can also use AI to explore datasets to find insights and make predictions. Lastly, AI allows for the processing and analysis of much larger quantities of data, empowering everything from real-time shipment condition tracking to AI-powered inventory management.

How AI and Automation Work Together

Along with the various roles of AI in logistics that we have already mentioned, AI can be combined with automation solutions to perform a wide range of functions. With Expedock's AI-powered freight documentation solution, freight forwarders can leverage AI and automation to do the following.

Convert Unstructured Data

Structured data is much easier to work with than unstructured data and thus much more valuable. However, transforming large quantities of unstructured data into structured data is often challengin. Thankfully, Expedock makes this task much easier. With Expedock, freight forwarders utilize AI to automatically convert all the unstructured data they collect into structured data. Simply upload your PDF or XLS documents into Expedock to have them automatically classified and critical information automatically extracted.

Audit Invoices

Auditing freight invoices is one of the more time-consuming and tedious tasks in the freight forwarding industry, but it's a task that can now be completely automated. With Expedock, you can reconcile a 30-page statement from your freight agent, pier pass vendor, or drayage and trucking provider against what’s been posted on Cargowise or any other TMS and look forward to a guaranteed accuracy of 99.97%.

Charge Code Mapping

Charge code mapping is vital to understanding where your organization’s expenses are coming from but is yet another time-consuming and error-prone task when done manually. Expedock, however, can completely automate the charge code mapping process for freight forwarders. By identifying how line items should be matched with charge codes, Expedock can automatically match charge descriptions in a document with the corresponding Cargowise charge code to help clarify charges and align invoices with the expected data.

Accounting Organization

Expedock enables freight forwarders to automate key accounting workflows, including data entry, auditing, and P&L reports. This strategic automation allows for a much more streamlined and organized accounting process. With all of their data structured and organized in one place, accounting teams can much easier analyze the organization's current state and make payouts where necessary.

Benefits of AI, Automation, and Structured Data for Logistics & Freight Accounting Teams

Using AI and automation to transform unstructured data into structured data and automatically analyze it offers several significant benefits to the accounting teams at logistics and freight forwarding companies. Just a few of the benefits that freight forwarding companies can look forward to with Expedock include:

• Lessened Chance of Human Error – Automated data processing and analysis via Expedock limits costly human errors by reducing reliance on manual processes.
• Fewer Busywork Tasks – Expedock frees up accounting teams to focus on more critical obligations by automating many time-consuming tasks that take time away from their schedule.
• Prioritized Accounts – Freight forwarders can prioritize accounts payable reconciliation by looking at the Days Past Due information included in the Expedock results
• Streamlined Invoice to Cash Processes – Expedock helps freight forwarders avoid late payment fees by streamlining the invoice to cash process.
• Real-Time Accounting Insight – Expedock provides real-time accounting insights as data is uploaded so freight forwarders can make data-driven cashflow decisions.
• Reduced Costs – By limiting human error, wasted employee time, and late payment fees, Expedock helps freight forwarders reduce expenses.
• Improved Relationships – Expedock helps freight forwarders avoid the damaged reputation and relationships that can come from accounting errors and payment discrepancies.
• Improved Revenue – Freeing up time in your team's schedule by automating tedious tasks means they will have more time to focus on growing the company's revenue.

How Human-in-the-Loop AI Delivers Logistics Process Automation

AI has come a long way over the years, but it still is not infallible. To offer our industry-leading 99.97% accuracy guarantee, Expedock leverages an innovative method known as human-in-the-loop AI. An Expedock freight operator monitors AI-driven data extraction to verify accuracy and ensure consistent results.

Unlock the Power in Your Logistics Data with Expedock

If you would like to start transforming your unstructured data into structured data that can be used to automate a broad range of freight accounting process then Expedock is the perfect tool for your freight forwarding company. Start a free trial today to learn more about how Expedock can help your company streamline logistics data processing and analysis