Logistics Document AI — Features to Look For

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How should you select the optimal Logistics Document AI solution?

The logistics industry is progressively adopting AI. Document AI, one of these solutions, streamlines the entire workflow by automating document recognition, data extraction, and system integration for freight forwarders.

Let’s explore the key features to consider when looking to automate logistics documents.

Answer Flagger

For each exporter who wants to ship their goods, a freight forwarder needs to read their shipping invoice(s), find the right data, and input it into the Transportation Management System (TMS). Here are some common difficulties during this process:

If data is inputted incorrectly, this causes a ripple effect down the supply chain that results in a massive wave of delays.
Exporters constantly change their shipping invoice format, making traditional OCR useless and human recognition extremely tedious.

To solve these issues, document AI automatically scans each invoice, no matter the format, efficiently extracting and inputting the data directly into the TMS.

But doesn't AI recognition still make mistakes? It can’t be 100% accurate every time, right?

Correct. That is why an optimal document AI logistics solution should have an answer flagger.

Image from Sightify

The answer flagger marks any suspicious recognitions on a document that might need an extra pair of eyes to confirm. This way, forwarders only need to double-check the red (in the case above) values each time a document is uploaded and entered into the system, ensuring maximum accuracy and efficiency.

Document Comparison

In the logistics export workflow, there are two key scenarios where document verification is crucial to prevent delays, fees, and miscommunication between exporters, carriers, and forwarders:

Freight forwarders must verify consistency between their booking requests and booking confirmations from the carrier.

If the request and confirmation don’t match, this leads to: cargo being left behind or extra fees for last-minute cargo space.
Any sudden changes in departure dates, order placements, or shipping routes result in new booking requests, forcing employees to constantly verify new versions.

Freight forwarders must verify consistency between each Bill of Lading from the carrier and their shipping order(s).

Consignees use the B/L to claim the goods upon arrival at the destination port
If consignees cannot pick up their goods, extra demurrage/storage fees will be charged.
Carriers often modify their B/L format based on location and update it periodically.

In these two scenarios, document recognition is not enough to automate this process. Forwarders still must compare the data one-by-one between documents, over and over.

Document comparison eliminates these problems by first scanning both documents (just like regular document recognition), and then putting the same field value or table entry from each document side-by-side for easy comparison.

Image from Sightify

Recognitions with differences are highlighted in yellow (in the case above), allowing forwarders to only have to check the marked boxes, making the verification process significantly more efficient and streamlined.

On-premise Deployment

Exporters are always trying to make the best profit margins, while buyers are always trying to get the best deal. This constant tug-of-war keeps a manufacturer's pricing extremely confidential. Let’s use an example:

A popular manufacturer typically sells their product at $100 per unit, but of course has special deals with distributors, big customers, etc.
Due to a data leak on the cloud, some of the manufacturer’s shipping orders and Bills of Lading are leaked to the public.
Upon finding out they are not getting the best price-per-unit for the product, many of the manufacturer’s “regular” buyers demand to be offered cheaper pricing.

As you can see, pricing is something that must be kept private at all costs from the manufacturer’s perspective. Thus, an optimal logistics document AI solution should offer private or on-premise deployment to accommodate a company’s required level of data security. This way, data leaks that could otherwise lead to disasters for manufacturers are efficiently prevented and mitigated.