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Logistics teams work with consignment notes, packing lists, delivery receipts, and exception notices.

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Foxit helps turn these documents into usable data and a clearer view of each shipment.

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First, we use Foxit IDP to capture information from a consignment note.

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In this example, the shipment contains 10 cartons across two product lines,

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with a total weight of 120 kilograms.

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The extraction schema defines the information we need.

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Shipment references, dates, sender and receiver details, quantities,

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weights, and cargo items.

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The workflow connects document intake to data extraction.

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We submit the sample PDF and open the extracted fields beside the original document.

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The carton count, weight, and both cargo rows have been captured.

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The same schema also processes the scanned version.

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In the exception sample, the total weight and one cargo weight are missing.

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We can see those gaps and check the source before following up.

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Next, Foxit AI Workspace helps us understand the shipment across several documents.

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The project contains the consignment note, packing list, proof of delivery,

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exception notice, and service procedure.

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We ask whether the shipment has been fully delivered.

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The answer compares 10 cartons shipped with 9 received

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and identifies the outstanding carton of sensor modules.

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It also finds the planned redelivery time in the exception notice.

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Source references let us return to the supporting documents.

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We then ask what customer service should do next.

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Using the service procedure, the answer identifies the coordinator,

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calculates the customer update deadline, and explains the follow-up actions.

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Together, Foxit IDP and AI Workspace help teams reduce retyping,

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compare shipment information, and prepare the next action with the relevant documents at hand.