Process Details

Input: Proof of Delivery documents (PDF, JPG, PNG) Shipment data (tracking numbers, order details)
Output: Digitized and archived Proof of Delivery records. Automated confirmation of successful deliveries. Immediate alerts and cases created for delivery exceptions (damages, shortages)
System: ERP System / Transportation Management System (TMS) - for shipment data

Proof of Delivery Data Extraction and Reconciliation

Supply Chain

Use Case Overview

An AI agent that automates the processing of Proof of Delivery (POD) documents received from carriers. It extracts key data, matches it against shipment records, identifies any delivery exceptions noted on the document (like damages or shortages), and archives the clean PODs while flagging exceptions for immediate human attention.

Challenges

High potential for costly errors from manual data handling.
Significant time and resources are spent on repetitive, low-value work.
The manual process is difficult to scale without increasing headcount.
Process bottlenecks lead to delays and missed deadlines.

Solution

This use case solution follows these general steps at a high level:

POD IngestionThe AI agent monitors Email Inboxes and Carrier Portals for incoming POD documents, which are typically PDFs or image files.
Data ExtractionThe agent reads the POD and extracts key data points: the tracking or PRO number, the date and time of delivery, the name of the signatory, and crucially, it scans the document for any handwritten notes, checked boxes, or typed comments indicating an exception.
Shipment Record MatchingThe extracted tracking/PRO number is used to look up the corresponding shipment record in the company's ERP System or Transportation Management System (TMS) to confirm the consignee and expected delivery date.
Exception AnalysisIf any notes or checked boxes are detected, the agent interprets the meaning (e.g., "carton crushed," "short 2 cases," "refused") and categorize the exception.
Automated Routing Based on Outcome:Clean PODs: If the POD is signed, dated, and has no exceptions, the agent automatically links it to the shipment record in the ERP System and flags the order as "Delivered." PODs with Exceptions: If an exception is found, the agent creates a case, attaches the POD and a summary of the issue, and routes it via Email to the appropriate team (e.g., Claims department for damage, Customer Service for shortages).

Primary Benefits

Increase EfficiencyDramatically reduce the time and manual effort required to complete the process.
Enhance AccuracyEliminate human error to ensure data integrity and reduce financial risk.
Empower EmployeesFree your team from monotonous tasks, allowing them to focus on strategic work that requires their expertise.
Improve ScalabilityHandle growing volumes of work without a proportional increase in operational costs.
Ensure TransparencyMaintain a complete, auditable trail of every action the AI agent takes, described in plain English.

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