The logistics industry requires the timely movement of goods from manufacturers or suppliers to consumers and operates under numerous regulations. Further, it collects a great deal of information (data) throughout its operations within each supply chain. Each shipment that moves through logistics, whether it is a cross-border ocean shipment, a last-mile package delivery, or a supplier’s cargo sent to a factory, generates documents, messages, and status updates that need to be interpreted and acted upon. Historically, these large amounts of data have created bottlenecks in how this information is entered into systems (manual entry), how quickly it is validated, the differences in information formats sent between parties, and how quickly errors that may occur in these processes are resolved.
Artificial intelligence (AI)-driven data extraction is key to achieving operational efficiency in logistics, enabling the conversion of unstructured, messy data into usable, standardized data at scale. This article will cover the basics of AI data extraction, why the logistics industry uses AI-driven data extraction, actual use cases, issues that need to be resolved when implementing AI Data Extraction, how to measure the benefits of using AI data extraction, and, finally, actions that can be taken to avoid the most common problems associated with AI data extraction implementations.
What Does “AI Data Extraction” Mean In Logistics?
The automatic extraction of relevant information from unstructured/semi-structured sources (such as PDF files) and its conversion into a structured format (e.g., a database or document management system) using AI.
The technical components of AI Data extraction include:
● Machine learning + Optical Character Recognition (OCR): It is used to capture characters/text from an image or scanned document.
● Natural Language Processing (NLP): It is utilized to determine the meaning of a document by identifying the fields (address, Purchase Order number, etc.) contained within it and clarifying any ambiguities.
● Computer Vision: It is used to analyze the layout of documents, identify handwriting, and extract items from photographs representing documents (e.g., container seal, Bill of Lading, etc.).
● Entity Resolution/Normalization: It is a process to map extracted values to existing master data for easy identification of that item (such as SKU, port code, Customer ID, etc.).
● Robotic Process Automation (RPA)/Workflow Engines: It allows for routing of the extracted data directly into TMS/WMS/ERP systems and triggering any downstream activity.
Instead of viewing data extraction as a single solution, logistics providers have developed an approach that combines OCR, domain-specific NLP models, and integration logic to deliver timely, accurate data to their operational systems.
Why Does Logistics Face A Data Problem?
Logistics relies heavily on automated data extraction due to multiple structural characteristics:
● High volume and a wide range of documentation. Numerous types of documentation follow freight through the movement process, including: bills of lading, commercial invoices, packing lists, customs declarations, delivery receipts, digital images created as proof of delivery, and carrier tracking updates via email. Furthermore, types of documents will vary by carrier, geographic area, and customers.
● Unstructured data formats. The majority of critical data is still received via scanned PDFs, faxed images, or plain-text emails rather than via standard APIs. Structured EDI feeds will coexist with unstructured attachments.
● Time-sensitive data and exposure to exceptions. Due to the time-sensitive nature of logistics operations, delays in processing a document will affect other phases, including customs clearance, planning for shipment unloading, and billing cycles. Manual processing will also create inefficiencies in exception identification, e.g., missing signatures, mismatched quantities, or being flagged as an unfit export product due to regulatory issues.
● The high cost of manual data entry, the high likelihood of data entry errors, and the inability to easily increase data processing capacity during peak months or during the transition to new lanes are additional challenges. Additionally, cross-system integrations (e.g., TMS, WMS, customs platforms, and financial systems) often do not receive consistently normalized data inputs from all the different systems, creating inconsistencies when attempting to extract and process data from them.
Using artificial intelligence to scrape data solves many of the structural issues in cargo logistics by improving data flow efficiency between systems and eliminating inconsistencies in data-collection formats.
What Are The Primary Operational Benefits?
Artificial intelligence (ai) data extraction provides immediate operational and financial benefits.
● Quicker data processing: Automation can reduce the time needed to access data from days/hours to minutes, allowing companies to improve customs clearance and invoicing/order fulfilment times.
● Increased Accuracy: When Logistics documents are processed through Machine Learning (ML) models, they are less likely to contain transcription errors or other errors than if a human manually entered them. It reduces the work required to correct these errors and decreases claims against companies.
● Scalable: AI-driven automated pipelines allow companies to scale production during peak seasons, as they have more capacity than would be feasible if humans processed all the data.
● Enhanced Exception Detection: AI Logistic Systems help organisations quickly identify missing paperwork, weight discrepancies, and regulatory non-compliance.
● Reduced Expense: Lower manual operations costs, fewer detention/missing cargo fees due to late document submission, and improved invoice reconciliation; all of these improvements reduce operating expenses.
● More Information (Visibility) and Better Analytics: Structured, real-time information enables dashboard views for using optimised algorithms to select Routes, Inventory, and Carriers.
What Are The Key Use Cases Across The Logistics Value Chain?
● Carrier Documentation and Bol Processing: Process invoices and Bills of Lading to identify required information (shipper/consignee, container ID, seal number, weight) to properly update the Transport Management System (TMS) and provide proper instructions to yard operations.
● Customs and Compliance: Automation of HS Codes, the Origin Declaration, and the Invoice Line Item extraction process to prefill customs declarations, reducing the likelihood of any holds/fines.
● Warehouse Intake & Put-Away: Validate Quantity Check (Qty Check: did we get what our records said) (Packing List/ASN) to facilitate receipt of items in the warehouse.
● Freight Audit & Billing: Invoice field capture, matching to pricing contracts, and identifying discrepancies for auto-audit and dispute workflow.
● Proof-of-Delivery (POD) & Claims: Capture Signatures/Time Stamps & Picture Evidence from (POD) to facilitate quicker Customer Settlement and Claims Processing.
● Exceptions Last Mile (Last Mile Exceptions): Parsing of Customer Correspondence/Driver Notes/Pictures to detect Delivery Exceptions (Blocked Access/Damaged Packaging), which creates Alternative Workflow Opportunities.
● Supplier Onboarding & Master Data Capture: Capture of Supplier Registration/Document and Certificate (Document & Certificate) to populate your Procurement System/Records for Compliance.
Measuring ROI: KPIs to track
Organizations must evaluate both direct and indirect impacts of their technology initiatives. Some of the more common metrics to assess are:
● Time from start to finish (time spent on paperwork) will now be recorded in hours and minutes.
● Cost of each piece of processed paper.
● % Decrease in the overall number of manual data input tasks.
● % Accuracy of data entry and field matching to predetermined benchmarks.
● Time to identify and rectify data input inaccuracies.
● Decrease in the number of items to claims for damages (detention/demurrage fees, late payment fines, etc.).
● Sales invoices sent out will be printed and submitted sooner (improving days until received).
● Each FTE employee can complete a larger volume of work than in pre-project implementations (greater throughput).
If implemented correctly, you’ll generally achieve ROI (Return on Investment) within 6-12 months, primarily due to Reduced Labor Costs, fewer regulatory fines and fees, and quicker conversion of paper to payments.
Implementation Considerations And Best Practices
To achieve maximum benefits, operators should pursue a systematic, methodical approach to the implementation, including:
● Domain-Specific Training Data: All logistics documentation uses its own unique, industry-specific vernacular and idiosyncratic formatting; thus, training a machine learning model on samples from that domain will significantly increase its accuracy.
● Hybrid Human-in-the-Loop Workflows: Start with an automated approach, then review any low-confidence fields manually. Subsequently, use human corrections to retrain the ML models (active learning) and, as confidence improves, continue the automation process.
● Change Management: Train all Operation Teams and Exception Management Teams on the use of new workflows. The automated data collection process will inherently change the format of the exception resolution process for staff; therefore, staff members will need to transition from data entry to exception resolution.
Conclusion
Logistics are time-sensitive and get impacted by large amounts of data. Manual processing of paperwork introduces many costs, delays and errors and creates a significant liability that negatively impacts the Supply Chain. Artificial Intelligence Data Extraction is designed to eliminate the inefficiencies of manually processing paperwork by converting unstructured and heterogeneous documents into timely and resource-efficient data. AI extraction improves the efficiency of the supply chain in all areas including Receiving, Customs, Invoicing, Claims, and Operations by producing quicker cycle times, lower costs, fewer exceptions, and better customer support. When implemented properly through domain training data, human oversight, secure integration, and ongoing monitoring, AI data extraction will provide a logistics company with the foundational infrastructure to capture both short-term savings and the capabilities to achieve long-term strategies such as predictive planning and dynamic optimization.
