The future of TMS invoicing: AI-powered automation

The logistics and transportation industry is a complex web of moving parts, and at the heart of it lies a constant stream of documentation. From bills of lading to invoices, customs declarations to export documents, the sheer volume of paperwork is staggering. Processing these documents eats up countless man-hours, often relying on tedious manual data entry and error-prone verification. Imagine if all that time could be freed up, allowing your team to focus on strategic tasks instead? The answer lies in AI-powered automation, specifically leveraging Intelligent Document Processing (IDP).

What is Intelligent Document Processing (IDP)?

Intelligent Document Processing (IDP) is a technology that automates the extraction, classification, and processing of data from various types of documents, including structured, semi-structured, and unstructured formats. It transcends traditional Optical Character Recognition (OCR) by leveraging Artificial Intelligence (AI) technologies, such as Machine Learning (ML), Natural Language Processing (NLP), and Generative AI, to understand the content, context, and relationships within documents. IDP systems self-learn and continuously improve their accuracy over time through iterative training on processed documents. This self-learning capability allows the system to adapt to variations in document layouts, handwriting styles, and language nuances, leading to increasingly accurate data extraction, reduced manual intervention, ultimately optimizing workflow automation and delivering greater efficiency.

The logistics documentation overload

Think about the vast array of documents frequently encountered in transportation and logistics. Beyond the commonly known, the document landscape is surprisingly complex:

1. Bills of Lading (BOL): Crucial for tracking shipments, verifying goods, and serving as a receipt of freight services.
2. Invoices: Received from numerous suppliers for a variety of services (freight, warehousing, fuel, etc.) and often require detailed cost allocation.
3. Customs Declarations: Necessary for international shipping, requiring meticulous detail and adherence to country-specific regulations (e.g., import/export licenses, certificates of origin, packing lists).
4. Export Documents: Facilitating the legal export of goods, including commercial invoices, pro forma invoices, export packing lists, and shipper’s export declarations.
5. Delivery Receipts: Proof of delivery and confirmation of service, often including signatures, photos, and timestamps.
6. Packing Lists: Detailing the contents of each package or pallet within a shipment.
7. Purchase Orders: Used to formally request goods or services from a supplier.
8. Safety Data Sheets (SDS): Required for the transportation of hazardous materials.
9. Inspection Certificates: Documents confirming that goods have been inspected and meet specific quality standards.
10. Transport Documents: Cover the movement of goods and vary depending on the mode of transport such as Air Waybills (AWB), Ocean Bills of Lading, and Rail Consignment Notes.

Manually processing these documents is not only time-consuming but also prone to errors, leading to costly mistakes, delays, and compliance issues. This is where intelligent automation steps in to revolutionize the process.

Invoice processing: A pain point ripe for automation

Let’s focus on invoice processing within a Transportation Management System (TMS). The generic process typically involves:

Receiving invoices: Invoices arrive from hundreds of suppliers in various formats (PDF, image, etc.) for diverse services like freight loading, cleaning, documentation, and more. The lack of standardization creates an immediate bottleneck.

Data extraction: Manually extracting invoice details like vendor name, invoice number, amounts, service descriptions, and line items. This is a highly repetitive and tedious task, susceptible to human error.

Cost matching: Matching the extracted invoice details with the corresponding cost lines within the TMS. This often involves cross-referencing multiple systems and spreadsheets, adding to the complexity.

Verification: Identifying errors, duplicates, and inconsistencies. Catching these discrepancies manually is extremely challenging and time-consuming.

Dispute management: Handling disputed invoices, often involving back-and-forth communication via email, phone, and potentially even physical mail. This can drag on for weeks, impacting cash flow and vendor relationships.

Payment posting: After verification and dispute resolution, the invoice is finally posted for payment in the finance system. This entire cycle can take weeks to complete manually.

This process is highly inefficient and error-prone. However, AI can dramatically transform each step.

Processing with AI: the smart way forward

AI is transforming TMS invoice processing by automating data capture, validation, and matching. This results in faster processing times (potentially reducing processing time by up to 80%), improved data accuracy, and streamlined dispute resolution. Leveraging technologies like Generative AI, semantic search, and machine learning, these solutions eliminate manual data entry, enrich data, and accurately allocate costs, enhancing financial visibility and strengthening vendor relationships.

Embracing the future with Rappit Undoc

The transformation of TMS invoicing is underway. AI-driven automation, powered by solutions like Rappit Undoc, is no longer a futuristic concept; it’s a present-day solution ready to revolutionize your operations.

Rappit Undoc facilitates the building of complete, AI-driven document processing workflows directly into your TMS. Leveraging a robust suite of building blocks built on top of Google AI, users can design and deploy custom automation applications that integrate seamlessly with their existing TMS. The constant influx of transportation documents is automatically classified upon arrival. Critical information is extracted from diverse formats, which is then seamlessly integrated into the TMS systems to automate downstream processes and generate insightful reports and recommendations. Real-time insights are unlocked from even the most complex and unstructured documents, and rapid search & match capabilities can be implemented using advanced vector search technology for semantically similar results.

To ensure accuracy and compliance, Rappit Undoc provides a configurable verification app where users can review and correct extracted data. This manual verification step isn’t a dead end; it triggers a crucial feedback loop, continually training the underlying ML models for automated self-learning and improved accuracy over time. Finally, Undoc offers a robust document management system, configurable for scalable document storage, tagging, indexing, search, and retrieval, providing a central repository for all transportation documents and further streamlining TMS operations. This end-to-end solution delivers unparalleled automation and efficiency gains within the TMS ecosystem.

By leveraging the adaptable Rappit Undoc solution, users can unlock significant benefits:

• Reduced processing time: Automate tedious tasks and accelerate document processing.
• Improved accuracy: Minimize errors and ensure data integrity.
• Enhanced efficiency: Free up valuable resources to focus on strategic initiatives.
• Cost savings: Lower operational expenses and improve profitability.
• Better vendor relationships: Streamline communication and facilitate faster payments.