Document Processing in
Transportation & Logistics

Industry Focus: Transportation & Logistics

In the current document processing system in transportation and logistics, traditionally revolving around significant paper management, document data is stored and managed using simple document management systems (DMS) such as document repositories. Instead of storing full document contents, they store document metadata only. Documents are scattered in multiple repositories, with no central access point for collaboration or analytics. This leads to reduced visibility into document information and document-driven workflow processes. This can be costly, inefficient, and time consuming for document owners to retrieve document data.

Any document processing system in the industry needs to handle two very different document types: structured (such as bills of lading) and unstructured documents (such as packing lists). It should also support a range of document content, document types and document sources. The document processing system does not act on any of the document data directly. Instead, it processes document data to produce a series of actions or documents that drive the logistics process forward. For example, in order for carriers to make freight payments against shipper-generated bills of lading, they must first pull document content from the document repository and then reformat it into a standard template. This document-driven process repeats itself at every carrier, and is labor-intensive for document owners with very little document processing value.

Currently, document processing in the transportation and logistics industry uses inefficient batch processes to handle document data. It requires human intervention before any document data can be extracted. The document processing workflow requires multiple steps to transform document content into document actions, and is highly time-consuming. This process also uses inefficient document formats such as PDFs that are not easily merged or transformed.

The combination of document data from paper documents and electronic documents in a transportation and logistics company’s enterprise system might range anywhere from a few thousand document records to more than half a million document records. This data is growing rapidly as document volume increases and new document types are produced.

This makes document processing in transportation and logistics extremely challenging for document owners. Inefficiencies add time, cost and on-going maintenance efforts to manual processes that are typically error-prone. Meanwhile document owners struggle to find document data in document repositories, and often have no visibility into the document content at all. Without automated document processing they risk losing competitive advantage to their competitors

Intelligent Document Processing (IDP) is a document-centric system that converts unstructured document data to a structured document format such as XML or JSON as well as document analysis. IDP combines document processing, document management and machine learning technologies to handle document data of all kinds, formats and origins. It includes document analytics tools that analyze both structured and unstructured document content in the same system for enhanced document-driven workflow performance.

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DocDigitizer mimics what a human mind does by taking into consideration semantic and structural information and using it to provide data capture with unrivaled precision. The future of transportation and logistics is in streamlining the parts of the processes that can be tackled at the speed of light.

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DocDigitizer’s system creates solutions where your average document management system only creates more problems. Unlike most data extraction technologies or OCR engines, DocDigitizer’s data capture engine powered by Machine Learning can understand and capture information from semantic patterns available in the document, learning as it goes to generate error-free unstructured data results to be processed by your organization.

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