Novel research into language understanding to automate customs and freight paperwork

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JP Morgan 2017 trade outlook report estimates that trade-involved Fortune 500 companies incur more than $81 billion of unnecessary supply chain costs each year due to human inefficiencies and lack of scrutiny. Since medieval times, freight forwarders have organized transportation on behalf of a) shippers, by air, ocean or land, and b) carriers, to get physical goods from A to B. The traditional labour- intensive paper driven approaches result in long turn-around times, little formal knowledge capture and high operational risk.McKinsey consulting report that "in global supply chains, telephones and email are still the dominant channels, just as they were decades ago", and CargoLogik note the "current processes are an immense time and overhead killer... still largely manual ...which result in lost time for freight forwarders and a clunky, poor customer experience." JPMorgan estimates that there are typically 27 highly fragmented document entities involved for every shipment made.This project aims to develop an expert natural language processing (NLP) based tool, to identify and extract key information from unstructured text found in emails, which will be trained and configured for the logistics sector specific language and email communications. In doing so we will develop technology capable of creating structured rules for cross-checking free-form text in emails against trade paperwork and driving action on the content found within global trade paperwork.