Remote Productivity and Paperless Processing in a Trade Document Workflow Platform for Pandemic Resilience and Greener Recovery

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In April, the World Economic Forum (WEF) noted, in regard to supply-chains, that "measures for COVID-19 have made clear that operations dependent on physical assets, such as paper, can face serious disruption when physical presence is not a possibility...paper printouts are usually handled by operations personnel who must come to the office, or another place of work, and coordinate with others. In addition, value chains that rely on information in these paper documents lose access to that visibility very quickly and cannot react to changing conditions...Trade is notoriously reliant on paper-based processes". JPMorgan's 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 inefficiencies and lack of visibility around paperwork. The current manual approach to checking paperwork results in long turn-around times, limited visibility, no formal knowledge capture and error prone processes. Beyond monetary costs, there is an environmental burden, global trade generates 320 billion documents/year (JPMorgan,UNCTAD), conservatively assuming each document is only printed once, that equals 1.5mio paper tonnes and 16.5mio tonnes of CO2 emissions/year.. WEF notes for better performance under COVID-19, "digitizing...is not simply a matter of cost, but primarily of visibility and managing supply chain risk. To limit the impact of points of failure... it is important to make data available through digital means." COVID-19 has created "fragmented knowledge across an organization, the knowledge transfer that normally happens when you're sitting in an office doesn't happen naturally anymore. The cumulative knowledge and know-how of organizations is going to have to be captured better in the systems and processes --- because collaboration will still be the key to survival in shipping." This project aims to conduct research and preliminary testing into cutting edge machine learning research to upgrade our core platform's algorithms and deliver highly accurate, reliable document digitisation to incite the heavily impacted trade sector's movement into paperless trade and expedite a robust, sustained recovery through use of flexible, remotely accessible, digital tools. We will deploy machine learning improvements to our current extraction algorithm, in doing so delivering productivity enhancements, captured know-how and greater remote digital visibility over data within paperwork. We will further develop team communication features that will enable trade operations departments to work efficiently remotely and in collaboration in a way that is currently not possible.