Next-Generation Algorithm Training Research to Expedite AI Adoption and Accelerate Pandemic Resilience in Trade

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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. CargoLogik acknowledges 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." The World Economic Forum (WEF) highlighted that COVID-19 exposed these systemic weaknesses in Logistics' physical and manual data-processing operations, primarily email and trade-document driven workflows. The pandemic compounds existing inefficiencies and lack of scrutiny, costing Fortune 500 companies $81 billion of unnecessary supply chain costs each year (JPMorgan 2017 Trade Outlook).Whilst the WEF recommendation of digitisation innovations offer remedies to improve desperately needed business resiliency, there are challenges to effective adoption. The Logistics industry has been notably slow to adopt AI, only 12% of organisations currently leverage AI (MHI Industry report, 2020). One major reason for the lack of AI adoption is that the Logistics sector has ever-changing, non-standard and complex information, which poses massive algorithm scalability challenges.This project aims to address AI accessibility and adoption by developing a no-code, end-to-end automated algorithm-training pipeline. The tool will be in-built into our existing logistics' machine learning operations workflow platform and the pipeline will operate in the background, automatically re-training our data-extraction algorithms to customers' evolving email and document content, thus encouraging rapid adoption via scaling ease and efficiency.