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

从中世纪开始，货运代理就代表a）托运人（通过空运、海运或陆运）和B）承运人组织运输，将实物从A运到B。传统的劳动密集型纸张驱动的方法导致周转时间长，很少正式的知识捕获和高运营风险。CargoLogik承认“目前的流程是一个巨大的时间和开销杀手.仍然主要是手工操作。这会导致货运代理损失时间以及笨重、糟糕的客户体验。“世界经济论坛（WEF）强调，COVID-19暴露了物流物理和手动数据处理操作中的这些系统性弱点，主要是电子邮件和贸易文档驱动的工作流程。疫情加剧了现有的低效率和缺乏审查，财富500强企业每年花费810亿美元的不必要供应链成本（摩根大通2017年贸易展望）。虽然世界经济论坛建议的数字化创新提供了改善迫切需要的业务弹性的补救措施，但有效采用仍然存在挑战。物流行业采用人工智能的速度非常缓慢，目前只有12%的组织利用人工智能（MHI Industry report，2020）。人工智能应用不足的一个主要原因是物流行业拥有不断变化、非标准和复杂的信息，这对算法的可扩展性提出了巨大的挑战。该项目旨在通过开发一个无代码、端到端的自动化算法训练管道来解决人工智能的可访问性和采用问题。该工具将内置于我们现有的物流机器学习运营工作流程平台中，管道将在后台运行，自动重新训练我们的数据提取算法，以适应客户不断变化的电子邮件和文档内容，从而通过扩展易用性和效率来鼓励快速采用。

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.