Machine Learning applications for international trade

全球挑战和电子商务趋势的变化意味着，管理国际商品的流动正变得越来越苛刻和复杂。亚马逊(Amazon)和全球速卖通(AliExpress)等全球销售平台运往当地配送中心的货运快速增长，加剧了这种情况。在支离破碎的全球物流业，海关处理成本正在螺旋式上升，延误正在成为地方性现象。贸易公司、海关经纪人和货运代理迫切需要解决方案，以消除其海关流程的瓶颈，这些瓶颈使全球行业损失数十亿英镑。市场机会是巨大的：物流业每年处理13.5万亿GB的货物；其中英国进口5000亿GB(对欧盟的进口占25%)。国际快递包裹为快递公司创造了38亿GB的收入。核心瓶颈来自于国内和国际包裹编码之间持续脱节，以及缺乏用于分析的数字化。数据难以获取和结构化，限制了竞争对手解决方案的能力。SiftyML基于机器学习的软件提供了一个革命性的解决方案，它将颠覆全球物流业，具有独特的海关数据结构和处理工具，可以成为国际贸易的操作系统。该项目将把SiftyML的现有解决方案转变为一个动态系统，实时适应全球需求的变化，使海关经纪人能够准确和快速地处理包裹，解决关键瓶颈，并提高效率和收入。它将颠覆最先进的技术，利用先进的机器学习技术，在海量的实时海关数据集上进行预测、评估和建议，从而提高货物跨境运输的效率，从根本上将处理时间从几周减少到几天，从几天减少到几小时，从几小时减少到几秒。SiftyML的算法可以使95%的简单包裹以最少的人力进行处理，使经验丰富的海关官员能够专注于需要他们的专业知识的5%的复杂包裹，并得到SiftyML的自动化和洞察力的支持，以增强人工流程。市场机会是巨大的：物流业每年处理13.5万亿GB的货物；英国的货物进口量为5000亿GB(欧盟占25%)。国际快递包裹为快递公司创造了38亿GB的收入，但由于现有的瓶颈解决方案需要昂贵的员工时间和精力，利润率很低。

Global challenges and shifting trends in e-commerce mean that managing the flow of international goods is becoming increasingly demanding and complex. The rapid growth of freight to local fulfilment centres for global sales platforms such as Amazon and AliExpress exacerbates this. Across the fragmented global logistics industry, customs processing costs are spiralling, and delays are becoming endemic. Trading companies, customs brokers, and freight forwarders urgently need solutions which remove the bottlenecks from their customs processes that cost the global industry billions of pounds.The market opportunity is enormous: the logistics industry handles \>£13.5tn in goods annually; with UK cargo imports \>£500bn (25% with the EU). International incoming parcels create £3.8bn revenues for courier companies.Core bottlenecks stem from a continuing disconnect between national and international package coding and a lack of digitisation for analysis. Data is difficult to obtain and structure, limiting competitor solution capability.SiftyML's machine learning-powered software provides a revolutionary solution which will disrupt the global logistics industry, with a unique customs data structuring and processing tool which can become the operating system of international trade. The project will transform SiftyML's existing solution into a dynamic system adapting in real-time to worldwide changes in requirements, enabling customs brokers to process parcels accurately and swiftly, addressing critical bottlenecks, and increasing efficiencies and revenues. It will disrupt the state-of-the-art, with advanced machine learning unleashed on a massive dataset of real-time customs data to predict, assess and suggest information that can increase the efficiency of goods moving across borders, radically reducing processing times from weeks to days, days to hours, and hours to seconds. SiftyML's algorithms can enable 95% of straightforward parcels to be processed with minimal human effort, freeing up experienced customs officials to concentrate on the 5% of complex parcels which require their expertise, supported by SiftyML's automation and insights to augment the human process. The market opportunity is enormous: the logistics industry handles \>£13.5 trillion in goods annually; with UK cargo imports \>£500 billion (25% with the EU). International incoming parcels create £3.8 billion in revenues for courier companies, but profit margins are tight with existing bottleneck solutions requiring costly staff time and effort.