How are new technologies along the trade value chain improving efficiencies and driving change?
According to the World Economic Forum, worldwide spending on blockchain and distributed ledger technology in 2019 is forecast to be $2.9bn, rising to $12.2bn in 2022. While this spend is not wholly attributable to the world of global trade, given the current model of primarily paper based, manual processing and opaqueness of data and status, a significant portion of this spend will be targeted at supply chains and the associated financing. Understandably distributed ledger technologies are getting much of the attention when it comes to trade. This is because of its unique characteristics of immutability and support of consensus, and real-time decision making based on a single version of the truth. It means it has the potential to significantly transform a business that is highly fragmented, involves a large number of stakeholders across different industries, and is becoming increasingly more complex.
Distributed ledger and other collaboration technologies have stimulated innovation and a change of mindset within global trade. Networks and platforms have emerged where traditional competitors are joining forces for the common good of the industry, while for the first time cross-industry silos are being broken down through common standards and initiatives such as the Universal Trade Network which includes representatives from financial services, policy makers, shipping and logistics providers and corporates. Networks are at various stages of maturity depending on their scope, geography and structure, and over time there is likely to be some rationalisation of this. However, it is unlikely that one network will dominate, rather a “network of networks” paradigm will come into existence where seamless interoperability, driven by common data and processing standards, is achieved. This interoperability is critical to ensure we don’t simply use new technology to recreate the digital islands that currently exist.
However distributed ledgers and blockchain are not the only new technologies that are driving change. Technologies such as Optical Character Recognition (OCR) and Robotic Process Automation (RPA) that have been available for a number of years are seeing a renaissance thanks to increased reliability and faster data processing capabilities. Having previously required pre-programmed templates and rules, thereby been relatively inflexible, these technologies are now being combined with machine learning (ML) and artificial intelligence (AI) to allow decisions to be made more dynamically. The increasing availability of data from multiple sources, and the increased speed at which that data can be consolidated, analysed and used, is driving both efficiencies in current business models and the development of new products and services.
From a supply chain perspective, increased visibility of data in real time, improved processing power and the availability of AI and ML is transforming the way businesses are managing their supply chains. Buyers and suppliers can understand bottlenecks in their supply chain, view the inventory of providers in real time and dynamically change tact or providers to de-risk delivery. Just-In-Time (JIT) supply chains are nothing new, but whereas JIT previously meant goods arriving on the same day they were to be used, in the automobile industry we now see components arriving on the production line minutes before assembly. Manufacturers are now able to predict to unerring accuracy when goods will be finished, when they will be sold and when more stock is needed. Through IoT sensors and digital objects, a real time view of inventory supports the automatic creation of purchase orders and the associated requests for financing. The self-servicing supply chain is now a reality. In the aerospace sector 3D printing is already being used to further drive efficiencies through the onsite creation of certified parts on demand.
From a financial services perspective, the provision of financing has previously been the domain of the traditional banks and institutions. These organisations rely on historical data submitted by their corporate clients to assess risk and provide facilities, before using evidence in the form of shipping documents, purchase orders or invoices to provide working capital. The real time status of goods and services, which is now available through sensor data, gives the potential for financing to be provided based on this information rather than supporting documentation. Furthermore, dynamic pricing, facility provision and risk assessment is a real possibility.
The exponential growth of platform companies such as Amazon, Google, Alibaba and Tencent has been driven by the increasing use of cloud solutions and open APIs, which enable new products and services to be delivered to consumers with accelerated speed and ease. The result has been that some areas of supply chain and financial services have become increasingly commoditised, meaning existing companies need to diversify and find new sources of revenue. The commoditisation of payments handling has resulted in companies such as Mastercard and Visa moving into supply chain management, while one reason that Maersk has disbursed $700m of trade financing worldwide since 2016 is the increasing commoditisation of container shipping and associated overcapacity fuelled by platform availability.
So, what is the end game here? The pricing flexibility and data processing power enabled through cloud and quantum computing, the ability for stakeholders across the ecosystem to collaborate based on a real-time single source of truth enabled through distributed ledger technologies, and the provision of new products and services at speed through open APIs and platforms, have the potential to eradicate the $1.5tn global financing gap. They have the power to reduce the cost and risk of trading internationally and the associated financing. However, for this to be achieved, common standards across initiatives and industries are required, along with regulations that allow new business models to be adopted and scaled in a sustainable and safe manner. Finally, we must not underestimate the socio-cultural element of delivering the future of global trade, ensuring that the people involved are brought along the journey to maintain trust in the network.