Flexport (https://www.flexport.com/) is a freight forwarding company that uses software to help businesses optimize its supply chain and ship cargo around the world. Based in San Francisco, Flexport has raised over $300M in financing from Google Ventures (GV), First Round Capital, and other prominent investors.
Logistics is 12% of global GDP, and companies spend $1.1 trillion dollars on freight forwarding each year. The massive scale and complex network of people involved in transporting goods – from freight forwarders to retailers – make supply chain visibility a challenge for all stakeholders involved .
Machine Learning’s importance to Flexport’s product development
Machine Learning (ML) is a component of Artificial Intelligence (AI) that uses an algorithm to learn from prior data in order to produce a behavior or predict an outcome. As a software company that processes shipping data across air, ocean, rail, and truck for over 15,000 global businesses every day, Flexport is uniquely positioned to leverage that data to create new products that help businesses optimize their supply chains.
Given the complexity of the supply chain, the biggest data problem facing Flexport is that forwarders in Flexport’s networks provide unstructured data that are not labelled adequately for machine learning. Oftentimes, Flexport employees would need to call the forwarder, ask detailed questions on the data provided, and then manually update Flexport’s platform . Without clean and accurate data to train machine learning algorithms, predictions for shipping events, shipment times, and shipping demands become practically impossible.
At the same time, legacy technology companies are starting to enter into the freight-forwarding space. Most notably, IBM launched Watson Supply Chain last year to create supply chain visibility in shipping . IBM’s focus with this new platform is to leverage data from external sources (e.g., weather forecasts) and the forwarder’s internal data sources to predict supply chain disruptions. Given IBM Watson’s strong foothold in the machine learning space and its parent company’s financial resources, this new supply chain platform creates pressure for Flexport.
How Flexport plans to address the data challenges
In the next two years, Flexport’s goal is to provide better end-to-end services for companies, while including more inland services . More importantly, Flexport will create easy-to-use interfaces for the forwarder to input unstructured data into a format that is accurate and labelled into the Flexport platform. The main focus here is to collect clean data throughout the entire system in order to train a machine learning algorithm.
The company is doubling down on efforts that will help factories coordinate bookings, logistics teams collaborating on imports, and supply chain managers relaying pickup details to truckers . In order to succeed in the next two years, Flexport will continue to focus on operations and deliver a better customer experience at a relatively low cost.
Over the next ten years, the CEO plans to open offices in the top 20 exporting countries in the world and continue to build out its network of warehouses so that more data is being captured and fed into the company’s algorithm .
The case for building Flexport’s next AI-first office in Canada
My recommendation is for Flexport to build an AI-first (instead of an operations-first) office in Toronto, Canada.
Currently, Flexport has offices in the US (San Francisco, New York), Asia (Hong Kong, Shenzhen), and Europe (Amsterdam, Hamburg) that’s focused strictly on operations. All of these offices are strictly focused on operations and not on the research & development of machine learning expertise. Without world-class machine learning capabilities, Flexport will not be able to build the operating system for global trade.
Canada is the best place to build a world-class machine learning company. In addition to having two pioneers of machine learning in Geoffrey Hinton and Yoshua Bengio , Toronto is home of the Vector Institute for Artificial Intelligence – a $120 million initiative that exists to further generate and retain world-class AI talent.
The political landscape in the US reinforces Canada’s AI sector’s strength . More than 50% of Toronto companies are seeing more US applicants in 2017 than in 2016, signalling that there is a potential “reverse brain drain” of top talent flowing from the US to Canada.
Additionally, Flexport building an office in Toronto will provide immediate access to top machine learning experts at a fraction of the cost. Not many Silicon Valley startups have adopted this strategy yet, which provides Flexport a first-mover advantage in nurturing machine learning talent.
- Does machine learning expertise need to be developed in-house in order to build a competitive advantage for product development?
- Is it better to solicit help from “AI-as-a-Service” vendors to help Flexport apply machine learning using data that Flexport collects so that Flexport can focus only on operations?
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