I recently caught up with Adam Compain, CEO of ClearMetal, to discuss how AI already is being used in the logistics industry to decipher the global supply chain’s many big data complexities. Adam co-founded ClearMetal after working in Hong Kong at a container-shipping company. For 5 years prior, he worked at Google deploying the company’s newest geo-commerce technology, and for 16 years he has been the Executive Director of the nonprofit he founded to export charitable goods. Adam holds five technology patents, degrees from University of Michigan, and an MBA from Stanford University.
insideBIGDATA: What is the current state of the global logistics and ocean shipping industries? Why have efficiencies plateaued?
Adam Compain: The container-shipping industry is facing massive challenges. These challenges include high inventory levels, over-supply, low freight rates, a mild holiday, shipping season and the recent collapse of the 7th largest ocean carrier, Hanjin. Fundamentally, these problems are rooted in the increasing complexities and uncertainties of the shipment cycle. The adoption of technology hasn’t kept pace to help make sense of these complexities.
To date, the industry has driven efficiency through economies of scale– alliances, mergers, and literally bigger ships (think: increasing bandwidth). The challenge, however, is that economies of scale only go so far and can actually become detrimental: more supply without demand reduces rates and then profitability. The new megaships which carry 18,000 twenty-foot containers are also contributing to this spiral.
Industry is at capacity and CEOs of the largest logistics providers in the world have explained they cannot squeeze any more out of these efforts. IT innovation is one of the only places left to turn. A far better approach to resolving complexities is by using data and technology to make sense of it all. Think: compression vs bandwidth.
insideBIGDATA: How are big data and artificial intelligence poised to disrupt the global logistics industry?
Adam Compain: Due to the way the shipping industry grew and developed its core competency around solving problems operationally. Further, the shipping industry only digitized relatively recently and so using AI to create efficiencies wouldn’t have even been possible just a few years ago. But now, a number of technology providers and company-internal efforts have built the technology infrastructure that’s a necessary precursor to modern technologies and approaches like artificial intelligence and machine learning
All data related to a freight shipment or operational movement can be leveraged to create highly accurate and actionable insights. As a result, massive operational and commercial efficiency improvements can be made. It isn’t as easy as applying a standard Support Vector Machine (SVM) given the industry is so complex and intricate. Deep expertise and customization to the industry’s nuances are required for success, and this requires years of proprietary development. But this disruption is now possible.
insideBIGDATA: What are the top changes AI and big data will bring to the global supply chain?
Adam Compain: Like all industries, AI is fundamentally transformative for the supply chain because it delivers a level of intelligence far beyond human capabilities. AI assists in the most complex environments where people get overwhelmed, but computers thrive. As a result, AI can deliver unprecedented efficiency and profitability via more optimal asset allocation and trade management decisions. Think: in logistics, every decision is based on a prediction. Whether a shipping carrier decides to reposition a container, a broker/forwarder offers a freight rate, or a retailer orders more inventory, it’s all based on a forecast of what will happen in the future.
The challenge, however, is that customer behavior, operational performance, and market movements are so complex and uncertain, that the econometric models used in operations simply aren’t sophisticated enough to deal with the industry’s dynamism. As a result, poor forecasts lead to suboptimal decisions about how to manage equipment, labor, and inventories, which result in low profitability. By contrast, the predictive intelligence delivered through AI fundamentally transforms every logistics decision and results in an order of efficiency that has yet to be seen.
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