Nigel Cummings
In 2020, weeks before the World Health Organisation declared a global pandemic, MIT professor David Simchi-Levi, cowrote with Pierre Haren, an article about the closure of manufacturing plants that would lead to future toilet paper shortages and supply chain chaos globally.
Simchi-Levi, who, at that time, led the MIT Data Science Lab, made those projections with the help of his “Risk Exposure Index,” a mathematical model that predicted the impact of disruptions (like pandemics and volcanic eruptions) on a supply chain.
His research indicated that streamlining manufacture required a shift in how companies thought about risk: in fact, rather than measuring the likelihood that a disruption could occur at any time, a focus should be made on building resilience, something he defined as the ability to respond quickly when and where shocks occur.
Could a resilient supply chain meet consumer demands under the pressure of such eventualities? Simch-Levi argued a flexible, resilient approach would ensure enough ‘products’ were on shelves to meet consumer demand, but such supply would need AI to make it work efficiently.
Azita Martin, VP and GM of retail AI at Nvidia broadly agreed with Simchi-Levi when she said, “AI is not about automation, it’s about bringing intelligence to automation”.
Nvidia’s “Omniverse” is simulation software which is being used to create “digital twinning technologies” for use in accurate digital representations of the physical environment found in a factories.
Nvidia worked on creating an “industrial metaverse,” so that companies could test out different factory layouts and make optimisation decisions aided by real-time updates on the physical space.
In a similar timeframe, machine health company Augury was making predictions about mechanical failures before they happened to help factories avoid disruptions caused by malfunctioning machines.
Brian Richmond, Augury said, “If you’re able to tell a manufacturer that one of their key components is going to have a failure months or weeks before it fails, you can put it on a planning horizon. It gives you the ability to plan the plant and quit letting the plant plan you.”
Augury’s sensors, which collect data about vibration and temperature to diagnose malfunctions, are currently used in factories for everything from consumer-packaged goods manufacturing to lumber and paper.
One thing is certain, in the AI age, automated factories are cheaper to operate, and companies will likely bring production closer to their end consumers more quickly than ever before. It’s that type of flexibility that Simchi-Levi says is necessary to avoid the type of supply chain chaos caused by eventualities such as the recent Covid-19 pandemic.
David Simchi-Levi is a Professor of Engineering Systems at MIT and serves as the head of the MIT Data Science Lab. He is considered one of the premier thought leaders in supply chain management and business analytics.
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