Global supply chains are by their very nature complex, but data analytics offers an opportunity to simplify planning and forecasting, and create more resilient sourcing, production and distribution, says Melissa Koeman, client executive with Varis.

Given the inherent complexity of global supply chains, the notion of “simplifying” them might seem problematic at best. But Koeman believes that’s possible to an extent, by moving manufacturing and sourcing closer to the point of consumption, and diversifying the supplier base.

New advances in data analytics can help. Companies can draw on a wealth of data to make intelligent decisions about where the greatest risk of disruption in their supply chains exists. They can ensure that sourcing arrangements “are not so specific that [companies] are backed into a corner when something’s not available.”

When it comes to data, “knowledge is power,” Koeman says. It allows companies to determine what they’re buying, where it’s coming from and how it’s being used in the organization. Finally, it enables the creation of reports on the back end, so that they can assess the resilience of their supply chains now, and protect against future disruptions. 

Artificial intelligence is playing a key role today in helping companies interpret and act on the flood of data that’s available to them. Armed with data and supporting analytics, they can compare costs and risk factors to understand the consequences of, say, the shutting down of a factory in China. Often that process will make clear the need to diversify suppliers on one hand, and locate them within a more proximate geographical location on the other. 

“Risk management isn’t simply avoiding the pitfalls from day-to-day interactions,” Koeman says. “It’s understanding what’s going to happen one, five and 10 years from now — not just to the widgets they’re making themselves, but also to all of the other companies contributing to their bottom line.”