What is a supply chain control tower, and how has the concept evolved in the age of artificial intelligence and data analytics? Heidi Benko, vice president of product management with Infor, explains.

In the context of the supply chain, the term “control tower” should signify a single source of analytics and decision-making. Yet there are a number of variations in existence today, including those aimed at transportation and logistics, planning and forecasting, and true end-to-end supply chain management. In the case of the last, that means bringing in data from multiple functions, both within and outside the organization, to make decisions affecting every stage of the supply chain. “All users need to be looking at the same data at the same time,” Benko says.

The ultimate goal, she says, is to break down functional siloes in order to achieve better visibility of both orders in transit and their related data. For that task, organizations today need help from machine learning and artificial intelligence, technologies that are able to handle the massive amounts of data that are generated by global supply chains.

Because an estimated 80% of all data and processes originates outside a single organization, the control tower must be part of a multi-enterprise business network, Benko says. Companies need to be able to see where inventory is in the system, then respond quickly to any disruptions or changes in demand.

Equally essential to organizations today is the creation of a digital twin, which mirrors the physical supply chain and can provide better insights to the control tower, including “observing the patterns that are constantly changing,” Benko says.

“Things are not static,” she adds, “they’re dynamic. When you take action, you want the twin to be able to simulate based on what’s being observed in the true supply chain.”