The global supply chain is undergoing a fundamental shift, driven by the urgent need for resilience and responsiveness. Traditional models are no longer enough in the face of frequent disruptions, rising complexity and shifting demand signals. In their place, cognitive supply chains are emerging — AI-powered systems designed to sense, understand and adapt in real time.

Yet despite this urgency, supply chain management remains one of the most fragmented areas in enterprise software. Siloed systems and bespoke data flows create inefficiencies that block end-to-end visibility. Cognitive supply chains, powered by artificial intelligence, offer a path to autonomy by unifying decisions across planning, logistics and execution.

“It’s about speed and agility, and it’s also about understanding the disconnecting things that are existing in the supply chain,” said Wayne Usie (pictured), chief strategy officer of Blue Yonder Group Inc. “It is quite fascinating when you look at all of the sectors out there; it’s probably the one sector that you would think would be unified in its data and processes. Unfortunately, it’s the most bespoke sector, I think, that we have in all of enterprise software. Therefore, the challenge is starting to understand how to get a collective view across the supply chain and start to understand how you optimize decisions about those inventory and raw material moves across the supply chain.”

Usie spoke with theCUBE’s David Vellante and George Gilbert during the “AI and the Autonomous Supply Chain” interview series on theCUBE, SiliconANGLE Media’s livestreaming studio. They discussed cognitive supply chains as a competitive imperative, with AI as the foundational force driving the shift. (* Disclosure below.)

Cognitive supply chains in detail

Cognitive supply chains are about more than just automation — they involve AI agents that can see, understand, decide and even act. These systems leverage unified data clouds that represent the entire supply chain ecosystem, allowing businesses to simulate and optimize scenarios in real-time, according to Usie.

“We have historically required companies to decide whether you allocate a product or you replenish a product,” he said. “Now you’re able to have the technology determine, on every single inventory move decision, whether it should be based on a pull signal or a push signal. When you talk about cognitive, think about technology and agents. Not only can they see; they understand, they can comprehend the disruption, they can decide and they can even act. So, we can provide root cause analysis, we can make prescriptive recommendations and it’s really up to us, the humans, to decide to let the technology go out and execute the optimization itself.”

In contrast to siloed approaches, cognitive systems unify decision-making. For instance, warehouse management traditionally focused on inbound and outbound logistics without considering transport efficiency. Now, AI can evaluate downstream implications and optimize the entire flow accordingly.

Another example is the ability to simultaneously manage demand and supply. Rather than reacting to constrained supply after demand forecasting, cognitive systems balance the two in tandem, maximizing opportunity and responsiveness, according to Usie.

“Today, because we’re operating where we’ve brought all of these capabilities concurrently, we’re now able to have supply chain optimizations that are done simultaneously,” he said. “Therefore, supply and demand are done concurrently. Therefore, I can optimize the demand that I absolutely want to go out and serve and satisfy more cohesively. Same thing for inventory moves in retail, where we’ve had disciplines of allocation and replenishment.”

HJere’s theCUBE’s complete video interview with Wayne Usie, part of the “AI and the Autonomous Supply Chain” interview series:

(* Disclosure: Blue Yonder Group Inc. sponsored this segment of theCUBE. Neither Blue Yonder nor other sponsors have editorial control over content on theCUBE or SiliconANGLE.)

Photo: SiliconANGLE