We Won’t Get Fooled Again: De-Risking AI/ML in the Supply Chain

What really works when applying advanced technology in today’s evolving logistics industry.

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*This content is sponsored by SAP*

Gartner’s latest Hype Cycle for emerging technologies places much of today’s most touted AI/ML technology at the apex of inflated expectations. Certainly, the promise of AI/ML is tantalizing, but who wants to invest in these up-and-coming technologies, only to find out that the money was spent on nothing more than another round of vaporware?

Meet the new boss. Same as the old boss. Really?

Supply & Demand Chain Executive recently hosted “Navigating the Future: Transforming Supply Chain Networks through Digital Innovation,” a one-hour panel discussion featuring Kellen Betts, Course Lead and Research Project Manager at MIT’s Center for Transportation and Logistics, Cory Hughes, SAP Regional Director, and Stanford Huynh, SAP Business Network Director.

The webinar attempted to sort out what’s really working in the areas of AI/ML in today’s supply chain environment, and what to steer clear of to avoid wasting money.

“Much of what’s been proven in the realm of AI/ML lately has occurred in the consumer world,” said Hughes, who has led IT transformations involving integrated business planning and supply chain collaboration platforms as a planning practitioner. “We’re just now starting to see how AI/ML can impact value streams across the supply chain, especially in the area of transportation logistics.”

Final mile delivery offers just one example. Complex routing problems, such as the final mile in urban areas, can be computationally difficult to solve using more traditional techniques. However, recent research into solving these problems using ML is showing significant promise. More efficient solutions would allow logistics companies to deliver packages faster, while using less fuel and inflicting less wear-and-tear on trucks.

In the warehouse, increasingly sophisticated robots are now handling the movement of goods, with advanced systems coordinating their activities using machine vision, AI/ML, and other technologies.

“While AI may be in a hype cycle with the introduction of powerful new generative AI tools,” said Betts, who has been researching new ways to apply advanced technology in the logistics sector, “machine learning has a well-established track record in the supply chain, earning it a promising place in future applications. I have no doubt these new generative AI tools will transform knowledge work in supply chains, and some of the underlying technology advances will carry over to operational applications, as well.”

Prior to COVID, supply chain operators were content to take a wait-and-see approach. But as the labor market tightened, direct deliveries increased, and raw materials grew scarce, injecting digital technologies into the value stream became critical. Knowing what buttons to push is now a concern across the industry.

“Demand forecasting has been a highlight of recent AI/ML success stories in retail markets worldwide,” said Huynh, whose focus at SAP is to help companies create operational efficiencies. “This is one of those technologies that can instantly impact the bottom line of an operator and separate them quickly from the competition.”

So, as we tip our hat to the new constitution, the lesson to learn is this: Understand how to navigate the future of AI/ML and associated digital technologies that are increasingly automating supply chain activities and improving decisions that impact customer satisfaction in our ever-evolving global marketplace.

Visit Supply & Demand Chain Executive now to view the webinar on demand.