Supply Chain AI-JDA Software and KPMG LLP recently published a wide-ranging survey regarding supply-chain technology. The main takeaway: end-to-end visibility is the No. 1 priority. But in order to make this a reality, the survey also notes that AI (Artificial Intelligence), machine learning (ML) and cognitive analytics will be critical.
Yet pulling this off is far from easy and fraught with risks. So what to do? Well, I recently had a chance to talk to Dr. Michael Feindt. A physicist by education, he has used his strong mathematical skills to focus on AI. He developed the NeuroBayes algorithm while at the scientific research center at CERN and founded Blue Yonder in 2008 to apply his theories to supply-chain management. And yes, the company got lots of traction, as the platform would eventually deliver 600 million intelligent, automated decisions every day. Then in 2018 JDA Software acquired Blue Yonder.
No doubt, when it comes to applying AI and the supply chain, Michael is definitely someone to listen to.
“The self-learning supply chain marks the next major frontier of supply chain innovation,” he said. “It’s a futuristic vision of a world in which supply chain systems, infused with AI and machine learning (ML), can analyze existing strategies and data to learn what factors lead to failures. Because of recent advancements in technology, the autonomous supply chain is no longer ‘blue-sky thinking.’”
OK then, so let’s take a look at some of his recommendations:
The System Must Read Signals and Manage Billions of Pieces of Information: You need to process as many signals as possible to get a complete picture, such as weather events, temperatures, social trends and so on. For example, by using weather forecasts and port congestion data, it’s possible to predict the impact on freighters in route and determine which shipments will be late — and the captain may not even know what’s happening!