Blue Yonder Finalizes $839 Million One Network Purchase

Supply chain solutions provider Blue Yonder’s $839 million acquisition of One Network Enterprises is complete.

The deal, announced Thursday (Aug. 1), lets Blue Yonder customers collaborate and share data on things like inventory levels and materials movement in real time across all trading partners up and down the supply chain.

“It’s no longer sufficient for companies to rely on periodic updates about their inventory, capacity, and resources; true business agility requires real-time information across an entire network,” the company said in a news release.

“With the addition of One Network’s commercial technology, Blue Yonder can now offer customers a multi-enterprise, multi-tier network ecosystem; artificial intelligence (AI)-powered supply chain assistants to identify, monitor, analyze, and resolve problems; and a simplified process to onboard and work with trading partners.”

Blue Yonder in March announced its plans to acquire One Network to create a unified end-to-end supply chain platform and collaboration ecosystem.

“Supply chains have become more complex, and as more and more companies reduce risk by diversifying sourcing of products globally, there is an increased demand for the sharing of information and resources across the whole value chain,” Blue Yonder CEO Duncan Angove said at the time.

In other supply-chain-related news, PYMNTS last week examined reinforcement learning (RL), a subset of machine learning, and its role in industries like eCommerce.

Reinforcement learning mimics how humans learn through experience, with an AI model interacting with its environment, acting and receiving feedback via rewards or penalties to learn which actions lead to the best outcomes.

RL, that report said, is positioned to play an increasingly crucial part in shaping the future of commerce. From supply chain optimization to personalized marketing, the tech has the potential to promote efficiencies and create new capabilities across the business world.

“We may soon see RL systems managing entire supply chains and dynamically adjusting to global events and market shifts,” PYMNTS wrote.

“In retail, advanced RL algorithms could create hyper-personalized shopping experiences, predicting customer needs before they even arise. RL could lead to more sophisticated risk management tools and trading strategies in the financial sector, potentially increasing market stability while creating new challenges for regulators.”

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