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AI for Supply Chain: Use Cases – Part Three

This article describes a ‘rethinking of AI’, looking at data first, establishing a hierarchy of data stores, ‘best fit’ capabilities, and reducing nervousness in supply chain decision making.

AI for Supply Chain: Use Cases – Part Two

We look at the use of AI for demand planning and forecasting, as well as inventory management and making MEIO (multi-echelon inventory optimization) work better.

AI for Supply Chain: Debunking the Myths – Part One

Previously intractable challenges that supply chain professionals have been grappling with for decades are now within reach using AI/ML. These include fixing new product introductions, stock-outs, optimizing inbound through outbound fulfillment, and improving profit margins.

AI Is Reshaping Supply-Chain Jobs and Services

AI and ML are not taking over your job. But they will change things. To avoid digital displacement, employees have to prepare themselves now to be the leaders of using these capabilities, and the advisers to their companies based on the new insights.

Solving Multi-Enterprise Challenges With Supply-Chain Application Networks

Supply Chain Application Networks provide more than just multi-enterprise messaging and supply chain visibility. Multi-enterprise planning and execution functionality is built into these networks, in areas such as transportation, sourcing and procurement, demand collaboration, and outsourced production management.

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