Manufacturing data and AI: Connecting the product value chain
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A manufacturing defect rarely belongs to one system. A scrap spike may relate to a machine setting, a supplier batch, a logistics event, or a recurring issue recorded in a quality system. Yet the data needed to investigate it is usually split across plant, functional, and system boundaries.
More than 50 years ago, Dr. Joseph Harrington’s vision of Computer Integrated Manufacturing (CIM) recognized that manufacturing depends on a connected flow of information across functions. Today, that vision is becoming practical as data and AI connect the stages of the product value chain.
The hardest manufacturing questions are cross-stage questions:
Answering any of these requires joining data from systems that were never designed to talk to each other. Manufacturers do not need more isolated reports. They need a connected flow of information across the product value chain, with the governance and business context to make that information usable. This is the role a modern Data and AI Platform can play.
What is the manufacturing product value chain?
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