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Manufacturing AI: Building the Data Foundation for the Next Industrial Revolution
Turn Manufacturing Data into a Scalable Competitive AdvantageFactories create massive amounts of data from IoT sensors, MES, SCADA, quality systems, supply chain, maintenance, and others. Yet most organizations can’t turn it into decisions fast enough. The result is reactive firefighting and AI pilots that stall.
The Unified Manufacturing Data Architecture (UMDA) is a practical framework built for industry, not adapted from IT. It shows you how to handle real-time streams, integrate legacy systems, enforce security, and scale AI across sites.
What you’ll learnDesign Common Data Models (CDMs) that unify complex, multi-system dataUse edge federation for low-latency, on-site decisionsBuild a Unified Data Layer (UDL) that powers analytics and LLMsApply data contracts for quality, security, and complianceDeploy Edge Intelligence Hubs and agentic AI/LLM routingConnect digital threads/twins to real-time operations
Potential outcomes when UMDA is implemented wellIdentify failure patterns earlier and plan maintenance proactivelyCatch quality drift in real time and reduce scrap/reworkSynchronize planning with live constraints for fewer schedule breaksShorten time-to-value by standardizing data and integrationsShare proven improvements across sites with less frictionWho it’s Plant leaders, manufacturing engineers, OT/IT architects, data/AI teams, and executives driving Industry 4.0.
Stop drowning in data. Build an AI-ready architecture that anticipates, adapts, and continuously improves by turning information into measurable results.
The Unified Manufacturing Data Architecture (UMDA) is a practical framework built for industry, not adapted from IT. It shows you how to handle real-time streams, integrate legacy systems, enforce security, and scale AI across sites.
What you’ll learnDesign Common Data Models (CDMs) that unify complex, multi-system dataUse edge federation for low-latency, on-site decisionsBuild a Unified Data Layer (UDL) that powers analytics and LLMsApply data contracts for quality, security, and complianceDeploy Edge Intelligence Hubs and agentic AI/LLM routingConnect digital threads/twins to real-time operations
Potential outcomes when UMDA is implemented wellIdentify failure patterns earlier and plan maintenance proactivelyCatch quality drift in real time and reduce scrap/reworkSynchronize planning with live constraints for fewer schedule breaksShorten time-to-value by standardizing data and integrationsShare proven improvements across sites with less frictionWho it’s Plant leaders, manufacturing engineers, OT/IT architects, data/AI teams, and executives driving Industry 4.0.
Stop drowning in data. Build an AI-ready architecture that anticipates, adapts, and continuously improves by turning information into measurable results.
306 pages, Kindle Edition
Published August 21, 2025
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Displaying 1 - 3 of 3 reviews
September 4, 2025
Actionable, not academic. I could hand the local data mapping chapter to my controls team and start next week. Hill explains how to keep deterministic loops on the line while using analytics for real-time quality and predictive maintenance. The case study on catching drift before scrap was painfully familiar, but in a good way. Furthermore, I appreciated the people side by including change management, role clarity, and how to sequence upgrades without stopping production.
The data lineage and context emphasis is outstanding. I also liked the direct language around costs and latency such as what stays local, what goes to cloud, what’s batched. The tactical approach to predictive maintenance and AI enabled scheduling are the best I’ve seen. We’re now using the checklists to prep our first PoC line.
The data lineage and context emphasis is outstanding. I also liked the direct language around costs and latency such as what stays local, what goes to cloud, what’s batched. The tactical approach to predictive maintenance and AI enabled scheduling are the best I’ve seen. We’re now using the checklists to prep our first PoC line.
October 3, 2025
My consulting firm works with mid-market manufacturers on operational improvements. This book gave us a structured methodology for data and AI projects that we didn't have before. Now I have the knowledge to ask the right questions during due diligence.
The Norman and Rita scenarios illustrate exactly what good versus poor data management looks like in practice. I'd honestly love a whole book focused on their experiences with these concepts, as this really does a great job of bringing the concepts into real-world scenarios in a relatable manner.
The step-by-step implementation guide in Chapter 9 has become our standard approach. We've shortened project timelines because we're not reinventing frameworks for each engagement. Hill has done the hard work of codifying best practices that we can apply immediately.
The Norman and Rita scenarios illustrate exactly what good versus poor data management looks like in practice. I'd honestly love a whole book focused on their experiences with these concepts, as this really does a great job of bringing the concepts into real-world scenarios in a relatable manner.
The step-by-step implementation guide in Chapter 9 has become our standard approach. We've shortened project timelines because we're not reinventing frameworks for each engagement. Hill has done the hard work of codifying best practices that we can apply immediately.
October 5, 2025
Most books talk about use cases. This one shows you how to build the plumbing so use cases actually work.
Manufacturing AI is the rare audiobook that treats the factory as it is: mixed vintages, mixed vendors, and real constraints. The UMDA framework lays out a clear path from edge signals to governed enterprise analytics to agentic AI, with data contracts and feedback loops tying it together.
Essential for anyone doing AI in manufacturing.
Manufacturing AI is the rare audiobook that treats the factory as it is: mixed vintages, mixed vendors, and real constraints. The UMDA framework lays out a clear path from edge signals to governed enterprise analytics to agentic AI, with data contracts and feedback loops tying it together.
Essential for anyone doing AI in manufacturing.
Displaying 1 - 3 of 3 reviews