『Manufacturing AI』のカバーアート

Manufacturing AI

Building the Data Foundation for the Next Industrial Revolution

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期間限定:2025年10月14日(日本時間)に終了
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Manufacturing AI

著者: Ryan Andrew Hill, Jeff Knepper - foreword
ナレーター: Brian Arens
タイトルを¥2,170 で購入し、プレミアムプランを2か月間無料で試す

無料体験終了後は月額1,500円で自動更新します。いつでも退会できます。

¥3,100 で購入

¥3,100 で購入

このコンテンツについて

Turn Manufacturing Data into a Scalable Competitive Advantage

Factories 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 learn

  • Design Common Data Models (CDMs) that unify complex, multi-system data
  • Use edge federation for low-latency, on-site decisions
  • Build a Unified Data Layer (UDL) that powers analytics and LLMs
  • Apply data contracts for quality, security, and compliance
  • Deploy Edge Intelligence Hubs and agentic AI/LLM routing
  • Connect digital threads/twins to real-time operations

Potential outcomes when UMDA is implemented well

  • Identify failure patterns earlier and plan maintenance proactively
  • Catch quality drift in real time and reduce scrap/rework
  • Synchronize planning with live constraints for fewer schedule breaks
  • Shorten time-to-value by standardizing data and integrations
  • Share proven improvements across sites with less friction

Who it’s for: 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.

PLEASE NOTE: When you purchase this title, the accompanying PDF will be available in your Audible Library along with the audio.

©2025 UMDA Publishing (P)2025 UMDA Publishing
コンピュータサイエンス 工学 工業・製造業 機械理論・人工知能
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