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/https://libri-media.knygos-static.lt/a2cb7531-7cdc-4be8-ad75-094f3fd01321/2)
- Leidėjas: Taylor & Francis
- Metai: 2026
- Puslapiai: 368
- ISBN-10: 1041209223
- ISBN-13: 9781041209225
- Kalba: Anglų
Data-Driven Innovation in Supply Chains and Manufacturing (el. knyga) (skaityta knyga) | knygos.lt
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Aprašymas
This book explores how modern technologies-especially data analytics, machine learning (ML), and the Internet of Things (IoT)-are transforming supply chain and manufacturing operations. Bridging academic research and industrial practice, the book presents data as a strategic asset driving agility, efficiency, and resilience.
Structured around four themes, it covers:
-- Foundational analytics and optimization
-- Predictive and prescriptive analytics for proactive decision-making
-- Real-time IoT data for workflow monitoring and control
-- Digital Twins and Natural Language Processing (NLP) for modeling and interaction
Chapters include mathematical modeling, case studies, and implementation frameworks, with coverage spanning stochastic forecasting, reinforcement learning, anomaly detection, and semantic parsing of logistics documentation.
Key benefits include its emphasis on integrated intelligence-blending ML, IoT, and simulation for real-time, predictive insights. It also highlights scalability across industries, with tools adaptable to sectors like automotive, healthcare, and aerospace. Each chapter concludes with open problems and future directions, offering a roadmap for innovation in intelligent operations.
- Leidėjas: Taylor & Francis
- Metai: 2026
- Puslapiai: 368
- ISBN-10: 1041209223
- ISBN-13: 9781041209225
- Kalba: Anglų
This book explores how modern technologies-especially data analytics, machine learning (ML), and the Internet of Things (IoT)-are transforming supply chain and manufacturing operations. Bridging academic research and industrial practice, the book presents data as a strategic asset driving agility, efficiency, and resilience.
Structured around four themes, it covers:
-- Foundational analytics and optimization
-- Predictive and prescriptive analytics for proactive decision-making
-- Real-time IoT data for workflow monitoring and control
-- Digital Twins and Natural Language Processing (NLP) for modeling and interaction
Chapters include mathematical modeling, case studies, and implementation frameworks, with coverage spanning stochastic forecasting, reinforcement learning, anomaly detection, and semantic parsing of logistics documentation.
Key benefits include its emphasis on integrated intelligence-blending ML, IoT, and simulation for real-time, predictive insights. It also highlights scalability across industries, with tools adaptable to sectors like automotive, healthcare, and aerospace. Each chapter concludes with open problems and future directions, offering a roadmap for innovation in intelligent operations.
Atsiliepimai