- ホーム
- > 洋書
- > 英文書
- > Business / Economics
Full Description
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.
Contents
Chapter 1: Introduction to Data-Driven Supply Chains and Manufacturing Chapter 2: Fundamentals Of Supply Chain Analytics Chapter 3: Predictive Analytics And Real-Time Decision-Making Chapter 4: Internet Of Things (Iot) In Manufacturing And Logistics Chapter 5: Natural Language Processing (Nlp) For Supply Chains Chapter 6: Digital Twins: Concept And Applications Chapter 7: Machine Learning Algorithms For Supply Chain Optimization Chapter 8: Integrating Data-Driven Technologies for End-to-End Supply Chain Transformation
