The New Manufacturing Value Chain

Name: The New Manufacturing Value Chain
SKU: 978-1-60427-210-9
Availability: BackOrder

Retail Price: $69.95

$59.95

Leveraging AI, Machine Learning, and Advanced Analytics for Success
Arun Gupta, PhD
Hardcover, 7×10, 340 pages
ISBN: 978-1-60427-210-9
e-ISBN: 978-1-60427-865-1
July 2025

Available on backorder

SKU: 978-1-60427-210-9 Categories: Business, Operations Management

Description

We are in the early stages of a transformational period in our economic and digital age that will revolutionize the manufacturing value chain. As this period progresses, there will be opportunities to optimize key activities across manufacturing, supply chain planning, distribution, procurement, and contracting. Organizations that apply Generative AI, business intelligence (BI), machine learning (ML), and advanced analytics will unlock trillions of dollars in economic value, reduce working capital and waste, increase agility, and make better decisions.

Unlike other sources that may address these technologies in isolation or focus on generic applications, The New Manufacturing Value Chain provides a comprehensive framework that integrates these powerful tools into every aspect of manufacturing, from design to after-market service. It explores the many ways in which ML, AI, BI, advanced analytics, and Generative AI are harnessed to gain real-time visibility into production metrics, equipment performance, and supply chain dynamics. By leveraging cutting-edge algorithms and tools, manufacturers can optimize processes and achieve superior performance. This book will show you how. Its practical approach ensures that readers can immediately apply what they learn to their work environments.

Key Features

Covers everything from foundational concepts to advanced applications, making it an all-in-one resource for professionals seeking to understand and implement these technologies in their operations
Tailored to all levels of expertise within manufacturing, especially leadership trying to jump into AI/ML or jumpstart their stalled efforts
Goes beyond theoretical concepts, offering practical, actionable strategies that can be directly applied to real-world manufacturing challenges, thereby enhancing efficiency, reducing costs, and driving innovation
Includes case studies and practical examples from leading manufacturers that demonstrate how to leverage these technologies effectively
Focuses not only on current applications but also explores the future of smart factories, Industry 4.0, and AI-driven manufacturing, including the convergence of AI, IoT, digital twins, and real-time ML decision-making on the production floor

About the author(s)

Dr. Arun P. Gupta is a seasoned expert with over 25 years of experience spanning academia, manufacturing, and management consulting. His work centers on IT, analytics, artificial intelligence (AI), and machine learning (ML), helping organizations harness these technologies to drive digital transformation and business optimization.

Dr. Gupta holds a bachelor’s degree in chemical engineering and a doctorate in computer science engineering from the University of Texas at Arlington. His career has been defined by his ability to bridge the gap between technical innovation and business leadership. Having worked with industry giants such as Weyerhaeuser, IBM, Caterpillar, and Deloitte, he understands the nuanced language required to foster collaboration between programmers, managers, and executives.

His academic contributions include developing and teaching courses in computer science and the application of IT in supply chain management. These early experiences laid the foundation for his transition into the manufacturing sector, where he played a pivotal role in guiding a major industrial manufacturer’s IT and AI-driven transformation. During this time, he pioneered the use of natural language processing models to extract actionable insights from complex documents, marking a significant milestone in his AI journey.

In management consulting, Dr. Gupta has led strategic business transformation initiatives, designed innovative e-business solutions, and spearheaded process development efforts across diverse industries. His recent focus is on leveraging AI, ML, and Generative AI to enhance supply chain operations, optimize business processes, and mitigate environmental and social risks.

For over two decades, Dr. Gupta has been a research associate with the Supply Chain Resource Consortium at North Carolina State University, contributing to the development of a widely recognized supply chain maturity model. A thought leader in his field, he has published influential conference papers and is frequently invited to present at major industry events.

Chapter 1: Why Focus on Manufacturing
Introduction
Manufacturing and Advanced Manufacturing
Definition and Concept of The Manufacturing Value Chain
Key Components of The Manufacturing Value Chain
Summary

Chapter 2: The Manufacturing Value Chain
Introduction
Overview of the Manufacturing Value Chain
Industry 4.0 and Advanced Technologies
Challenges, Opportunities, and Considerations
Case Studies
Future Trends
Summary and Key Insights

Chapter 3: Leveraging Advanced Analytics and Business Intelligence in Manufacturing
Introduction
Common Advanced Analytics Algorithms and BI Tools for the Manufacturing Sector
Data Collection and Preparation in Manufacturing
Applications of Advanced Analytics and BI in Manufacturing
Implementing Advanced Analytics Solutions
Data Security and Governance
Future Trends and Opportunities
Summary and Key Insights

Chapter 4: Common Algorithms, Tools, and Frameworks in AI/ML for Manufacturing
Machine Learning Algorithms in Manufacturing
Artificial Intelligence Algorithms in Manufacturing
AI Tools and Frameworks in Manufacturing
Summary and Key Insights

Chapter 5: Product Design and Development in Manufacturing
Fundamentals of Product Design and Development
Leveraging Advanced Analytics and BI in Product Design and Development
Utilizing Business Intelligence in Product Design and Development
Utilizing AI/ML in Product Design and Development
Summary and Key Insights

Chapter 6: Leveraging Advanced Analytics, BI, ML, and AI in Procurement
Understanding Data Sources and Integration
Role of Advanced Analytics and BI in Raw Material Procurement
AI and ML Applications in Raw Material Procurement
Advanced Analytics, BI, ML, and AI in Cost Optimization
Risk Management and Compliance
Data Governance and Security
Managing Procurement Processes
Future Trends and Opportunities
Summary and Key Insights

Chapter 7: Incorporating Advanced Analytics, BI, ML, and AI in Manufacturing and Production
Understanding Manufacturing and Production Processes
Understanding Data Sources and Integration
Key Algorithm Classes Used in Manufacturing and Production
Application of Advanced Analytics and BI in Manufacturing and Production – Enhancing Operational Visibility and Performance
Harnessing ML and AI in Manufacturing and Production
Advanced Analytics, BI, ML, and AI in Supply Chain Management: Driving Efficiency and Optimization
Implementation Challenges and Best Practices
Future Trends and Outlook
Summary and Key Insights

Chapter 8: Leveraging Advanced Analytics, BI, ML, and AI in Distribution and Logistics
Understanding Data Sources and integration
Application of Advanced Analytics and BI in Distribution
Harnessing ML and AI for Route Optimization
Optimization of Warehouse Operations With AI
Sustainability and Risk Mitigation
Implementation Challenges and Best Practices
Future Trends and Outlook
Summary and Key Insights

Chapter 9: Advanced Analytics, BI, ML, and AI in Sales & Marketing and After-Sales Service
Understanding Data Sources and Integration
Application of Advanced Analytics and BI in Sales and Marketing and After-Sales Support
ML and AI in Sales and Marketing and After-Sales Support
Predictive Analytics for Sales Forecasting and Service
Optimizing Marketing Campaigns and Customer Support With AI
Sustainability and Ethical Considerations
Implementation Challenges and Best Practices
Future Trends and Outlook
Summary and Key Insights

Chapter 10: Unleashing the Potential of Advanced Analytics, BI, ML, and AI in Manufacturing
Summarizing Key Insights
Algorithm Usage and Trends
Broader Implications
Inspiring Action
Call To Action

References
Index