Data-Driven Innovation in Supply Chains and Manufacturing

From Predictive Analytics to Natural Language Processing
1. Auflage 2026
ISBN: 978-1-041-20922-5
Verlag: Taylor & Francis Ltd

Buch, Englisch, 368 Seiten, Format (B × H): 178 mm x 254 mm, Gewicht: 639 g

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, this 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.

Zielgruppe

Professional Practice & Development

Autoren/Hrsg.

Saxena, Parth

Gottimukkala, Srinivas R.

Bhuram, Shiva Kumar

Joshi, Nipun

Tripathi, Sahil

Fachgebiete

• Wirtschaftswissenschaften Betriebswirtschaft Bereichsspezifisches Management Management: Führung & Motivation
• Wirtschaftswissenschaften Wirtschaftssektoren & Branchen Fertigungsindustrie
• Wirtschaftswissenschaften Betriebswirtschaft Bereichsspezifisches Management Einkauf, Logistik, Supply-Chain-Management
• Wirtschaftswissenschaften Betriebswirtschaft Management Entscheidungsfindung
• Wirtschaftswissenschaften Betriebswirtschaft Management Wissensmanagement
• Wirtschaftswissenschaften Betriebswirtschaft Management Strategisches Management
• Technische Wissenschaften Maschinenbau | Werkstoffkunde Produktionstechnik Fertigungstechnik

Inhaltsverzeichnis

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

Über Autor(innen)

Parth Saxena is a lead software engineer, leading innovation in investment banking technology at a global financial institution. He has spearheaded large-scale digital transformation initiatives across financial services, utilities, energy, and technology, delivering secure, scalable platforms in highly regulated environments. A senior member of IEEE and a Global Fellow at AI2030, Parth is a published author, open-source contributor, and speaker who serves as an advisor to organizations and professionals navigating AI-driven transformation. His recent work focuses on AI-enabled enterprise modernization, intelligent agents, and LLM-based automation, with an emphasis on responsible, production-ready adoption.

Srinivas R. Gottimukkala, with over 25 years of experience, is an accomplished finance and IT leader who specializes in driving business growth and innovation through transformational initiatives across industries. Backed by a strong academic foundation in commerce, law, and project management, they currently lead business process digitization and financial transformation at Deere & Company, delivering significant business value. Their expertise includes SAP Finance solutions, business analysis, and large-scale global implementations, with a proven track record in supply chain and financial management transformations. Known for strong cross-functional collaboration and innovative problem-solving, they are passionate about leveraging SAP, AI, and ML to deliver impactful, future-ready solutions in a rapidly evolving business landscape.

Shiva Kumar Bhuram’s expertise spans over 21 years in SAP development, enterprise integration, and digital transformation, with strong depth in supply chain platforms, real-time execution systems, and cloud-based architectures. Early experience in scientific research produced published work in GPS and ionospheric modeling, establishing a solid analytical foundation. Core strengths include SAP ECC, middleware integration, warehouse automation, and large-scale system orchestration that improves inventory accuracy, process reliability, and operational performance. Technical focus also extends to AI and ML, including federated learning, prescriptive analytics, and secure distributed systems for enterprise environments. This expertise combines hands-on engineering with architectural design to deliver scalable, production-ready solutions. Work emphasizes practical innovation, measurable impact, and the ability to translate complex requirements into resilient platforms that support modern business operations.

Nipun Joshi is a senior product and technology leader with over 15 years of experience in building and scaling data-driven digital platforms. He currently serves as senior director of product management at Fareportal, where he leads digital, loyalty, and personalization initiatives across global travel brands. His work focuses on leveraging AI and analytics to enhance customer experience, recommendation systems, and intelligent service platforms. Nipun has been instrumental in launching large-scale loyalty ecosystems and AI-enabled customer engagement solutions. He holds an MBA from Cornell University along with advanced degrees in computer science and information technology. His professional interests span AI-powered product innovation, customer-centric design, and scalable enterprise systems.

Sahil Tripathi is a Ph.D. student at Jamia Hamdard, New Delhi, where he advances research in artificial intelligence, ML, deep learning, and NLP with a strong focus on making AI systems more explainable, robust, and ethically aligned with human values. His work bridges foundational ML techniques and real-world applications, particularly in large language models and model robustness. Born and raised in India, Sahil’s academic journey includes bachelor’s and master’s degrees in computer applications from Jamia Hamdard, demonstrating a consistent commitment to academic excellence. His research passion is driven by a problem-solving mindset and a creative approach toward addressing complex technological challenges. With strong technical skills in Python, NLP, and ML frameworks, Sahil aims to contribute to socially beneficial AI adoption across critical domains.