Principles of Supply Chain Analytics and Optimization
About the Book
Principles of Supply Chain Analytics and Optimization provides a structured and analytically grounded framework for understanding modern supply chain decision-making in an increasingly complex and data-driven environment. As global and regional supply chains face growing challenges such as demand volatility, operational uncertainty, infrastructure constraints, and risk exposure, the role of analytics and optimization has become central to effective management and strategic planning.
The book is designed to bridge the gap between traditional supply chain management theory and quantitative analytical practice. It adopts a progressive approach, beginning with foundational concepts of supply chain management and analytics, and advancing toward specialized domains including demand forecasting, inventory optimization, network design, transportation planning, production and capacity planning, and risk management.
Core analytical frameworks are supported by rigorous modeling techniques and real-world applications. The text systematically develops both theoretical understanding and practical capability, enabling readers to analyze supply chain performance, design optimized systems, and make data-driven decisions across strategic, tactical, and operational levels.
Advanced sections of the book focus on optimization methodologies such as linear and integer programming, nonlinear and dynamic models, metaheuristic approaches including genetic algorithms, particle swarm optimization, and ant colony optimization, as well as simulation-based optimization and decision support systems.
A distinctive feature of this work is its strong emphasis on emerging market contexts, particularly the Indian supply chain environment. It addresses practical challenges such as infrastructure variability, demand uncertainty, regulatory complexity, and fragmented distribution networks, providing contextually relevant analytical solutions.
The book concludes with a forward-looking perspective on digital and intelligent supply chains, examining the transformative role of artificial intelligence, machine learning, big data analytics, cloud computing, Internet of Things (IoT), blockchain, and Industry 4.0 technologies in enhancing visibility, resilience, and performance across supply networks.
Key Features
- Comprehensive coverage of supply chain analytics from foundational concepts to advanced optimization
- Detailed treatment of demand forecasting, inventory optimization, and network design
- Integration of transportation, production planning, and capacity analytics
- Advanced optimization techniques including mathematical programming and metaheuristics
- Focused discussion on supply chain risk analytics and resilience strategies
- Strong emphasis on Indian and emerging market supply chain challenges
- Coverage of digital supply chains, AI, IoT, blockchain, and Industry 4.0 technologies
Target Audience
This book is intended for graduate students, researchers, academicians, and industry professionals in supply chain management, operations research, industrial engineering, and management science. It serves both as an academic textbook and a practical reference for data-driven supply chain decision-making.
Keywords
Keywords: Supply Chain Management, Supply Chain Analytics, Optimization, Demand Forecasting, Inventory Management, Network Design, Transportation Planning, Risk Analytics, Supply Chain Resilience, Mathematical Programming, Metaheuristics, Digital Supply Chain, Artificial Intelligence, Industry 4.0, Emerging Markets
Contents
| Chapter | Title | Page |
|---|---|---|
| Chapter 1 | Foundations of Plant Genetics | 11 |
| 1.1 | Introduction to Plant Genetics | 12 |
| 1.2 | Historical Milestones in Genetic Discovery | 14 |
| 1.3 | Mendelian Principles and Laws of Inheritance | 18 |
| 1.4 | Chromosomal Basis of Heredity | 22 |
| 1.5 | Genetic Variation and Mutation in Plants | 28 |
| 1.6 | Significance of Genetics in Modern Agriculture | 31 |
| Chapter Summary | 39 | |
| Chapter 2 | Molecular Structure and Function of Genes | 57 |
| 2.1 | Structure and Composition of DNA and RNA | 42 |
| 2.2 | Gene Organization in Plant Genomes | 46 |
| 2.3 | DNA Replication, Transcription, and Translation | 52 |
| 2.4 | Gene Expression and Regulation in Plants | 60 |
| 2.5 | Mutation Mechanisms and DNA Repair | 69 |
| 2.6 | Molecular Markers and Genetic Polymorphism | 73 |
| Summary | 80 | |
| Chapter 3 | Genetic Mapping and Molecular Tools | 123 |
| 3.1 | Principles of Linkage and Recombination | 83 |
| 3.2 | Genetic Mapping Techniques | 85 |
| 3.3 | Quantitative Trait Loci (QTL) Mapping | 89 |
| 3.4 | Molecular Marker Systems: RFLP, AFLP, SSR, SNP | 92 |
| 3.5 | Marker-Assisted Selection and Genomic Selection | 96 |
| 3.6 | Applications in Crop Breeding and Diversity Analysis | 100 |
| Summary | 106 | |
| Chapter 4 | Genomics, Transcriptomics, and Epigenetics | 165 |
| 4.1 | Plant Genome Sequencing and Annotation | 109 |
| 4.2 | Comparative and Functional Genomics | 110 |
| 4.3 | Transcriptomics and Gene Expression Profiling | 112 |
| 4.4 | Epigenetic Mechanisms: DNA Methylation and Histone Modifications | 113 |
| 4.5 | Non-Coding RNAs and Gene Regulation | 115 |
| 4.6 | Systems Biology Approaches in Plant Genetics | 117 |
| Summary | 118 | |
| Chapter 5 | Genetic Engineering and Biotechnology Advances | 120 |
| 5.1 | Recombinant DNA Technology in Plants | 121 |
| 5.2 | Gene Cloning and Transformation Methods | 121 |
| 5.3 | Genome Editing Tools: CRISPR, TALENs, and ZFNs | 123 |
| 5.4 | Development of Transgenic and Gene-Edited Crops | 124 |
| 5.5 | Molecular Breeding and Trait Improvement | 126 |
| 5.6 | Ethical, Environmental, and Regulatory Considerations | 126 |
| Summary | 127 | |
| References | References & Bibliography | 128 |
Recommended Citation
APA 7th Edition