Principles of Supply Chain Analytics and Optimization

About the Book

Supply Chain Analytics and Optimization offers a comprehensive and contemporary exploration of analytical techniques and optimization strategies that drive efficient and resilient supply chains. Designed within a global and data-driven context, the book highlights how analytics transforms traditional supply chain operations into intelligent, adaptive, and performance-oriented systems.

The book begins by establishing foundational concepts of supply chain management, including its evolution, scope, and key drivers. It clearly differentiates between logistics and supply chain functions while emphasizing the growing importance of analytics in decision-making across strategic, tactical, and operational levels.

A major strength of this work lies in its structured coverage of core analytical domains such as demand forecasting, inventory optimization, network design, and transportation planning. It integrates both classical models and modern data-driven approaches, enabling readers to understand variability, uncertainty, and performance measurement in real-world supply chains.

The book further explores advanced optimization techniques including linear programming, simulation models, and metaheuristic algorithms. These approaches are presented alongside practical applications in production planning, capacity management, and logistics optimization, ensuring both theoretical depth and practical relevance.

Special emphasis is placed on emerging technologies such as artificial intelligence, machine learning, big data analytics, Internet of Things (IoT), and blockchain. The integration of these digital tools into supply chain systems is discussed in detail, highlighting their role in enhancing visibility, agility, and resilience in complex global networks.

By combining analytical rigor, technological advancements, and strategic insights, this book serves as a valuable resource for understanding modern supply chain challenges and solutions. It equips readers with the knowledge required to design, analyze, and optimize supply chains in an increasingly dynamic and uncertain environment.

Key Features

Comprehensive coverage of supply chain analytics and optimization techniques
Integration of forecasting, inventory, logistics, and network design models
Application of quantitative methods including linear programming and simulation
Inclusion of advanced techniques such as genetic algorithms and metaheuristics
Focus on real-world supply chain challenges and performance measurement
Detailed discussion on AI, machine learning, IoT, and blockchain in supply chains
Emphasis on risk management, resilience, and sustainable supply chain practices

Target Audience

This book is intended for management students, engineering students, supply chain professionals, operations managers, researchers, academicians, and industry practitioners. It is particularly useful for those seeking to apply analytical and optimization techniques to improve supply chain efficiency, responsiveness, and decision-making.

Keywords

Keywords: Supply Chain Management, Supply Chain Analytics, Demand Forecasting, Inventory Optimization, Logistics Management, Network Design, Optimization Techniques, Linear Programming, Artificial Intelligence, Machine Learning, IoT, Blockchain, Supply Chain Risk, Digital Supply Chains

Chapter 1		8
	Introduction to Supply Chain Management	8
1.1	Evolution and Scope of Supply Chain Management	9
1.2	Supply Chain versus Logistics	10
1.3	Key Supply Chain Drivers and Decision Levels	12
1.4	Role of Analytics in Modern Supply Chains	14
1.5	Supply Chain Performance Challenges in Global Context	17
	Conclusion	20
Chapter 2		22
	Fundamentals of Supply Chain Analytics	22
2.1	Types of Analytics: Descriptive, Diagnostic, Predictive, Prescriptive	23
2.2	Data Sources in Supply Chains	26
2.3	Data Quality, Integration, and Governance	29
2.4	Supply Chain Metrics and Key Performance Indicators	31
2.5	Analytical Maturity Models	34
	Conclusion	37
Chapter 3		39
	Demand Forecasting and Analytics	39
3.1	Demand Patterns and Variability	40
3.2	Qualitative and Quantitative Forecasting Methods	41
3.3	Time Series Models for Demand Forecasting	44
3.4	Forecast Accuracy Measurement	46
3.6	Integration of Forecasting with Supply Chain Planning	52
	Conclusion	54
Chapter 4		57
	Inventory Analytics and Optimization	57
4.1	Inventory Types and Cost Components	58
4.2	Deterministic and Probabilistic Inventory Models	62
4.3	EOQ and Safety Stock Optimization	65
4.4	Multi-Echelon Inventory Systems	68
4.5	Inventory Optimization Using Analytics Tools	71
	Conclusion	75
Chapter 5		78
	Network Design and Supply Chain Configuration	78
5.1	Supply Chain Network Structures	79
5.2	Location-Allocation Models	83
5.3	Transportation and Warehousing Trade-offs	88
5.4	Risk and Resilience in Network Design	93
5.5	Optimization Techniques for Network Planning	97
	Conclusion	102
Chapter 6		105
	Transportation and Logistics Optimization	105
6.1	Transportation Modes and Cost Analysis	106
6.2	Routing and Scheduling Problems	110
6.3	Vehicle Routing Problem (VRP) Models	114
6.4	Last-Mile Logistics Optimization	119
6.5	Green and Sustainable Logistics Analytics	123
	Conclusion	129
Chapter 7		132
	Production and Capacity Planning Analytics	132
7.1	Aggregate Production Planning	133
7.2	Capacity Planning Under Uncertainty	137
7.3	Scheduling Models and Constraints	142
7.4	Lean and Just-In-Time Analytics	146
7.5	Integration of Production and Supply Chain Plans	151
	Conclusion	155
Chapter 8		157
	Supply Chain Risk Analytics and Resilience	157
8.1	Sources of Supply Chain Risk	159
8.2	Risk Identification and Assessment Models	164
8.3	Disruption Modeling and Scenario Analysis	168
8.4	Resilient Supply Chain Strategies	172
8.5	Post-Pandemic Supply Chain Lessons	176
	Conclusion	180
Chapter 9		182
	Optimization Techniques in Supply Chains	182
9.1	Linear and Integer Programming	183
9.2	Nonlinear and Dynamic Optimization Models	188
9.3	Metaheuristics: Genetic Algorithms, PSO, ACO	193
9.4	Simulation-Based Optimization	199
9.5	Decision Support Systems for Optimization	203
	Conclusion	208
Chapter 10		210
	Digital and Intelligent Supply Chains	210
10.1	Role of AI and Machine Learning in Supply Chains	211
10.2	Big Data and Cloud-Based Supply Chain Analytics	218
10.3	IoT, Blockchain, and Visibility Platforms	223
10.4	Industry 4.0 and Smart Supply Chains	229
10.5	Future Research Directions and Global Case Studies	233
	Conclusion	240

Syed Hassan Imam Gardezi

Syed Hassan Imam Gardezi is an accomplished strategist, executive leader, and board member with extensive expertise in leadership, management, governance, technology, and supply chain domains. He has held senior executive and board-level positions across private equity, investment firms, logistics, manufacturing, family offices, and holding/SPV structures. His professional experience spans governance leadership, strategic oversight, compliance, ESG integration, and supply chain–focused investment decision-making.

Dr. Gardezi holds a Ph.D. in Management with a specialization in corporate governance frameworks within private equity and venture capital ecosystems, with a particular focus on emerging markets and the GCC region. His academic background encompasses supply chain management, strategic leadership, international business, accounting, finance, and political science. He has completed advanced academic and professional qualifications across institutions in the United States, United Kingdom, Australia, Cambodia, and Mexico.

His research interests include global supply chain transformation, analytics and optimization, governance and risk frameworks, ESG integration, and digital and AI-enabled decision systems. He actively contributes to academic research and professional practice, focusing on the development of resilient, adaptive, and high-performance supply chains in emerging and global markets.

Dr. Gardezi has published extensively in leading international journals and academic outlets, with contributions covering corporate governance, board effectiveness, ESG integration, risk management, analytics-driven performance, supply chain resilience, hyper-localized supply chains, logistics systems, supply chain trade finance, and emerging technologies such as artificial intelligence, machine learning, big data, blockchain, and additive manufacturing.

His scholarly and professional work has been featured by major academic and industry publishers, including Springer, Elsevier, IEEE, Wiley, IGI Global, Bentham Science, Taylor & Francis, CRC Press, and the American Institute of Physics. He has also presented research at international conferences and contributed to book chapters, theses, and research publications.

Dr. Gardezi is a Fellow of multiple prestigious professional bodies, including the Institute of Supply Chain Management (UK), the Chartered Institute of Logistics & Transport (UK), the Institute of Corporate Responsibility & Sustainability (UK), the Institute of Energy (UK), the Institute of Directors (UK), the Chartered Management Institute (UK), and the Institute of Consulting.

He actively serves on several boards as a Non-Executive Director, providing strategic guidance on supply chain resilience, digital transformation, and sustainable value creation. He is also a sought-after keynote speaker on strategic leadership, integrated logistics, and the future of global supply chains.

Recommended Citation

APA 7th Edition

Syed Hassan Imam Gardezi (2026). Principles of Supply Chain Analytics and Optimization. ISRI Press. doi:https://doi.org/10.1039/ 978-93-47486-66-1

Principles of Supply Chain Analytics and Optimization

Syed Hassan Imam Gardezi

About the Book

Key Features

Target Audience

Keywords

Contents

Syed Hassan Imam Gardezi

Recommended Citation