Digital Supply Chain Transformation
Implementing Technology, Analytics, and Data-Driven Solutions
- 1st Edition - April 23, 2026
- Latest edition
- Author: Vinaytosh Mishra
- Language: English
Digital Supply Chain Transformation: Implementing Technology, Analytics, and Data-Driven Solutions delves into the intricate world of supply chain management, emphasizing the ro… Read more
Description
Description
Digital Supply Chain Transformation: Implementing Technology, Analytics, and Data-Driven Solutions delves into the intricate world of supply chain management, emphasizing the role of digital transformation in modern supply chains. Through a blend of theoretical learning and practical applications, readers will gain a deep understanding of foundational supply chain principles while exploring emerging trends and technologies reshaping the industry. Topics such as system dynamics modelling, machine learning, artificial intelligence, and end-to-end visibility are explored in-depth, equipping readers with the tools and knowledge needed to excel in the rapidly evolving landscape of supply chain management.
Readers will learn how comprehend core principles and elements of supply chain management and its pivotal role in businesses and industries, recognize the significance of digital transformation in supply chains, understand the tools, technologies, and strategies essential for a successful transformation, evaluate the importance of end-to-end supply chain visibility, employ methods and technologies to enhance this visibility in practical scenarios, and apply system dynamics modeling techniques to address complex supply chain problems to optimize supply chain processes, and much more.
Readers will learn how comprehend core principles and elements of supply chain management and its pivotal role in businesses and industries, recognize the significance of digital transformation in supply chains, understand the tools, technologies, and strategies essential for a successful transformation, evaluate the importance of end-to-end supply chain visibility, employ methods and technologies to enhance this visibility in practical scenarios, and apply system dynamics modeling techniques to address complex supply chain problems to optimize supply chain processes, and much more.
Key features
Key features
- Covers system dynamics modeling techniques to address complex supply chain problems and optimize supply chain processes
- Discusses the potential of machine learning and artificial intelligence in supply chains, along with application of these technologies to innovate and solve real-world supply chain challenges
- Presents theories and examples on supply chain analytics and tools to make data-driven decisions for enhancing efficiency and reducing operational costs
Readership
Readership
Supply chain management, logistics and digital business researchers and graduate students. Supply chain leaders, supply chain and logistics managers, data scientists, AI/ML researchers, engineers, and consultants
Table of contents
Table of contents
1: Foundations in Supply Chain Management
1.1 Introduction to Supply Chain Management (SCM)
1.2 Key components and processes in SCM
1.3 Evolution and history of SCM
1.4 Role of SCM in businesses and industries
1.5 Challenges and opportunities in modern SCM
2: Framework for Digital Supply Chain Transformation
2.1 Understanding digital transformation in SCM.
2.2 Steps to initiate digital supply chain transformation.
2.3 Role of technologies in digital SCM
2.4 Barriers and challenges in digital transformation
2.5 Case studies: Successful digital supply chain transformations
3: End-to-End Supply Chain Visibility
3.1 Importance of supply chain visibility
3.2 Technologies enabling end-to-end visibility.
3.3 Benefits and ROI of enhanced visibility
3.4 Challenges in implementing end-to-end visibility.
3.5 Best practices and solutions for improving visibility.
4: System Dynamics Modelling for Supply Chain
4.1 Basics of system thinking and system dynamics
4.2 Application of system dynamics in SCM
4.3 Tools and software for system dynamics modelling.
4.4 Case studies: Using system dynamics for supply chain optimization.
4.5 Limitations and future prospects
5: Supply Chain Analytics and Tools
5.1 Introduction to supply chain analytics
5.2 Different types of analytics: Descriptive, predictive, prescriptive
5.3 Tools and software for supply chain analytics
5.4 Use cases: Improving efficiency and reducing costs through analytics.
5.5 Limitations and potential pitfalls
6: Machine Learning in Supply Chains
6.1 Basics of machine learning and its relevance to SCM
6.2 Applications: Demand forecasting, inventory management, route optimization
6.3 Tools and platforms for implementing machine learning in SCM.
6.4 Challenges and limitations
6.5 Case studies: Success stories and lessons learned.
7: AI Applications in Supply Chains
7.1 Introduction to AI and its potential in SCM
7.2 Applications: Chatbots, cognitive automation, and procurement
7.3 Tools and platforms for AI in SCM
7.4 Ethical considerations and challenges
7.5 Future trends and predictions
8: Emerging Trends in Supply Chain Management
8.1 Recent innovations and technologies
8.2 Sustainability and green supply chain practices
8.3 The impact of global geopolitics on SCM
8.4 The future of work and SCM
8.5 Preparing for the future: Agility, resilience, and adaptability.
1.1 Introduction to Supply Chain Management (SCM)
1.2 Key components and processes in SCM
1.3 Evolution and history of SCM
1.4 Role of SCM in businesses and industries
1.5 Challenges and opportunities in modern SCM
2: Framework for Digital Supply Chain Transformation
2.1 Understanding digital transformation in SCM.
2.2 Steps to initiate digital supply chain transformation.
2.3 Role of technologies in digital SCM
2.4 Barriers and challenges in digital transformation
2.5 Case studies: Successful digital supply chain transformations
3: End-to-End Supply Chain Visibility
3.1 Importance of supply chain visibility
3.2 Technologies enabling end-to-end visibility.
3.3 Benefits and ROI of enhanced visibility
3.4 Challenges in implementing end-to-end visibility.
3.5 Best practices and solutions for improving visibility.
4: System Dynamics Modelling for Supply Chain
4.1 Basics of system thinking and system dynamics
4.2 Application of system dynamics in SCM
4.3 Tools and software for system dynamics modelling.
4.4 Case studies: Using system dynamics for supply chain optimization.
4.5 Limitations and future prospects
5: Supply Chain Analytics and Tools
5.1 Introduction to supply chain analytics
5.2 Different types of analytics: Descriptive, predictive, prescriptive
5.3 Tools and software for supply chain analytics
5.4 Use cases: Improving efficiency and reducing costs through analytics.
5.5 Limitations and potential pitfalls
6: Machine Learning in Supply Chains
6.1 Basics of machine learning and its relevance to SCM
6.2 Applications: Demand forecasting, inventory management, route optimization
6.3 Tools and platforms for implementing machine learning in SCM.
6.4 Challenges and limitations
6.5 Case studies: Success stories and lessons learned.
7: AI Applications in Supply Chains
7.1 Introduction to AI and its potential in SCM
7.2 Applications: Chatbots, cognitive automation, and procurement
7.3 Tools and platforms for AI in SCM
7.4 Ethical considerations and challenges
7.5 Future trends and predictions
8: Emerging Trends in Supply Chain Management
8.1 Recent innovations and technologies
8.2 Sustainability and green supply chain practices
8.3 The impact of global geopolitics on SCM
8.4 The future of work and SCM
8.5 Preparing for the future: Agility, resilience, and adaptability.
Product details
Product details
- Edition: 1
- Latest edition
- Published: April 28, 2026
- Language: English
About the author
About the author
VM
Vinaytosh Mishra
Dr. Vinaytosh Mishra is an Associate Professor and Associate Dean at the Thumbay College of Management and AI in Healthcare, Gulf Medical University, UAE. He holds a PhD in Healthcare Supply Chain Management and has completed postdoctoral fellowships in AI in Healthcare and Ethical AI. With more than 19 years of experience spanning information technology, manufacturing, finance, healthcare, and education, he has held leadership roles including CEO of a multispecialty hospital. His research and teaching focuses on supply chain management, operations management, business analytics, AI-enabled decision support, digital transformation, optimization, simulation, Lean Six Sigma, and digital twins. He is an inventor on one Australian and two German patents in AI for healthcare and serves as a domain expert for AI implementation in healthcare at AHB.ai in Sharjah, UAE. He has designed and launched successful undergraduate and postgraduate programs in healthcare management and AI in healthcare.
Affiliations and expertise
College of Healthcare Management and Economics, Gulf Medical University, Ajman, UAEView book on ScienceDirect
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