Digital Supply Chain Transformation
Implementing Technology, Analytics, and Data-Driven Solutions
- 1st Edition - April 1, 2026
- Latest edition
- Author: Vinaytosh Mishra
- Language: English
Digital Supply Chain Transformation: Implementing Technology, Analytics, and Data-Driven Solutions offers an in-depth exploration of how digital innovation is reshaping modern… Read more
Digital Supply Chain Transformation: Implementing Technology, Analytics, and Data-Driven Solutions offers an in-depth exploration of how digital innovation is reshaping modern supply chain management. The book seamlessly blends theoretical concepts with practical application, ensuring readers not only grasp fundamental supply chain principles but also see how technologies like machine learning, AI, and system dynamics modeling are transforming the industry. Readers are guided through the evolving landscape of supply chains, learning to harness digital tools and analytics for improved efficiency, accuracy, and end-to-end visibility in operations.
This comprehensive resource equips readers to tackle real-world challenges by applying advanced technologies and analytics to supply chain problems. It highlights the growing importance of data-driven decision-making, encourages critical evaluation of strategies and solutions, and anticipates emerging trends and future opportunities. By merging theory with hands-on practice, the book enables professionals to drive innovation and recommend informed improvements in the rapidly advancing field of supply chain management.
This comprehensive resource equips readers to tackle real-world challenges by applying advanced technologies and analytics to supply chain problems. It highlights the growing importance of data-driven decision-making, encourages critical evaluation of strategies and solutions, and anticipates emerging trends and future opportunities. By merging theory with hands-on practice, the book enables professionals to drive innovation and recommend informed improvements in the rapidly advancing field of supply chain management.
- 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
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
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.
- Edition: 1
- Latest edition
- Published: April 1, 2026
- Language: English
VM
Vinaytosh Mishra
Dr. Vinaytosh Mishra is an Associate Professor for Healthcare Management (Management, Digital Health, and Heath Analytics) at the College of Healthcare Management and Economics, Gulf Medical University, Ajman (UAE). He has more than 14 years of experience in industries like Healthcare, EdTech, Finance, and Information Technology. He was instrumental in making a diabetes specialty clinic into a fifty bed multi-specialty clinic in his tenure at Aaroha Healthcare (P) Ltd. In his last Assignment as Associate Professor at FORE School of Management, New Delhi he was program director for two successful long duration programs namely “Building Intelligent Healthcare Analytics” and “PGPM in Healthcare Management and Analytics”. He is also a certified Diabetes Educator and Green Belt in Lean Six Sigma. He has also successfully filled a patent for a device for early detection of diabetes using nailfold capillaroscopy. His research interests include AI in Healthcare, Chronic Disease Care, Healthcare Supply Chain Management, Health Economics, Medical Tourism, Health Technology Assessment, and Digital Health.
Affiliations and expertise
Associate Professor, College of Healthcare Management and Economics, Gulf Medical University, Ajman, UAE