Call for Chapters: Supply Chain Transformation Through Generative AI and Machine Learning

Editors

Ehap Sabri, University of Texas at Dallas, United States

Call for Chapters

Proposals Submission Deadline: May 21, 2024
Full Chapters Due: September 7, 2024
Submission Date: September 7, 2024

Introduction

The transformative role of Generative AI and Machine Learning (ML) in supply chain management is increasingly being recognized as a game-changer in the industry. Recent statistics underscore this trend, highlighting the rapid adoption and significant impact of these technologies. The AI in supply chain market to reach an impressive $21.8 billion by 2027, at a compound annual growth rate of 45.3%. This growth is attributed to the escalating demand for AI-driven analytics and real-time operation management. EY's CEO Outlook for January 2024 underscores a significant interest among CEOs in transforming their businesses, with 95% planning to maintain or accelerate their transformational change in 2024. This transformation includes leveraging AI and focusing on financial operations for enhanced efficiencies. According to EY, companies leveraging these technologies are better equipped to respond to market changes and supply chain disruptions, benefiting from improved forecasting accuracy and decision-making processes. Moreover, A McKinsey study highlighted that early adopters of AI in supply chain management have seen significant improvements, including a 15% reduction in logistics costs and substantial increases in inventory levels and service levels compared to their slower-moving competitors However, the path to digital (Gen A/ML) transformation is not without its challenges. Despite improved success rates, about 60% of digital transformation initiatives in supply chains still struggle to fully meet their objectives. This shortfall is often attributed to several key factors: the complexity and scale of integrating new technologies into existing systems; organizational resistance to change and inadequate stakeholder buy-in; lack of skilled professionals adept in these new technologies; insufficient data governance and quality; and underestimation of the need for a robust change management strategy. These challenges highlight the critical need for a comprehensive approach that addresses both the technical and human aspects of digital transformation. This book is timely and essential, offering a comprehensive guide to best practices in digital enablement, change management, and process optimization, with a specific focus on Generative AI and ML. It seeks to equip readers with the knowledge and strategies necessary for successful integration of these technologies, drawing on the latest industry insights and expert recommendations, to enhance supply chain efficiency & effectiveness, reduce costs, and drive revenue growth.

Objective

This book is meticulously crafted to serve as a pivotal resource in the rapidly evolving domain of digital supply chain management. It synthesizes the latest best practices and cutting-edge research in digital supply chain enablement, offering business professionals an indispensable guide to navigate and excel in supply chain business transformation initiatives. In an era where digitalization of the supply chain has become a top priority for a majority of executives, as evidenced by several recent surveys, there is a pressing need for a resource that not only collates theoretical insights but also bridges them with real-world applications and proven strategies. This publication meets that need by going beyond the limited literature currently available in the field of digital supply chain optimization and business transformation. It fills a critical gap by providing comprehensive coverage of the subject, enriched with practical tactics and case studies drawn from the industry. The objectives of this book are multi-fold: 1. To Educate: Offering a thorough understanding of the principles, strategies, and technologies underpinning the digital supply chain, particularly the roles of Generative AI and ML. 2. To Guide: Providing actionable insights and step-by-step guidance on implementing digital transformation strategies effectively in supply chain operations. 3. To Innovate: Encouraging innovative thinking by showcasing successful case studies and emerging trends in digital supply chain management. 4. To Address Challenges: Identifying common pitfalls and challenges in digital supply chain transformation and offering solutions and best practices to overcome them. 5. To Inspire: Inspiring business professionals and decision-makers to rethink traditional supply chain processes and embrace digital transformation for enhanced efficiency, sustainability, and competitiveness. By achieving these objectives, this book aims to be an essential reference, equipping readers with the knowledge, skills, and confidence to lead successful digital transformations in their supply chain operations. It is an invaluable asset for business professionals, supply chain managers, and students who aspire to be at the forefront of the digital supply chain revolution.

Target Audience

The target audience for this book is meticulously chosen to ensure it thoroughly addresses the intricacies of Generative AI and Machine Learning in supply chain management, catering to the needs of a diverse range of professionals and students in this rapidly evolving field. 1. Executives and Business Leaders: This book is particularly valuable for executives and business leaders who are steering their organizations through the integration of Generative AI and ML in supply chains. It provides deep insights into the strategic aspects of these technologies, guiding leaders in understanding the phases of AI and ML implementation, evaluating the business impacts, and effectively supporting and monitoring progress. It is an indispensable tool for those making critical decisions on adopting these advanced technologies. 2. Supply Chain and AI/ML Program Managers: For professionals directly involved in the implementation and management of Generative AI and ML initiatives in supply chains, this book serves as a comprehensive guide. It offers detailed best practices in the integration of these technologies, covering aspects of process optimization, data governance, and technological infrastructure. A significant focus is given to change management, crucial for ensuring the successful adoption of AI and ML in existing supply chain processes. 3. Graduate and MPA Students Specializing in AI and ML: This book is an excellent resource for graduate and MPA students focusing on AI and ML applications in supply chain management. It bridges the gap between academic research and real-world application, providing students with a robust understanding of how Generative AI and ML transform supply chain operations. It is perfect for those looking to extend research in this cutting-edge field or to apply these concepts in industry practices. 4. AI and ML Developers, Data Scientists: This book also targets professionals like AI developers and data scientists who are at the heart of creating and implementing these technologies. It provides insights into the practical challenges and opportunities of applying AI and ML in supply chains, enhancing their ability to develop solutions that are both innovative and effective in real-world scenarios. 5. Industry Analysts and Consultants in AI/ML: Industry analysts and consultants specializing in AI and ML will find this book to be a valuable asset. It equips them with the latest trends and knowledge in AI and ML applications in supply chains, enabling them to offer more informed and strategic advice to their clients. 6. Technology Enthusiasts and Innovators in AI/ML: Lastly, for technology enthusiasts and innovators who are passionate about AI and ML, this book offers a wealth of information on the latest developments and future trends in the field. It’s an excellent resource for staying abreast of how these technologies are transforming supply chain management. Overall, this book is tailored to address the needs of a broad spectrum of readers, from business leaders to technical professionals, all of whom play a pivotal role in the adoption and advancement of Generative AI and Machine Learning in supply chain.

Recommended Topics

1. Emerging Trends in Generative AI and ML for Supply Chains: Exploring the latest advancements and market dynamics in AI and ML technologies that are shaping the future of supply chains. 2. Navigating Challenges in AI and ML-Driven Transformation: Identifying common obstacles encountered during digital transformation with a focus on AI and ML integration, and strategies to overcome them. 3. Optimizing the AI and ML Transformation Lifecycle: Best practices for managing the entire lifecycle of a digital transformation initiative, from conception to implementation, using AI and ML. 4. End-to-End AI and ML Process Analysis: Comprehensive analysis of supply chain processes with a focus on implementing AI and ML solutions effectively. 5. The Strategic Value of AI and ML in Business Transformation: Examining the unique value proposition offered by AI and ML in transforming business operations and supply chains. 6. AI and ML in Supply Chain Maturity Models and Roadmaps: Developing maturity models and roadmaps for businesses to integrate AI and ML into their supply chains effectively. 7. AI and ML Solutions for Supply Chain Optimization: Detailed exploration of how AI and ML technologies can enhance various supply chain processes, including demand planning, inventory management, and logistics. 8. Segmentation Strategies in the AI-Driven Supply Chain: Techniques for segmenting supply chains in the digital era, leveraging AI and ML for enhanced efficiency and customization. 9. Building a Compelling Case for AI and ML in Supply Chains: Strategies for convincing stakeholders of the necessity and benefits of implementing AI and ML in supply chain operations. 10. Assembling an Effective AI and ML Transformation Team: Guidelines for selecting and forming a team capable of successfully driving AI and ML transformation initiatives. 11. Developing a Comprehensive Communication and Education Plan: Crafting effective communication strategies and ongoing educational programs to support AI and ML transformations in supply chains. 12. Cultural Adaptation and Organizational Restructuring for AI and ML: Best practices for developing cultural support and restructuring organizations smartly to accommodate AI and ML technologies. 13. Measuring Performance in AI and ML-Enhanced Supply Chains: Establishing effective performance measurement systems to track the success and impact of AI and ML implementations in supply chains. 14. Choosing the Right AI and ML Supply Chain Software Partner: Tips and criteria for selecting a software partner that aligns with the company's AI and ML transformation goals in supply chain management. 15. Implementing AI and ML Changes in Supply Chains: Practical guidance on the implementation process of AI and ML technologies in supply chains, including case studies and lessons learned from successful transformations. 16. AI-Enhanced Supply Chain Planning: Exploring the impact of AI and ML on supply chain planning processes, including demand forecasting, resource allocation, and capacity planning, with an emphasis on improving accuracy and responsiveness. 17. Revolutionizing Logistics with ML and AI: Delving into how ML and AI technologies are transforming logistics operations, from route optimization and fleet management to real-time tracking and predictive maintenance. 18. Fostering Supplier Collaboration through AI and ML: Examining the role of AI and ML in enhancing supplier relationships and collaborations, focusing on communication improvements, risk management, and performance optimization. 19. AI-Driven Order Management and Fulfillment: Discussing the application of AI and ML in order management, including order processing, fulfillment optimization, and customer service enhancements, to improve efficiency and customer satisfaction.

Submission Procedure

Researchers and practitioners are invited to submit on or before May 21, 2024, a chapter proposal of 1,000 to 2,000 words clearly explaining the mission and concerns of his or her proposed chapter. Authors will be notified by June 8, 2024 about the status of their proposals and sent chapter guidelines.Full chapters are expected to be submitted by September 7, 2024, and all interested authors must consult the guidelines for manuscript submissions at https://www.igi-global.com/publish/contributor-resources/before-you-write/ prior to submission. All submitted chapters will be reviewed on a double-blind review basis. Contributors may also be requested to serve as reviewers for this project.

Note: There are no submission or acceptance fees for manuscripts submitted to this book publication, Supply Chain Transformation Through Generative AI and Machine Learning. All manuscripts are accepted based on a double-blind peer review editorial process.

All proposals should be submitted through the eEditorial Discovery® online submission manager.



Publisher

This book is scheduled to be published by IGI Global (formerly Idea Group Inc.), an international academic publisher of the "Information Science Reference" (formerly Idea Group Reference), "Medical Information Science Reference," "Business Science Reference," and "Engineering Science Reference" imprints. IGI Global specializes in publishing reference books, scholarly journals, and electronic databases featuring academic research on a variety of innovative topic areas including, but not limited to, education, social science, medicine and healthcare, business and management, information science and technology, engineering, public administration, library and information science, media and communication studies, and environmental science. For additional information regarding the publisher, please visit https://www.igi-global.com. This publication is anticipated to be released in 2025.



Important Dates

May 21, 2024: Proposal Submission Deadline
June 8, 2024: Notification of Acceptance
September 7, 2024: Full Chapter Submission
November 8, 2024: Review Results Returned
December 20, 2024: Final Acceptance Notification
January 3, 2025: Final Chapter Submission



Inquiries

Ehap Sabri University of Texas at Dallas ehap.sabri@utdallas.edu

Classifications


Business and Management; Computer Science and Information Technology; Education; Science and Engineering
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