Supply Chain Transformation Through Generative Ai And Machine Learning
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Supply Chain Transformation Through Generative AI and Machine Learning
Author: Ehap H. Sabri
language: en
Publisher: Business Science Reference
Release Date: 2025
"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"--
Supply Chain Transformation Through Generative AI and Machine Learning
The transformative role of Generative Artificial Intelligence (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. However, the path to digital 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. Supply Chain Transformation Through Generative AI and Machine Learning is a comprehensive resource to the best practices in digital enablement, change management, and process optimization, with a specific focus on Generative AI and ML. It equips 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 and effectiveness, reduce costs, and drive revenue growth. Covering topics such as AI-powered visual models, demand planning, and product clustering, this book is an excellent resource for executives, business leaders, program managers, data scientists, AI and ML developers, industry analysts, consultants, professionals, scholars, researchers, academicians, and more.
Advancements in Artificial Intelligence and Machine Learning
Author: Asif Khan, Mohammad Kamrul Hasan, Naushad Varish, Mohammed Aslam Husain
language: en
Publisher: Bentham Science Publishers
Release Date: 2025-06-19
Artificial Intelligence (AI) and Machine Learning (ML) are revolutionizing industries, reshaping the way we interact with technology, and driving innovation across multiple disciplines. Advancements in Artificial Intelligence and Machine Learning is a comprehensive exploration of the latest developments, applications, and challenges in AI and ML, offering insights into cutting-edge research and real-world implementations. This book is a collection of twelve chapters, each exploring a distinct application of Artificial Intelligence (AI) and Machine Learning (ML). It begins with an overview of AI’s transformative role in Next-Gen Mechatronics, followed by a comprehensive review of key advancements and trends in the field. The book then examines AI’s impact across diverse sectors, including energy, digital communication, and security, with topics such as AI-based aging analysis of power transformer oil, AI in social media management, and AI-driven human detection systems. Further chapters address sentiment analysis, visual analysis for image processing, and the integration of AI in smart grid networks. The volume also covers AI applications in hardware security for wireless sensor networks, drone robotics, and crime prevention systems. The final set of chapters highlight AI’s role in healthcare and automation, including an AI-assisted system for women’s safety in India and the use of EfficientNet B0 CNN architecture for brain tumor detection and classification. Together, these chapters showcase the versatility and growing influence of AI and ML across critical modern industries. Key features A multidisciplinary approach covering AI applications in robotics, cybersecurity, healthcare, and digital transformation in 12 organized chapters. A focus on contemporary challenges and solutions in AI and ML across industries. Research-driven insights from experts and practitioners in the field. Practical discussions on AI-driven automation, security, and intelligent decision-making systems.