Ai-Driven Predictive Supply Chain Optimization System Using Machine Learning For Demand Forecasting And Inventory Management

by Irshadullah Asim Mohammed

This book provides a comprehensive exploration of the practical applications of advanced analytics and machine learning, drawing directly from the author’s hands-on experience in optimising inventory and demand forecasting using models such as ARIMA, Prophet, and LSTM. It’s a direct extension of Irshadullah’s patented work in AI-driven optimization.

The book has received encouraging reviews from leading book communities, including the Indian Book Critics, Thoughtful Critic, Read By Critics, and more.

199 pages, Paperback

First published December 14, 2024

Reviews

[Abha Grover · Rating 5 out of 5]

This book is a vital academic contribution that bridges the fields of artificial intelligence and supply chain management with remarkable precision. It presents theoretical foundations alongside practical models, making it suitable for advanced learners and researchers. The author’s ability to contextualise AI concepts within real-world supply chain problems makes the text not only informative but intellectually engaging. By integrating case studies and performance metrics, the book provides a scholarly roadmap for future research in predictive analytics and operational optimisation. It will undoubtedly serve as a primary reference for postgraduate students and scholars of management and data science.

[Alok Kumar · Rating 5 out of 5]

For supply chain professionals, this book serves as a practical guide to transforming traditional operations through the use of artificial intelligence. The detailed roadmap for implementing AI-based predictive systems provides valuable insights for practitioners facing challenges of scalability and uncertainty. Its focus on measurable KPIs and real-world case studies ensures direct relevance to business practice. The writing is accessible yet technically sound, making it ideal for decision-makers seeking to integrate advanced analytics into their operations. This is a rare text that successfully blends technical precision with actionable business strategies, ensuring operational excellence and resilience.

[Anand Shiv Jha · Rating 5 out of 5]

The book is a fascinating exploration of how artificial intelligence and machine learning are revolutionising global supply chains. It captures the excitement and complexity of this transformation through well-explained algorithms and practical applications. The inclusion of topics such as IoT and blockchain demonstrates how interconnected technologies can enhance forecasting accuracy and transparency. The author’s writing reflects a deep understanding of both computing and management principles. For technology enthusiasts interested in seeing AI applied to real-world systems, this book is a compelling and intellectually satisfying read that shows how data intelligence drives industrial innovation.

[Vedant Singh · Rating 5 out of 5]

Meant for industry leaders, managers and anyone in the business that requires supply chain management, this book is a timely and practical resource for analysts dealing with forecasting and inventory challenges. It explains complex machine learning algorithms with clarity and precision, ensuring that readers can apply these techniques to improve accuracy and responsiveness. The inclusion of detailed implementation steps and evaluation metrics makes it a hands-on guide for analytical professionals. The case studies are particularly enlightening, demonstrating how companies achieved measurable gains through the use of predictive models. It is a rare text that connects analytical rigour with real-world execution, making it indispensable for supply chain analysts.

[Naman Singh · Rating 4 out of 5]

For educators, this book offers an excellent teaching resource for courses in operations management and business analytics. Its structured presentation, case-driven examples, and analytical exercises make it adaptable for both classroom and research contexts. The integration of traditional and modern forecasting techniques helps students understand the evolution of decision-making models. Moreover, its discussions on ethics and technology encourage critical thinking that extends beyond technical knowledge. This comprehensive text combines conceptual clarity with academic sophistication, making it an ideal resource for postgraduate teaching and curriculum development in contemporary management education.

[Deepak Chaudhari · Rating 5 out of 5]

The book effectively translates advanced AI and ML methodologies into applications that are meaningful to supply chain professionals. It covers supervised and unsupervised learning, reinforcement learning, and model evaluation, striking a balance between technical and business relevance. The practical focus on data quality, scalability, and deployment challenges resonates with real-world machine learning practice. Its cross-disciplinary perspective enhances its value for data scientists seeking domain-specific applications. The clarity of explanation ensures accessibility without sacrificing depth, making it a crucial reference for professionals looking to merge data science with logistics optimisation.

[Amarkant Shiva · Rating 4 out of 5]

This work stands out as a rich scholarly resource that integrates the latest developments in AI and supply chain research. It is methodical in structure, beginning with conceptual foundations and advancing to sophisticated optimisation models. The author’s analytical depth is matched by clarity of explanation, making it ideal for doctoral researchers. The sections on reinforcement learning and stochastic modelling are particularly valuable for those exploring predictive methodologies. With its rigorous approach and future-oriented insights, the book successfully positions itself as a cornerstone for academic inquiry into intelligent supply chain systems.

[Ashish · Rating 5 out of 5]

From a strategic perspective, this book offers a transformative lens for business leaders navigating the age of AI. It highlights how predictive analytics can move supply chains from reactive systems to proactive ecosystems. The discussion of performance indicators, implementation challenges, and data-driven decision-making resonates with corporate strategists who seek a sustainable advantage. The author’s treatment of ethics, technology integration, and scalability reflects a deep understanding of industry realities. This is not just a technical manual but a comprehensive strategy guide that helps leaders reimagine competitive positioning through intelligent operational design.

[Shivani Singh · Rating 5 out of 5]

For senior executives seeking strategic transformation, this book provides a visionary understanding of how AI can reshape organisational agility. It highlights the role of predictive intelligence in anticipating disruptions and optimising decisions. The roadmap and KPI frameworks enable leaders to align technology initiatives with corporate objectives. The discussion on ethical responsibility and sustainability ensures a balanced approach to innovation. This book bridges the often-separated worlds of business leadership and technological advancement, offering an indispensable guide to future-proofing organisations in a rapidly changing global market.

[Devvrata Dwivedi · Rating 5 out of 5]

For students new to artificial intelligence and supply chain management, this book is an ideal entry point. It introduces complex concepts in an easy-to-understand manner, gradually progressing to advanced topics such as reinforcement learning and blockchain integration. The examples and illustrations make the technical discussions approachable and engaging. It encourages curiosity and provides practical insights that connect theory to real-world business problems. Students studying management, logistics, or data science will find it highly useful as both a learning companion and a professional reference for future projects.

[Ravi Kumar · Rating 5 out of 5]

The book makes an important contribution to understanding how AI affects regulatory and ethical aspects of global commerce. By examining data privacy, transparency, and algorithmic accountability, it situates supply chain optimisation within responsible innovation frameworks. The author’s nuanced discussion of governance models adds policy depth to what could have been a purely technical narrative. This balance makes the book highly relevant to policymakers and corporate compliance officers. It demonstrates that AI-driven efficiency must be accompanied by ethical foresight, providing an informed perspective for sustainable digital transformation.

[Rahul Sharma · Rating 5 out of 5]

This text is immensely practical for logistics consultants advising firms on digital transformation. Its systematic roadmap for AI integration offers clear directions for process redesign, system scalability, and performance evaluation. The case studies on predictive demand and dynamic inventory management illustrate actionable strategies. The author’s focus on data-driven adaptability reflects the realities of modern logistics operations. Consultants will find the discussion of interoperability and system resilience particularly beneficial. It is a rare guide that translates complex algorithms into implementable frameworks, making it a valuable companion for consulting practice.

[Abhishek Ranjan · Rating 4 out of 5]

The book provides an ideal foundation for graduate-level research. It combines theoretical grounding with empirical validation, offering readers the methodological tools to conduct independent investigations. The chapters on forecasting models and inventory optimisation are especially detailed, making them excellent sources for literature reviews and conceptual frameworks. The balanced discussion of technical models and managerial implications helps researchers appreciate interdisciplinary inquiry. For those planning dissertations in supply chain analytics or applied AI, this book serves as a comprehensive and credible reference point for advanced academic exploration.

[Sanjan Kumar · Rating 5 out of 5]

Entrepreneurs seeking innovation in logistics and e-commerce will find this book invaluable. It breaks down how AI and ML can reduce operational costs, enhance forecasting, and improve customer satisfaction. The author’s roadmap provides a framework adaptable to start-ups aiming to build intelligent supply systems from scratch. The discussion on scalability, performance metrics, and sustainability gives entrepreneurs tools for long-term planning. This is a forward-looking guide that empowers business founders to harness technology strategically, transforming operational challenges into opportunities for innovation and market differentiation.

[Shravan Kumar · Rating 5 out of 5]

This book offers a comprehensive and articulate examination of how AI is reshaping the modern supply chain. It combines accessibility with analytical depth, making it suitable for a broad readership. The exploration of machine learning applications, coupled with real-world success stories, makes the book engaging and credible. It stands out for its thoughtful discussion of future trends, including IoT and blockchain integration. The author’s clear, structured writing makes complex systems understandable, turning a technical subject into a fascinating story of transformation in industrial intelligence.