Introduction to Machine Learning for Supply Chains and Industrial Optimization

Many machine learning books survey techniques one at a time. This book shows how they fit together. Its checklist model of AI maturation provides a unifying path from regression and experimental design through decision trees, neural networks, large language models, and clustering to genetic algorithms, Markov decision processes, partially observable Markov decision processes, and reinforcement learning. A connected family of supply chain cases runs throughout the book, covering demand forecasting, supplier classification, vehicle routing, production scheduling, and inventory management. Each case includes printed “known answers,” allowing readers to check their results. Rather than relying on long program listings that can quickly become outdated, the book provides regeneration prompts that readers can use with LLM assistants such as ChatGPT, Gemini, and Claude to generate current Python implementations. Verification checklists and guidance for running code in Google Colab or locally help readers evaluate the generated programs rather than treating their output as automatically correct. Designed explicitly for the era of LLM-assisted coding, the book combines statistical foundations, modern machine learning, and decision-focused optimization. End-of-chapter problems include LLM workflow exercises in which readers generate, run, and verify their own implementations, while solutions to selected problems appear at the back of the book. A glossary, alternative plans for semester-long and shorter courses, real-world datasets, and supplementary teaching materials make the book adaptable for classroom and professional use. It is written for senior undergraduates, graduate students, researchers, and practitioners in supply chain management, industrial engineering, operations research, analytics, and related fields who have a working knowledge of basic statistics.