Chapter 1. Introduction -- Chapter 2. Artificial intelligence/machine learning project architecture and design -- Chapter 3. Knowledge base -- Chapter 4. Root cause analytics and analysis -- Chapter 5. Recommendation engine -- Chapter 6. Functional domain -- Chapter 7. Futuristic artificial intelligence -- Chapter 8. Conclusion.

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As we head into the ever-more globalized world of the 2020's, the critical role that logistics planning and operations plays in assuring a firm's financial well-being escalates in importance almost daily. Furthermore, the role of analytics in guiding both logistics planning and operational activities has dramatically spiked in the last decade, and this exponential growth shows no sign of slackening. As the phenomenon of Big Data has taken hold in the private sector, firms which as recently as ten years ago devoted minimal resources to large scale data mining and analytics have reversed course, and invested heavily in data analytics. In this environment, logistics professionals must have at their disposal, and must understand how to utilize a broad array of analytic techniques and approaches to logistics decision-making. Effective use of analytics requires a strong understanding of both fundamental and advanced logistics decision-making techniques and methodologies. Further, logistics professionals must organize and view these analytics-based decision support tools through well-structured planning frameworks. In this book, based on twenty-five plus years of logistics industry practice, we illustrate and explain a wide range of practical logistics strategies and analytic techniques to facilitate decision-making across functions such as manufacturing, warehousing, transportation and inventory management. Further we also describe how to organize these analytics-based tools and strategies through logistics frameworks that span strategic, tactical and operational planning and scheduling decisions. This book is intended for logistics professionals to use as a reference document that offers ideas and guidance for addressing specific logistics management decisions and challenges. In particular, this book provides explanatory and "how to implement" guidance on foundational analytics that logistics professionals can employ to generate practical insights to facilitate their daily and longer-term logistics management activities. This book can also serve as a valuable resource or secondary text for graduate and advanced undergraduate students. Students will develop an understanding of leading edge, real world approaches for logistics planning and scheduling, decision support, performance measurement and other key logistics activities.

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