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Inventory Analytics provides a comprehensive and accessible introduction to the theory and practice of inventory control – a significant research area central to supply chain planning. The book outlines the foundations of inventory systems and surveys prescriptive analytics models for deterministic inventory control. It further discusses predictive analytics techniques for demand forecasting in inventory control and also examines prescriptive analytics models for stochastic inventory control. Inventory Analytics is the first book of its kind to adopt a practicable, Python-driven approach to illustrating theories and concepts via computational examples, with each model covered in the book accompanied by its Python code. Originating as a collection of self-contained lectures, Inventory Analytics will be an indispensable resource for practitioners, researchers, teachers, and students alike.
This textbook provides a practice-oriented introduction into Analytics-based inventory management in complex supply chains. In the context of Business Analytics, we concentrate on Prescriptive Analytics. In addition to standard single-level inventory models also multi-level approaches for the optimal allocation of safety inventory are presented. Moreover, dynamic lot sizing problems under random demand and random yield and their relationship to Material Requirements Planning (MRP) are discussed.The models and algorithms are illustrated with the help of numerous examples. The book has been written for students of Supply Chain Management and Operations Management as well as for practitioners who are confronted with inventory management in their daily work.
This book provides a first-hand account of business analytics and its implementation, and an account of the brief theoretical framework underpinning each component of business analytics. The themes of the book include (1) learning the contours and boundaries of business analytics which are in scope; (2) understanding the organization design aspects of an analytical organization; (3) providing knowledge on the domain focus of developing business activities for financial impact in functional analysis; and (4) deriving a whole gamut of business use cases in a variety of situations to apply the techniques. The book gives a complete, insightful understanding of developing and implementing analytical solution.
This book addresses the challenging task of demand forecasting and inventory management in retailing. It analyzes how information from point-of-sale scanner systems can be used to improve inventory decisions, and develops a data-driven approach that integrates demand forecasting and inventory management for perishable products, while taking unobservable lost sales and substitution into account in out-of-stock situations. Using linear programming, a new inventory function that reflects the causal relationship between demand and external factors such as price and weather is proposed. The book subsequently demonstrates the benefits of this new approach in numerical studies that utilize real data collected at a large European retail chain. Furthermore, the book derives an optimal inventory policy for a multi-product setting in which the decision-maker faces an aggregated service level target, and analyzes whether the decision-maker is subject to behavioral biases based on real data for bakery products.
This book showcases how the latest and most advanced types of analytical modeling and empirical analysis can help to create value in the global supply chain. Focusing on practical relevance, it shares valuable management insights and addresses key issues in operations management (OM), demonstrating how past research has led to various practices and impacts, while also exploring the aspirations of the latest research. It presents current research on various topics such as global supply chain design, service supply chains, product design, responsible supply chains, performance and incentives in operations, data analytics in health services, new business models in the digital age, and new digital technology advances such as blockchain. In addition, it presents practical case studies on the aforementioned topics. Beyond the value of its contents, the book is intended as a tribute to Professor Morris Cohen, who has been a major contributor to advancing the research frontier in operations management and a driving force in shaping the field. Given its scope, the book will appeal to a wide readership, from researchers and PhD students to practitioners and consultants.
The Dashboards and Widgets Guide contains instructions for designing and creating MicroStrategy Report Services dashboards, a type of document that is optimized for viewing online and for user interactivity. It builds on the basic concepts about documents presented in the Document Creation Guide.
Nahmias and Olsen skillfully blend comprehensive coverage of topics with careful integration of mathematics. The authors’ decades of experience in the field contributed to the success of previous editions; the eighth edition continues the long tradition of excellence. Clearly written, reasonably priced, with an abundance of expertly formulated practice problems and updated examples, this textbook is essential reading for analyzing and improving all facets of operations. Some of the material in the newest edition has been reorganized. For example, the first chapter introduces service strategy, the product/process matrix and flexible manufacturing systems, benchmarking, the productivity frontier, the innovation curve, and lean production as a strategy. The focus is slightly more international. The analysis of capacity growth planning now appears in the chapter on supply chain analytics. Aggregate planning details were added to chapter 3, including chase and level strategies in an appendix to the chapter. There is an expanded discussion on risk pooling in the chapter on supply chain strategy. The mechanics behind lean production are included in the chapter on push and pull production systems. The chapter on quality and assurance downplays sampling in favor of discussions of quality management, process capability, and the waste elimination side of lean. The separate chapter on facilities layout and location was eliminated and the information redistributed throughout the text. The authors reinforce the learning process through key points at the beginning of each chapter to guide the reader, snapshots that provide useful examples of applications to businesses, and historical notes that provide a context for the topics discussed. Production and Operations Analytics, 8/e provides the tools for adapting to the dynamic global marketplace.
Information Technology for Management provides students with a comprehensive understanding of the latest technological developments in IT and the critical drivers of business performance, growth, and sustainability. Integrating feedback from IT managers and practitioners from top-level organizations worldwide, the International Adaptation of this well-regarded textbook features thoroughly revised content throughout to present students with a realistic, up-to-date view of IT management in the current business environment. This text covers the latest developments in the real world of IT management with the addition of new case studies that are contemporary and more relevant to the global scenario. It offers a flexible, student-friendly presentation of the material through a pedagogy that is designed to help students easily comprehend and retain information. There is new and expanded coverage of Artificial Intelligence, Robotics, Quantum Computing, Blockchain Technology, IP Intelligence, Big Data Analytics, IT Service Management, DevOps, etc. It helps readers learn how IT is leveraged to reshape enterprises, engage and retain customers, optimize systems and processes, manage business relationships and projects, and more.
From personalized recommendations and dynamic pricing to automated inventory management and fraud detection, AI offers a treasure trove of opportunities to boost efficiency, enhance customer experience, and increase profitability. We'll explore how AI can be harnessed to: Understand your customers: Go beyond demographics to uncover hidden preferences and predict buying behavior. Optimize your operations: Streamline logistics, minimize stockouts, and make data-driven decisions for smarter inventory management. Personalize the shopping journey: Create frictionless experiences, offer relevant product suggestions, and build lasting customer relationships. Stay ahead of the curve: Identify emerging trends, adapt to market fluctuations, and innovate with confidence.
Introduction to Business Analytics Using Simulation, Second Edition employs an innovative strategy to teach business analytics. The book uses simulation modeling and analysis as mechanisms to introduce and link predictive and prescriptive modeling. Because managers can't fully assess what will happen in the future, but must still make decisions, the book treats uncertainty as an essential element in decision-making. Its use of simulation gives readers a superior way of analyzing past data, understanding an uncertain future, and optimizing results to select the best decision. With its focus on uncertainty and variability, this book provides a comprehensive foundation for business analytics. Students will gain a better understanding of fundamental statistical concepts that are essential to marketing research, Six-Sigma, financial analysis, and business analytics. - Teaches managers how they can use business analytics to formulate and solve business problems to enhance managerial decision-making - Explains the processes needed to develop, report and analyze business data - Describes how to use and apply business analytics software - Offers expanded coverage on the value and application of prescriptive analytics - Includes a wealth of illustrative exercises that are newly organized by difficulty level - Winner of the 2017 Textbook and Academic Authors Association's (TAA) Most Promising New Textbook Award in the prior edition