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.

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 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.

Life Cycle Inventory (LCI) Analysis is the second phase in the Life Cycle Assessment (LCA) framework. Since the first attempts to formalize life cycle assessment in the early 1970, life cycle inventory analysis has been a central part. Chapter 1 “Introduction to Life Cycle Inventory Analysis“ discusses the history of inventory analysis from the 1970s through SETAC and the ISO standard. In Chapter 2 “Principles of Life Cycle Inventory Modeling”, the general principles of setting up an LCI model and LCI analysis are described by introducing the core LCI model and extensions that allow addressing reality better. Chapter 3 “Development of Unit Process Datasets” shows that developing unit processes of high quality and transparency is not a trivial task, but is crucial for high-quality LCA studies. Chapter 4 “Multi-functionality in Life Cycle Inventory Analysis: Approaches and Solutions” describes how multi-functional processes can be identified. In Chapter 5 “Data Quality in Life Cycle Inventories”, the quality of data gathered and used in LCI analysis is discussed. State-of-the-art indicators to assess data quality in LCA are described and the fitness for purpose concept is introduced. Chapter 6 “Life Cycle Inventory Data and Databases“ follows up on the topic of LCI data and provides a state-of-the-art description of LCI databases. It describes differences between foreground and background data, recommendations for starting a database, data exchange and quality assurance concepts for databases, as well as the scientific basis of LCI databases. Chapter 7 “Algorithms of Life Cycle Inventory Analysis“ provides the mathematical models underpinning the LCI. Since Heijungs and Suh (2002), this is the first time that this aspect of LCA has been fundamentally presented. In Chapter 8 “Inventory Indicators in Life Cycle Assessment”, the use of LCI data to create aggregated environmental and resource indicators is described. Such indicators include the cumulative energy demand and various water use indicators. Chapter 9 “The Link Between Life Cycle Inventory Analysis and Life Cycle Impact Assessment” uses four examples to discuss the link between LCI analysis and LCIA. A clear and relevant link between these phases is crucial.

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.

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