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 forbakery products.

Auteur
Anna-Lena Sachs works as Assistant Professor for Supply Chain Management at the Faculty of Management, Economics and Social Sciences at the University of Cologne. Her research focuses on inventory optimization for perishable products and behavioral operations management. Anna-Lena Sachs studied business administration at the University of Mannheim, Germany and completed her PhD at TUM School of Management.

Contenu
Introduction.- Literature Review.- Safety Stock Planning under Causal Demand Forecasting.- The Data-Driven Newsvendor with Censored Demand Observations.- Data-Driven Order Policies with Censored Demand and Substitution.- Empirical Newsvendor Decisions under a Service Contract.- Conclusions.