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.
