Retail Analytics, 2015 Integrated Forecasting and Inventory Management for Perishable Products in Retailing Lecture Notes in Economics and Mathematical Systems Series, Vol. 680

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.

Presents a data driven approach that integrates demand forecasting and inventory management

Presents an optimal inventory policy for a multi-product newsvendor setting with an aggregated service level target

Includes several analyses of real data from a large European retail chain

Analyzes behavioral biases for real-world decisions

Includes supplementary material: sn.pub/extras