Classification Approaches to Identify Intermittent Demand Series -- Consistent Forecast-Based Inventory Model -- Extensive Comparison of the Inventory Performance Among Different Forecast/Inventory Model Combinations.

This work aims to increase the service level and to reduce the inventory costs by combining the forecast and inventory model into one consistent forecast-based inventory model. This new model is based on the prediction of the future probability distribution by assuming an integer-valued autoregressive process as demand process. The developed algorithms can be used to identify, estimate, and predict the demand as well as optimize the inventory decision of intermittent demand series. In an extensive simulation study the new model is compared with a wide range of conventional forecast/inventory model combinations. By using the consistent approach, the mean inventory level is lowered whereas the service level is increased. Additionally, a modern multi-criteria inventory classification scheme is presented to distinguish different demand series clusters. Contents Classification Approaches to Identify Intermittent Demand Series Consistent Forecast-Based Inventory Model Extensive Comparison of the Inventory Performance Among Different Forecast/Inventory Model Combinations Target Group Students and researchers interested in business analytics and operations management Inventory managers and supply chain experts The Author Dr. Torben Engelmeyer works as a research assistant at the chair of International Economics - University of Wuppertal, Germany.