Managing Intermittent Demand

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. 

List of 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.

Product details

• Authors: Torben Engelmeyer
• Publisher: Springer, Berlin
• Languages: English
• Product format: Paperback / Softback
• Released: 11.05.2016
• EAN: 9783658140618
• ISBN: 978-3-658-14061-8
• No. of pages: 157
• Dimensions: 151 mm x 12 mm x 260 mm
• Weight: 237 g
• Illustrations: XV, 157 p. 65 illus.
• Subjects: Social sciences, law, business > Business > Management; Operations Research, C, Business and Management, Supply Chain Management, Operations Management, Operations Research/Decision Theory, Operations Research and Decision Theory, Logistics, Management decision making, Management science, Operational research, Decision Making, Purchasing & supply management, Business logistics, Logistik;Langsamdreher;Optimierung;Prognose;Bestandsführung