Abstract
This paper deals with data mining applications for the supply chain inventory management. It describes the use of business intelligence (BI) tools, coupled with data warehouse to employ data mining technology to provide accurate and up-to-date information for better inventory management decisions. The methodology is designed to provide out-of-stock forecasts at the store/product level. The first phase of the modelling process consists of clustering stores in the supply chain based upon aggregate sales patterns. After quality store-cluster models have been constructed, these clusters are used to more accurately make out-of-stock predictions at the store/product level using the decision trees and neural network mining algorithms. The methods for evaluation and accuracy measurement are described. Also, the specialized front-end BI web portal that offers integrated reporting, web analytics, personalization, customization and collaboration is described.
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© 2008 Springer-Verlag London Limited
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Stefanovic, N., Stefanovic, D., Radenkovic, B. (2008). Application of Data Mining for Supply Chain Inventory Forecasting. In: Ellis, R., Allen, T., Petridis, M. (eds) Applications and Innovations in Intelligent Systems XV. SGAI 2007. Springer, London. https://doi.org/10.1007/978-1-84800-086-5_13
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DOI: https://doi.org/10.1007/978-1-84800-086-5_13
Publisher Name: Springer, London
Print ISBN: 978-1-84800-085-8
Online ISBN: 978-1-84800-086-5
eBook Packages: Computer ScienceComputer Science (R0)