Geodesic
Collaborative predictive business intelligence model for spare parts inventory replenishment
Computer Science and Information Systems, Tome 12 (2015) no. 3.

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In today’s volatile and turbulent business environment, supply chains face great challenges when making supply and demand decisions. Making optimal inventory replenishment decision became critical for successful supply chain management. Existing traditional inventory management approaches and technologies showed as inadequate for these tasks. Current business environment requires new methods that incorporate more intelligent technologies and tools capable to make fast, accurate and reliable predictions. This paper deals with data mining applications for the supply chain inventory management. It describes the unified business intelligence semantic model, coupled with a data warehouse to employ data mining technology to provide accurate and up-to-date information for better inventory management decisions and to deliver this information to relevant decision makers in a user-friendly manner. Experiments carried out with the real data set, from the automotive industry, showed very good accuracy and performance of the model which makes it suitable for collaborative and more informed inventory decision making.
Keywords: predictive analytics, supply chain inventory management, data mining, collaborative business intelligence, web portal 
@article{CSIS_2015_12_3_a1,
     author = {Nenad Stefanovic},
     title = {Collaborative predictive business intelligence model for spare parts inventory replenishment},
     journal = {Computer Science and Information Systems},
     publisher = {mathdoc},
     volume = {12},
     number = {3},
     year = {2015},
     url = {http://geodesic.mathdoc.fr/item/CSIS_2015_12_3_a1/}
}
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Nenad Stefanovic. Collaborative predictive business intelligence model for spare parts inventory replenishment. Computer Science and Information Systems, Tome 12 (2015) no. 3. http://geodesic.mathdoc.fr/item/CSIS_2015_12_3_a1/