Computer Science and Information Systems 2015 Volume 12, Issue 3, Pages: 911-930
https://doi.org/10.2298/CSIS141101034S
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Collaborative predictive business intelligence model for spare parts inventory replenishment
Stefanovic Nenad (Faculty of Technical Sciences, Cacak)
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
Projekat Ministarstva nauke Republike
Srbije, br. III-44010: Intelligent Systems for Software Product Development
and Business Support based on Models