About the paper
| Title |
Predictive analytical model for spare parts inventory replenishment |
| Book |
ICIST 2014 Proceedings |
| Pages |
34-39 |
| Abstract: 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 showed as inadequate
for these tasks. Current business environment requires new
methods that incorporate more intelligent technologies and
tools capable to make accurate and reliable predictions.
This paper deals with data mining applications for the
supply chain inventory management. It describes the use of
business intelligence (BI) tools, 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 showed very
good accuracy of the model which makes it suitable for
more informed inventory decision making. |
Full citation
Stefanović, N.
Predictive analytical model for spare parts inventory replenishment. In:
Zdravković, M.,
Trajanović, M.,
Konjović, Z.
(Eds.)
ICIST 2014 Proceedings, pp.34-39, Belgrade, Serbia, 2014
Keywords
Business Intelligence
Supply Chain Management