| Summary: | An increasing number of organizations are involved in the development of strategic
information systems for effective linkages with their suppliers, customers, and other
channel partners involved in transportation, distribution, warehousing and maintenance
activities. An efficient inter-organizational inventory management system based on data
mining techniques is a significant step in this direction. This paper discusses the use of
neural network based data mining and knowledge discovery techniques to optimize
inventory levels in a large medical distribution company. The paper defines the
inventory patterns, describes the process of constructing and choosing an appropriate
neural network, and highlights problems related to mining of very large quantities of
data. The paper identifies the strategic data mining techniques used to address the
problem of estimating the future sales of medical products using past sales data. We
have used recurrent neural networks to predict future sales because of their power to
generalize trends and their ability to store relevant information about past sales. The
paper introduces the problem domain and describes the implementation of a
distributed recurrent neural network using the real time recurrent learning algorithm.
We then describe the validation of this implementation by providing results of tests with
well-known examples from the literature. The description and analysis of the
predictions made on real world data from a large medical distribution company are
then presented.
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