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Use of Recurrent Neural Networks for Strategic Data Mining of Sales

Author

Listed:
  • Vadhavkar, Sanjeev
  • Shanmugasundaram, Jayavel
  • Gupta, Amar
  • Prasad, M.V. Nagendra

Abstract

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.

Suggested Citation

  • Vadhavkar, Sanjeev & Shanmugasundaram, Jayavel & Gupta, Amar & Prasad, M.V. Nagendra, 2002. "Use of Recurrent Neural Networks for Strategic Data Mining of Sales," Working papers 4347-02, Massachusetts Institute of Technology (MIT), Sloan School of Management.
  • Handle: RePEc:mit:sloanp:717
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    File URL: http://hdl.handle.net/1721.1/717
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