Application of neural networks techniques to military pharmaceutical ordering problems

The military is one of the largest pharmaceutical distributors in the country. In order to minimize the amount of inventory held, and hence warehousing and expired drug costs, data mining techniques can be applied to old transaction records to predict future needs. One powerful method of data mining is the use of neural networks. Neural networks have the ability to learn inventory needs based on past situations which are expected to occur again. Using neural networks to data mine government pharmaceutical supply necessities will enable the reduction of inventory levels as well as improve customer satisfaction by increasing the chance the needed prescriptions will be in stock. This thesis introduces inventory methods, data mining methods, and explores the application of data mining and neural network methods to actual inventory optimization problems. Limits and future direction suggestions are included at the end of the document.

Department dc:contributor.department

Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science.

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

1999

Author dc:creator

• Novak, John J. (John Joseph), 1971-

Advisor dc:contributor.advisor

• Amar Gupta.

• Electrical Engineering and Computer Science.

Statement dc:rights

• M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

http://hdl.handle.net/1721.1/47902

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/47902