The total inventory level of the concerned medical distribution organization could be decreased from over a billion dollars to about half-a-billion dollars (reduction by 50 percent) using the method developed by the group.

One of the main requirements for agile organizations is the development of information systems for effective linkages with their suppliers, customers, and other channel partners involved in transportation, distribution, warehousing and maintenance. Agility increasingly depends on the quality of decision-making and companies are continuously trying to improve the quality of decisions by learning from past transactions and decisions. An efficient inventory management system based on contemporary information systems is a first step in this direction.

The group was able to use neural networks to optimize the inventory in a large medical distribution. The project uncovered the inventory patterns by discovering an appropriate method of constructing and choosing a neural network to solve the problem. As an extension to the neural network models, statistical procedures and assumptions were used to augment the neural network model.

With the large number of neural network classes, it is difficult to identify a particular class and model which offers the best inventory model. The group used an elaborate scheme based on traditional statistical techniques to evaluate the best neural network type.

Current Research 

The project is currently evaluating how data mining can work together with datawarehouses and OLAP to enhance knowledge discovery. Currently, the group is performing a state-of-the-art study on datawarehousing, OLAP, and data mining. The focus is on doing a market survey of all commercial applications available in these areas. This study will create a composite collection of information on the products and technologies available today.