Classifying inventory using an artificial neural network approach

Authors:
Fariborz Y. Partovi;Murugan Anandarajan
Affiliations:
Department of Decision Sciences, Drexel University, Philadelphia, PA;Department of Management, Drexel University, Philadelphia, PA
Venue:
Computers and Industrial Engineering
Year:
2002

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Abstract

This paper presents artificial neural networks (ANNs) for ABC classification of stock keeping units (SKUs) in a pharmaceutical company. Two learning methods were utilized in the ANNs, namely back propagation (BP) and genetic algorithms (GA). The reliability of the models was tested by comparing their classification ability with two data sets (a hold-out sample and an external data set). Furthermore, the ANN models were compared with the multiple discriminate analysis (MDA) technique. The results showed that both ANN models had higher predictive accuracy than MDA. The results also indicate that there was no significant difference between the two learning methods used to develop the ANN.