Journal article
Classifying inventory using an artificial neural network approach
Computers & industrial engineering, v 41(4), pp 389-404
2002
Featured in Collection : UN Sustainable Development Goals @ Drexel
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.
Metrics
Details
- Title
- Classifying inventory using an artificial neural network approach
- Creators
- Fariborz Y Partovi - Drexel UniversityMurugan Anandarajan - Drexel University
- Publication Details
- Computers & industrial engineering, v 41(4), pp 389-404
- Publisher
- Elsevier
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Decision Sciences (and Management Information Systems)
- Web of Science ID
- WOS:000173772000003
- Scopus ID
- 2-s2.0-0036466985
- Other Identifier
- 991019168288504721
UN Sustainable Development Goals (SDGs)
This publication has contributed to the advancement of the following goals:
InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Web of Science research areas
- Computer Science, Interdisciplinary Applications
- Engineering, Industrial