Journal article 584 views
Product backorder prediction using deep neural network on imbalanced data
International Journal of Production Research, Volume: 61, Issue: 1, Pages: 302 - 319
Swansea University Author:
Mohammad Abedin
Full text not available from this repository: check for access using links below.
DOI (Published version): 10.1080/00207543.2021.1901153
Abstract
Taking backorders on products is a common scenario in inventory and supply chain management systems. The ability to predict the likelihood of backorders can surely minimise a company's losses. Because the number of backorders is much lower than the number of orders that ship on time, applying a...
| Published in: | International Journal of Production Research |
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| ISSN: | 0020-7543 1366-588X |
| Published: |
Informa UK Limited
2023
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| Online Access: |
Check full text
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| URI: | https://cronfa.swan.ac.uk/Record/cronfa64236 |
| Abstract: |
Taking backorders on products is a common scenario in inventory and supply chain management systems. The ability to predict the likelihood of backorders can surely minimise a company's losses. Because the number of backorders is much lower than the number of orders that ship on time, applying a predictive model for this domain is a challenging task. This paper proposes a model that uses a deep neural network to predict backorders; it handles the data imbalance between backorders and filled orders with efficient techniques. To make the dataset balanced, we employ different techniques that include minority class weight boosting, randomised oversampling, SMOTE oversampling, and a combination of oversampling and undersampling. The balanced training data are used in our proposed, fully connected deep neural networks model to train the predictive model. The predictive model learns the likelihood of product backorders by using the training samples. We conduct experiments on a large benchmark dataset to test the performance of our proposed deep neural network–based model. The experimental results achieve a new state-of-the-art performance and outperform some prominent classification models in terms of standard evaluation metrics and expected profit measure. |
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| Keywords: |
Product backorder, deep neural network, synthetic oversampling, imbalanced data, prediction |
| College: |
Faculty of Humanities and Social Sciences |
| Issue: |
1 |
| Start Page: |
302 |
| End Page: |
319 |