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Product backorder prediction using deep neural network on imbalanced data

Md Shajalal, Petr Hajek, Abedin Abedin

International Journal of Production Research, Volume: 61, Issue: 1, Pages: 302 - 319

Swansea University Author: Abedin Abedin

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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...

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Published in: International Journal of Production Research
ISSN: 0020-7543 1366-588X
Published: Informa UK Limited 2023
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URI: https://cronfa.swan.ac.uk/Record/cronfa64236
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first_indexed 2023-09-19T14:58:38Z
last_indexed 2023-09-19T14:58:38Z
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spelling v2 64236 2023-08-31 Product backorder prediction using deep neural network on imbalanced data 4ed8c020eae0c9bec4f5d9495d86d415 Abedin Abedin Abedin Abedin true false 2023-08-31 BAF 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. Journal Article International Journal of Production Research 61 1 302 319 Informa UK Limited 0020-7543 1366-588X Product backorder, deep neural network, synthetic oversampling, imbalanced data, prediction 2 1 2023 2023-01-02 10.1080/00207543.2021.1901153 http://dx.doi.org/10.1080/00207543.2021.1901153 COLLEGE NANME Accounting and Finance COLLEGE CODE BAF Swansea University 2023-09-19T15:58:35.1553502 2023-08-31T17:37:47.0757727 Faculty of Humanities and Social Sciences School of Management - Accounting and Finance Md Shajalal 1 Petr Hajek 2 Abedin Abedin 3
title Product backorder prediction using deep neural network on imbalanced data
spellingShingle Product backorder prediction using deep neural network on imbalanced data
Abedin Abedin
title_short Product backorder prediction using deep neural network on imbalanced data
title_full Product backorder prediction using deep neural network on imbalanced data
title_fullStr Product backorder prediction using deep neural network on imbalanced data
title_full_unstemmed Product backorder prediction using deep neural network on imbalanced data
title_sort Product backorder prediction using deep neural network on imbalanced data
author_id_str_mv 4ed8c020eae0c9bec4f5d9495d86d415
author_id_fullname_str_mv 4ed8c020eae0c9bec4f5d9495d86d415_***_Abedin Abedin
author Abedin Abedin
author2 Md Shajalal
Petr Hajek
Abedin Abedin
format Journal article
container_title International Journal of Production Research
container_volume 61
container_issue 1
container_start_page 302
publishDate 2023
institution Swansea University
issn 0020-7543
1366-588X
doi_str_mv 10.1080/00207543.2021.1901153
publisher Informa UK Limited
college_str Faculty of Humanities and Social Sciences
hierarchytype
hierarchy_top_id facultyofhumanitiesandsocialsciences
hierarchy_top_title Faculty of Humanities and Social Sciences
hierarchy_parent_id facultyofhumanitiesandsocialsciences
hierarchy_parent_title Faculty of Humanities and Social Sciences
department_str School of Management - Accounting and Finance{{{_:::_}}}Faculty of Humanities and Social Sciences{{{_:::_}}}School of Management - Accounting and Finance
url http://dx.doi.org/10.1080/00207543.2021.1901153
document_store_str 0
active_str 0
description 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.
published_date 2023-01-02T15:58:38Z
_version_ 1777478421077557248
score 11.012678

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