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A Profit Function-Maximizing Inventory Backorder Prediction System Using Big Data Analytics

Petr Hajek Orcid Logo, Mohammad Abedin Orcid Logo

IEEE Access, Volume: 8, Pages: 58982 - 58994

Swansea University Author: Mohammad Abedin Orcid Logo

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DOI (Published version): 10.1109/access.2020.2983118

Abstract

Inventory backorder prediction is widely recognized as an important component of inventory models. However, backorder prediction is traditionally based on stochastic approximation, thus neglecting the substantial amount of useful information hidden in historical inventory data. To provide those inve...

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Published in: IEEE Access
ISSN: 2169-3536
Published: Institute of Electrical and Electronics Engineers (IEEE) 2020
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URI: https://cronfa.swan.ac.uk/Record/cronfa64272
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spelling 2023-09-19T16:13:13.5318405 v2 64272 2023-08-31 A Profit Function-Maximizing Inventory Backorder Prediction System Using Big Data Analytics 4ed8c020eae0c9bec4f5d9495d86d415 0000-0002-4688-0619 Mohammad Abedin Mohammad Abedin true false 2023-08-31 CBAE Inventory backorder prediction is widely recognized as an important component of inventory models. However, backorder prediction is traditionally based on stochastic approximation, thus neglecting the substantial amount of useful information hidden in historical inventory data. To provide those inventory models with a big data-driven backorder prediction, we propose a machine learning model equipped with an undersampling procedure to maximize the expected profit of backorder decisions. This is achieved by integrating the proposed profit-based measure into the prediction model and optimizing the decision threshold to identify the optimal backorder strategy. We show that the proposed inventory backorder prediction model shows better prediction and profit function performance than the state-of-the-art machine learning methods used for large imbalanced data. Notably, the proposed model is computationally effective and robust to variation in both warehousing/inventory cost and sales margin. In addition, the model predicts both major (non-backorder items) and minor (backorder items) classes in a benchmark dataset. Journal Article IEEE Access 8 58982 58994 Institute of Electrical and Electronics Engineers (IEEE) 2169-3536 Big data, inventory backorder, machine learning, prediction 7 4 2020 2020-04-07 10.1109/access.2020.2983118 http://dx.doi.org/10.1109/access.2020.2983118 COLLEGE NANME Management School COLLEGE CODE CBAE Swansea University This work was supported by the scientific research project of the Czech Sciences Foundation under Grant 19-15498S. 2023-09-19T16:13:13.5318405 2023-08-31T19:06:43.9172336 Faculty of Humanities and Social Sciences School of Management - Accounting and Finance Petr Hajek 0000-0001-5579-1215 1 Mohammad Abedin 0000-0002-4688-0619 2 64272__28558__609bd60925f54337aa0ddb79280be5c1.pdf 64272.VOR.pdf 2023-09-18T14:37:17.8686900 Output 5137811 application/pdf Version of Record true © Author(s) 2020. Distributed under the terms of a Creative Commons Attribution 4.0 License (CC BY 4.0). true eng https://creativecommons.org/licenses/by/4.0/
title A Profit Function-Maximizing Inventory Backorder Prediction System Using Big Data Analytics
spellingShingle A Profit Function-Maximizing Inventory Backorder Prediction System Using Big Data Analytics
Mohammad Abedin
title_short A Profit Function-Maximizing Inventory Backorder Prediction System Using Big Data Analytics
title_full A Profit Function-Maximizing Inventory Backorder Prediction System Using Big Data Analytics
title_fullStr A Profit Function-Maximizing Inventory Backorder Prediction System Using Big Data Analytics
title_full_unstemmed A Profit Function-Maximizing Inventory Backorder Prediction System Using Big Data Analytics
title_sort A Profit Function-Maximizing Inventory Backorder Prediction System Using Big Data Analytics
author_id_str_mv 4ed8c020eae0c9bec4f5d9495d86d415
author_id_fullname_str_mv 4ed8c020eae0c9bec4f5d9495d86d415_***_Mohammad Abedin
author Mohammad Abedin
author2 Petr Hajek
Mohammad Abedin
format Journal article
container_title IEEE Access
container_volume 8
container_start_page 58982
publishDate 2020
institution Swansea University
issn 2169-3536
doi_str_mv 10.1109/access.2020.2983118
publisher Institute of Electrical and Electronics Engineers (IEEE)
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/10.1109/access.2020.2983118
document_store_str 1
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description Inventory backorder prediction is widely recognized as an important component of inventory models. However, backorder prediction is traditionally based on stochastic approximation, thus neglecting the substantial amount of useful information hidden in historical inventory data. To provide those inventory models with a big data-driven backorder prediction, we propose a machine learning model equipped with an undersampling procedure to maximize the expected profit of backorder decisions. This is achieved by integrating the proposed profit-based measure into the prediction model and optimizing the decision threshold to identify the optimal backorder strategy. We show that the proposed inventory backorder prediction model shows better prediction and profit function performance than the state-of-the-art machine learning methods used for large imbalanced data. Notably, the proposed model is computationally effective and robust to variation in both warehousing/inventory cost and sales margin. In addition, the model predicts both major (non-backorder items) and minor (backorder items) classes in a benchmark dataset.
published_date 2020-04-07T08:24:02Z
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score 11.04748

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