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

Petr Hajek Orcid Logo, Abedin Abedin

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

Swansea University Author: Abedin Abedin

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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
Online Access: Check full text

URI: https://cronfa.swan.ac.uk/Record/cronfa64272
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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 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.
Keywords: Big data, inventory backorder, machine learning, prediction
College: Faculty of Humanities and Social Sciences
Funders: This work was supported by the scientific research project of the Czech Sciences Foundation under Grant 19-15498S.
Start Page: 58982
End Page: 58994

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