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Product backorder prediction using deep neural network on imbalanced data

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216275%3A25410%2F23%3A39920846" target="_blank" >RIV/00216275:25410/23:39920846 - isvavai.cz</a>

  • Result on the web

    <a href="https://www.tandfonline.com/doi/abs/10.1080/00207543.2021.1901153" target="_blank" >https://www.tandfonline.com/doi/abs/10.1080/00207543.2021.1901153</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.1080/00207543.2021.1901153" target="_blank" >10.1080/00207543.2021.1901153</a>

Alternative languages

  • Result language

    angličtina

  • Original language name

    Product backorder prediction using deep neural network on imbalanced data

  • Original language 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&apos;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.

  • Czech name

  • Czech description

Classification

  • Type

    J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database

  • CEP classification

  • OECD FORD branch

    50204 - Business and management

Result continuities

  • Project

    <a href="/en/project/GA19-15498S" target="_blank" >GA19-15498S: Modelling emotions in verbal and nonverbal managerial communication to predict corporate financial risk</a><br>

  • Continuities

    P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Others

  • Publication year

    2023

  • Confidentiality

    S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Data specific for result type

  • Name of the periodical

    International journal of production research

  • ISSN

    0020-7543

  • e-ISSN

    1366-588X

  • Volume of the periodical

    61

  • Issue of the periodical within the volume

    1

  • Country of publishing house

    GB - UNITED KINGDOM

  • Number of pages

    18

  • Pages from-to

    302-319

  • UT code for WoS article

    000631418900001

  • EID of the result in the Scopus database

    2-s2.0-85102925635