Predicting the Efficiency of Inventory Management Using Artificial Neural Networks

Hajeb, Hamidreza; Banafi, Mohammad

doi:10.5281/zenodo.7421138

Document Type : Original Research

Authors

Hamidreza Hajeb ¹
Mohammad Banafi ²

¹ Business and Economics School, Persian Gulf University, Bushehr, Iran

² Faculty of Economics and Social Sciences, Shahid Chamran University of Ahvaz, Ahvaz, Iran

https://doi.org/10.5281/zenodo.7421138

Abstract

The purpose of this study is to design a model to predict the efficiency of inventory management to help creditors and actual and potential investors and other stakeholders to avoid major losses in the capital market. For this reason, 137 companies listed on the Tehran Stock Exchange during the 10-years period 2012-2021 were examined. In this study, the predicting variables of institutional ownership, managerial ownership, corporate ownership, ownership concentration, board size, percentage of non-executive board members, and duality of CEO (Chief Executive Officer) role have been used. The efficiency of inventory management was predicted using a three-layer perceptron artificial neural network with the Backpropagation of Error algorithm. Finally, a network with the mean squared error of 0.360, 0.428, 0.261 and 0.353, respectively for training data, validation, test and total data and a coefficient of determination of more than 72%, as the best network Selected.

Keywords

Main Subjects

COPYRIGHTS

©2022 The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, as long as the original authors and source are cited. No permission is required from the authors or the publishers.

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International Journal of Management, Accounting and Economics

Predicting the Efficiency of Inventory Management Using Artificial Neural Networks

References

References

Volume 9, Issue 11 - Serial Number 100
November 2022
Pages 703-718

Predicting the Efficiency of Inventory Management Using Artificial Neural Networks

References

References

Volume 9, Issue 11 - Serial Number 100November 2022Pages 703-718

Volume 9, Issue 11 - Serial Number 100
November 2022
Pages 703-718