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Fog Computing and Industry 4.0 for Newsvendor Inventory Model Using Attention Mechanism and Gated Recurrent Unit

Authors :
Joaquin Gonzalez
Liliana Avelar Sosa
Gabriel Bravo
Oliverio Cruz-Mejia
Jose-Manuel Mejia-Muñoz
Source :
Logistics, Vol 8, Iss 2, p 56 (2024)
Publication Year :
2024
Publisher :
MDPI AG, 2024.

Abstract

Background: Efficient inventory management is critical for sustainability in supply chains. However, maintaining adequate inventory levels becomes challenging in the face of unpredictable demand patterns. Furthermore, the need to disseminate demand-related information throughout a company often relies on cloud services. However, this method sometimes encounters issues such as limited bandwidth and increased latency. Methods: To address these challenges, our study introduces a system that incorporates a machine learning algorithm to address inventory-related uncertainties arising from demand fluctuations. Our approach involves the use of an attention mechanism for accurate demand prediction. We combine it with the Newsvendor model to determine optimal inventory levels. The system is integrated with fog computing to facilitate the rapid dissemination of information throughout the company. Results: In experiments, we compare the proposed system with the conventional demand estimation approach based on historical data and observe that the proposed system consistently outperformed the conventional approach. Conclusions: This research introduces an inventory management system based on a novel deep learning architecture that integrates the attention mechanism with cloud computing to address the Newsvendor problem. Experiments demonstrate the better accuracy of this system in comparison to existing methods. More studies should be conducted to explore its applicability to other demand modeling scenarios.

Details

Language :
English
ISSN :
23056290
Volume :
8
Issue :
2
Database :
Directory of Open Access Journals
Journal :
Logistics
Publication Type :
Academic Journal
Accession number :
edsdoj.255263ee62104abd94e0940cb8e1268d
Document Type :
article
Full Text :
https://doi.org/10.3390/logistics8020056