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

Author

Listed:
  • Joaquin Gonzalez

    (Departamento de Ingeniería Eléctrica y Computación, Instituto de Ingeniería y Tecnología, Universidad Autónoma de Ciudad Juárez, Ciudad Juárez 32310, Mexico)

  • Liliana Avelar Sosa

    (Departamento de Ingeniería Industrial y Manufactura, Instituto de Ingeniera y Tecnologa, Universidad Autónoma de Ciudad Juárez, Ciudad Juárez 32310, Mexico)

  • Gabriel Bravo

    (Departamento de Ingeniería Eléctrica y Computación, Instituto de Ingeniería y Tecnología, Universidad Autónoma de Ciudad Juárez, Ciudad Juárez 32310, Mexico)

  • Oliverio Cruz-Mejia

    (Departamento de Ingeniería Industrial, FES Aragón, Universidad Nacional Autónoma de México, México 57171, Mexico)

  • Jose-Manuel Mejia-Muñoz

    (Departamento de Ingeniería Eléctrica y Computación, Instituto de Ingeniería y Tecnología, Universidad Autónoma de Ciudad Juárez, Ciudad Juárez 32310, Mexico)

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.

Suggested Citation

  • Joaquin Gonzalez & Liliana Avelar Sosa & Gabriel Bravo & Oliverio Cruz-Mejia & Jose-Manuel Mejia-Muñoz, 2024. "Fog Computing and Industry 4.0 for Newsvendor Inventory Model Using Attention Mechanism and Gated Recurrent Unit," Logistics, MDPI, vol. 8(2), pages 1-14, June.
    • Handle: RePEc:gam:jlogis:v:8:y:2024:i:2:p:56-:d:1407867
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    References listed on IDEAS

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    4. Pirayesh Neghab, Davood & Khayyati, Siamak & Karaesmen, Fikri, 2022. "An integrated data-driven method using deep learning for a newsvendor problem with unobservable features," European Journal of Operational Research, Elsevier, vol. 302(2), pages 482-496.
    5. Afshin Oroojlooyjadid & Lawrence V. Snyder & Martin Takáč, 2020. "Applying deep learning to the newsvendor problem," IISE Transactions, Taylor & Francis Journals, vol. 52(4), pages 444-463, April.
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