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In-Firm Planning and Business Processes Management Using Deep Neural Networks

Author

Listed:
  • Fedor Zagumennov

    (Plekhanov Russian University of Economics, Department of Industrial Economics, Moscow, Russia Author-2-Name: Andrei Bystrov Author-2-Workplace-Name: Plekhanov Russian University of Economics, Department of Industrial Economics, Moscow, Russia Author-3-Name: Alexey Radaykin Author-3-Workplace-Name: Plekhanov Russian University of Economics, Department of Industrial Economics, Moscow, Russia Author-4-Name: Author-4-Workplace-Name: Author-5-Name: Author-5-Workplace-Name: Author-6-Name: Author-6-Workplace-Name: Author-7-Name: Author-7-Workplace-Name: Author-8-Name: Author-8-Workplace-Name:)

Abstract

" Objective - The objective of this paper is to consider using machine learning approaches for in-firm processes prediction and to give an estimation of such values as effective production quantities. Methodology - The research methodology used is a synthesis of a deep-learning model, which is used to predict half of real business data for comparison with the remaining half. The structure of the convolutional neural network (CNN) model is provided, as well as the results of experiments with real orders, procurements, and income data. The key findings in this paper are that convolutional with a long-short-memory approach is better than a single convolutional method of prediction. Findings - This research also considers useof such technologies on business digital platforms. According to the results, there are guidelines formulated for the implementation in the particular ERP systems or web business platforms. Novelty - This paper describes the practical usage of 1-dimensional(1D) convolutional neural networks and a mixed approach with convolutional and long-short memory networks for in-firm planning tasks such as income prediction, procurements, and order demand analysis. Type of Paper - Empirical."

Suggested Citation

  • Fedor Zagumennov, 2021. "In-Firm Planning and Business Processes Management Using Deep Neural Networks," GATR Journals jber213, Global Academy of Training and Research (GATR) Enterprise.
    • Handle: RePEc:gtr:gatrjs:jber213
    DOI: https://doi.org/10.35609/jber.2021.6.3(4)
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    References listed on IDEAS

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    1. A. S. Kulyasova & A. R. Esina & V. D. Svirchevskiy, 2019. "Economic and mathematical modeling as an effective tool of the analysis of economic processes in industry," Russian Journal of Industrial Economics, MISIS, vol. 12(3).
    2. Leyla Gamidullaeva & Tatyana Tolstykh & Andrey Bystrov & Alexey Radaykin & Nadezhda Shmeleva, 2021. "Cross-Sectoral Digital Platform as a Tool for Innovation Ecosystem Development," Sustainability, MDPI, vol. 13(21), pages 1-24, October.
    Full references (including those not matched with items on IDEAS)

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    Cited by:

    1. Cruz, Yarens J. & Villalonga, Alberto & Castaño, Fernando & Rivas, Marcelino & Haber, Rodolfo E., 2024. "Automated machine learning methodology for optimizing production processes in small and medium-sized enterprises," Operations Research Perspectives, Elsevier, vol. 12(C).

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    More about this item

    Keywords

    Business; Neural; Networks; CNN; Platform;
    All these keywords.

    JEL classification:

    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • C49 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Other

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