Product Demand Forecasting For Inventory Management with Freight Transportation Services Index Using Advanced Neural Networks Algorithm

Authors

  • Tuan Ngoc Nguyen VNDirect Securities, 97 Lo Duc, Hai Ba Trung, Hanoi, Vietnam
  • Md Munsur Khan College of Graduate and Professional Studies, Trine University, Angola, IN, USA
  • Md Zakir Hossain College of Science and Technology, Grand Canyon University, Phoenix, AZ, USA
  • Kazi Shaharair Sharif Department of Computer Science, Oklahoma State University, Stillwater, OK, USA
  • Radha Das Researcher, Dhaka, Bangladesh
  • Md Sabbirul Haque IEEE Research Community, IEEE, NJ, USA

DOI:

https://doi.org/10.47672/ajce.2432

Keywords:

Neural Networks (C45), Inventory Management (G31), Demand Forecasting (C53), TSI, Economic Environment (O11), Explainable Artificial Intelligence

Abstract

Purpose: Accurate demand forecasting is critical for optimizing inventory management, improving customer satisfaction, and maximizing profitability in the retail sector. Traditional forecasting models predominantly utilize micro-level variables, such as historical sales data and promotional activities, often neglecting the influence of macroeconomic conditions.

Materials and Methods: This study addresses this gap by integrating Freight Transportation Services Index (TSI) which is an indicator for overall economic health with time series data of retail product sales. Utilizing an advanced neural networks model, we demonstrate that incorporating macroeconomic variables significantly enhances the model's predictive accuracy and explanatory power.

Findings: The results reveal that the model with the TSI index outperforms conventional models, highlighting its potential for practical application in the industry.

Implications to Theory, Practice and Policy: This approach offers a more comprehensive understanding of demand dynamics, enabling businesses to make more informed decisions, adapt to market fluctuations, and maintain a competitive edge.

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References

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Published

2024-09-17

How to Cite

Nguyen, T. N., Khan, M. M., Hossain, M. Z., Sharif, K. . S., Radha Das, & Haque, M. S. (2024). Product Demand Forecasting For Inventory Management with Freight Transportation Services Index Using Advanced Neural Networks Algorithm. American Journal of Computing and Engineering, 7(4), 50–58. https://doi.org/10.47672/ajce.2432

Issue

Vol. 7 No. 4 (2024)

Section

Articles

License

Copyright (c) 2024 Tuan Ngoc Nguyen, Md Munsur Khan, Md Zakir Hossain, Kazi Shaharair Sharif, Radha Das, Md Sabbirul Haque

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This work is licensed under a Creative Commons Attribution 4.0 International License.

Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution (CC-BY) 4.0 License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.