Journal of Smart Computing ISSN 2979-8752 · Biannual
Register Log in
Research Article · Vol. 1, No. 1 (2025)

An IoT-Enabled Smart Supply Chain Management Framework: Integrating Real-Time Visibility, Traceability, and LSTM-Based Demand Forecasting

Milad javadi*

Economics, School of Economic, Political and Policy Sciences (EPPS), The University of Texas at Dallas, Richardson, Texas, USA

* Corresponding author: Milad.javadi@utdallas.edu

Abstract The increasing complexity of global supply chains, amplified by major disruptions such as the COVID-19 pandemic, has made advanced digital technologies central to supply chain resilience and operational efficiency. This paper proposes IoT-SCM-Vision, a four-layer Internet of Things (IoT) architecture designed to improve real-time supply chain visibility, end-to-end product traceability, and demand-forecasting accuracy. The framework integrates heterogeneous IoT sensors at the perception layer; an MQTT-based communication layer with edge computing nodes; an intelligence layer that combines Long Short-Term Memory (LSTM) demand forecasting with autoencoder-based anomaly detection; and a decision-support layer that converts model outputs into managerial recommendations. On a simulated five-tier supply chain with twelve months of multi-product data, IoT-SCM-Vision attains a Mean Absolute Percentage Error (MAPE) of 4.73% for demand forecasting, outperforming ARIMA (9.12%) and a simple recurrent neural network (6.84%). Relative to a non-IoT baseline, it also reduces inventory holding costs by 18.2%, increases on-time delivery by 22.7%, and shortens disruption detection time from 48.2 hours to 3.7 hours. The results indicate that integrating IoT sensing with learning-based analytics can improve operational performance across multiple supply-chain metrics.

Keywords: Internet of Things, Supply Chain Management , Demand Forecasting, Long Short-Term Memory, Real-Time Visibility

DOI
10.5281/zenodo.22995355
Pages
63-75
Published
17 Dec 2025
Download PDF

To cite this article: javadi, M. (2025). An IoT-Enabled Smart Supply Chain Management Framework: Integrating Real-Time Visibility, Traceability, and LSTM-Based Demand Forecasting. Journal of Smart Computing , 1(1) , 63-75. https://doi.org/10.5281/zenodo.22995355

References

  1. Akbari, M., & Do, T. N. A. (2021). A systematic review of machine learning in logistics and supply chain management: Current trends and future directions. Benchmarking: An International Journal, 28(10), 2977–3005.
  2. Al-Rakhami, M. S., & Al-Mashari, M. (2021). A blockchain-based trust model for the Internet of Things supply chain management. Sensors, 21(5), 1759.
  3. Alzahrani, A., & Asghar, M. Z. (2023). Intelligent risk prediction system in IoT-based supply chain management in the logistics sector. Electronics, 12(13), 2760.
  4. Atzori, L., Iera, A., & Morabito, G. (2010). The Internet of Things: A survey. Computer Networks, 54(15), 2787–2805.
  5. Azizi, N., Malekzadeh, H., Akhavan, P., Haass, O., Saremi, S., & Mirjalili, S. (2021). IoT–Blockchain: Harnessing the power of the Internet of Thing and blockchain for smart supply chain. Sensors, 21(18), 6048.
  6. Ben-Daya, M., Hassini, E., & Bahroun, Z. (2019). Internet of things and supply chain management: A literature review. International Journal of Production Research, 57(15–16), 4719–4742.
  7. Birkel, H. S., & Hartmann, E. (2020). Internet of Things – the future of managing supply chain risks. Supply Chain Management: An International Journal, 25(5), 535–548.
  8. Boyes, H., Hallaq, B., Cunningham, J., & Watson, T. (2018). The industrial Internet of Things (IIoT): An analysis framework. Computers in Industry, 101, 1–12.
  9. Dong, Z., Liang, W., Liang, Y., Gao, W., & Lu, Y. (2022). Blockchained supply chain management based on IoT tracking and machine learning. EURASIP Journal on Wireless Communications and Networking, 2022, 203.
  10. Frederico, G. F., Garza-Reyes, J. A., Anosike, A., & Kumar, V. (2020). Supply Chain 4.0: Concepts, maturity and research agenda. Supply Chain Management: An International Journal, 25(2), 262–282.
  11. Haddud, A., DeSouza, A., Khare, A., & Lee, H. (2017). Examining potential benefits and challenges associated with the Internet of Things integration in supply chains. Journal of Manufacturing Technology Management, 28(8), 1055–1085.
  12. Koot, M., Mes, M. R. K., & Iacob, M. E. (2021). A systematic literature review of supply chain decision making supported by the Internet of Things and Big Data Analytics. Computers & Industrial Engineering, 154, 107076.
  13. Seyedan, M., & Mafakheri, F. (2020). Predictive big data analytics for supply chain demand forecasting: Methods, applications, and research opportunities. Journal of Big Data, 7(1), 53. https://doi.org/10.1186/s40537-020-00329-2
  14. Shahzad, A., Zhang, K., & Gherbi, A. (2020). Intuitive development to examine collaborative IoT supply chain system underlying privacy and security levels and perspective powering through proactive blockchain. Sensors, 20(13), 3760.
  15. Song, Y., Yu, F. R., Zhou, L., Yang, X., & He, Z. (2021). Applications of the Internet of Things (IoT) in smart logistics: A comprehensive survey. IEEE Internet of Things Journal, 8(6), 4250–4274.
  16. Spieske, A., & Birkel, H. (2021). Improving supply chain resilience through Industry 4.0: A systematic literature review under the impressions of the COVID-19 pandemic. Computers & Industrial Engineering, 158, 107452.
  17. Tjahjono, B., Esplugues, C., Ares, E., & Pelaez, G. (2017). What does Industry 4.0 mean to supply chain? Procedia Manufacturing, 13, 1175–1182.
  18. Tsang, Y. P., Choy, K. L., Wu, C. H., Ho, G. T. S., Lam, C. H. Y., & Koo, P. S. (2018). An Internet of Things (IoT)-based risk monitoring system for managing cold supply chain risks. Industrial Management & Data Systems, 118(7), 1432–1462.
  19. Wamba, S. F., Gunasekaran, A., Akter, S., Ren, S. J., Dubey, R., & Childe, S. J. (2017). Big data analytics and firm performance: Effects of dynamic capabilities. Journal of Business Research, 70, 356–365.
  20. Wang, G., Gunasekaran, A., Ngai, E. W. T., & Papadopoulos, T. (2016). Big data analytics in logistics and supply chain management: Certain investigations for research and applications. International Journal of Production Economics, 176, 98–110.
CC BY Creative Commons Attribution 4.0
Creative Commons Attribution 4.0 International CC BY 4.0

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

Read the full CC BY 4.0 license →