SupplyVision AI: A Machine Learning-Driven Material Demand Forecasting System for Intelligent Supply Chain Management
Main Article Content
Abstract
— Modern supply chains are increasingly vulnerable to demand unpredictability, resulting in significant operational and financial inefficiencies. Traditional forecasting methods—reliant on manual estimation, simple statistical averages, and rigid rule-based systems—fail to adapt to the dynamic and complex nature of contemporary markets. This paper presents SupplyVision AI, a machine learning-driven material demand forecasting system designed to address the critical information, accuracy, and efficiency gaps inherent in conventional supply chain planning. The system employs Python-based machine learning models including Linear Regression and Random Forest Regressor, integrated within a Flask-powered web application, backed by a PostgreSQL relational database, and visualized through interactive Power BI dashboards. Performance evaluation was conducted using standard regression metrics including Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and the R² coefficient of determination. Functional and load testing validated the reliability and scalability of the deployed backend under realistic traffic conditions. The proposed framework demonstrates measurable improvements in forecasting accuracy, procurement planning efficiency, and overall supply chain responsiveness, offering a scalable, data-driven alternative to legacy forecasting paradigms
Article Details
Section
COPYRIGHT
Submission of a manuscript implies: that the work described has not been published before, that it is not under consideration for publication elsewhere; that if and when the manuscript is accepted for publication, the authors agree to automatic transfer of the copyright to the publisher.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work
- The journal allows the author(s) to retain publishing rights without restrictions.
- The journal allows the author(s) to hold the copyright without restrictions.
References
[1] T. J. Smoothey and R. K. Patel, "Machine Learning Applications in Supply Chain Demand Forecasting: A Systematic Review," IEEE Transactions on Engineering Management, vol. 69, no. 4, pp. 1423–1438, Aug. 2022.
[2] Y. Chen, H. Liu, and J. Zhang, "Random Forest-Based Demand Forecasting Model for Retail Supply Chains," in Proc. IEEE Int. Conf. Big Data and Smart Computing (BigComp), Busan, South Korea, 2021, pp. 234–241.
[3] A. K. Singh and P. Sharma, "Deep Learning vs. Traditional Machine Learning for Inventory Demand Prediction: A Comparative Analysis," International Journal of Production Economics, vol. 245, pp. 108–119, Mar. 2022.
[4] G. Palshikar and S. Athavale, "Demand Forecasting Using Machine Learning for E-Commerce Platforms," in Proc. IEEE Int. Conf. Advances in Computing, Communications and Informatics (ICACCI), Bangalore, India, 2018, pp. 1045–1050.
[5] F. Pedregosa et al., "Scikit-learn: Machine Learning in Python," Journal of Machine Learning Research, vol. 12, pp. 2825–2830, 2011.
[6] W. McKinney, "Data Structures for Statistical Computing in Python," in Proc. 9th Python in Science Conf. (SciPy), Austin, TX, USA, 2010, pp. 51–56.
[7] L. Breiman, "Random Forests," Machine Learning, vol. 45, no. 1, pp. 5–32, Oct. 2001.
[8] T. Hastie, R. Tibshirani, and J. Friedman, The Elements of Statistical Learning, 2nd ed. New York, NY, USA: Springer, 2009.
[9] J. Han, M. Kamber, and J. Pei, Data Mining: Concepts and Techniques, 3rd ed. Burlington, MA, USA: Morgan Kaufmann, 2011.
[10] I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning, Cambridge, MA, USA: MIT Press, 2016.