Journal of Soft Computing Paradigm
view PDF
How to Cite
R., Rampriya, Mohamed Al Favaj M., and Shaarukeish E S. 2026. “MRP•NEXUS: Intelligent Demand Forecasting and Inventory Optimization Using Machine Learning”. Journal of Soft Computing Paradigm 8 (3): 280-95. https://doi.org/10.36548/jscp.2026.3.007.
Keywords
MRP•NEXUS
Ensemble Demand Forecasting
Intelligent Inventory Optimization
Economic Order Quantity
Safety Stock Modeling
Supply Chain Decision Support
Inventory Conflict Detection
Retail Demand Analytics
Abstract
The accurate demand forecasting and inventory optimization cannot be overstated when it comes to having efficient and smooth supply chain processes and reducing costs associated with inventory and risks connected with it. Inventory control systems that use traditional methods of operation depend on manual planning and separate forecasting process, which makes them ineffective in responding to dynamically changing demand patterns. In this paper, an intelligent decision support system is proposed by combining an ensemble machine learning algorithm with material requirements planning to enable forecasting and inventory optimization and detect conflicts in this process. The forecasted demand is then used to calculate Economic Order Quantity (EOQ), Safety Stock, and Reorder Point, whereas a conflict detection mechanism automatically detects important inventory states. The proposed framework has been developed as a web application with the help of Flask and SQLite, which supports forecasting, inventory tracking, and procurement planning in an interactive dashboard. The experimental analysis shows the accuracy of 91.3% with MAPE of 8.7% and MAE of 35.6, reflecting the efficiency of the framework for intelligent inventory management.References
- Hartmut Stadtler, Christoph Kilger, and Herbert Meyr. Supply Chain Management and Advanced Planning: Concepts, Models, software, and Case Studies. Springer, 2015: Fifth Edition.
- M. Christopher, Logistics and Supply Chain Management, Harlow: Pearson, 2016: Third Edition.
- Saini, Priyesh, and S. K. Parida. "A Novel Probabilistic Gradient Boosting Model with Multi-approach Feature Selection and Iterative Seasonal Trend Decomposition for Short-term Load Forecasting." Energy, 2024, vol. 294: 130975.
- Makridakis, Spyros, Evangelos Spiliotis, and Vassilios Assimakopoulos. "The M4 Competition: 100,000 Time Series and 61 Forecasting Methods." International Journal of Forecasting 2020, vol. 36, no. 1: 54-74.
- Shak, Md Shujan, Md Shahin Alam Mozumder, Md Amit Hasan, Ashim Chandra Das, Md Rashel Miah, Salma Akter, and Md Nur Hossain. "Optimizing Retail Demand Forecasting: A performance Evaluation of Machine Learning Models Including lstm and Gradient Boosting." The American Journal of Engineering and Technology, 2024, vol. 6, no. 09: 67-80. https://www.theamericanjournals.com/index.php/tajet/article/view/5438/5045
- Peter, Sven, Ferran Diego, Fred A. Hamprecht, and Boaz Nadler. "Cost Efficient Gradient Boosting." 31st Conference on Advances in neural information processing systems 2017, vol. 30: 1-11.
- Manik, Diomen Syahputra, Nazaruddin Matondang, and Nismah Panjaitan. "The Use of Machine Learning Algorithms for Supply Chain Optimization at PT. XYZ." Jurnal Sistem Teknik Industri, 2026, vol. 28, no. 1: 11-21.
- Bennett, Karen. "Having a Part Twice Over." Australasian Journal of Philosophy 2013, vol. 91, no. 1: 83-103.
- Silver, Edward A., David F. Pyke, and Douglas J. Thomas. Inventory and Production Management in Supply Chains. CRC Press, 2016: Fourth Edition.
- Chen, F., Drezner, Z., Ryan, J.K. and Simchi-Levi, D., Quantifying the Bullwhip Effect in a Simple Supply Chain: The Impact of Forecasting, Lead Times, and Information. Management Science 2000, vol. 46, no. 3: 436-443.
- Hyndman, Rob J., and Anne B. Koehler. "Another Look at Measures of Forecast Accuracy." International Journal of Forecasting 2006, vol. 22, no. 4: 679-688.
- M5 Forecasting Accuracy Competition 2026, Kaggle. Available: https://www.kaggle.com/competitions/m5-forecasting-accuracy