Volume 26 , Issue 4 , PP: 122-136, 2025 | Cite this article as | XML | Html | PDF | Full Length Article
Nagarajan Deivanayagampillai 1 , Thangavel Bhuvaneswari 2 * , Yasothei Suppiah 3 , Kanchana Anbalagan 4
Doi: https://doi.org/10.54216/IJNS.260412
Fuzzy sets and probabilistic methodologies have been integrated with forecasting but do not simultaneously capture the truth, indeterminacy, and falsity—really the crux of Neutrosophic Logic (NL). There is no literature investigating the incorporation of neutrosophic numbers into deep architectures, in particular into Neutrosophic Neural Networks (NNNs) for demand forecasting. This contribution fills the gap with the presentation of a Neutrosophic Neural Network (NNN) model with uncertainty explicitly included, enhancing the reliability and explain ability of demand forecasting. Deep learning-based demand forecasting strategies involving the use of Random Forest regression and XGBoosting algorithms generally do not deal with uncertainty and imprecision related with real-world demand data. The current work introduces a new model Neutrosophic Neural Network (NNN) where Neutrosophic Logic (NL) is integrated into deep learning demand forecasting. A novel neutrosophic activation function and a Neutrosophic Mean Squared Error (NMSE) loss function are proposed study, is implemented with the Random Forest regression and XGBoosting algorithms, and trained using synthetic and real-world demand data. Experimental results establish the better performance of the NNN approach about forecasting accuracy, robustness, and uncertainty handling. The sensitivity analysis also confirms the flexibility of the model with different demand patterns. The work contributes significantly towards neutrosophic deep learning and the possibility of robust and interpretable demand forecasting for supply chain and business intelligence.
Demand Forecasting , Deep Learning , Neutrosophic Neural Network , Uncertainty Modeling , LSTM , Supply Chain Optimization ,
[1] Subramanian, B., Mishra, A., Bharathi V, R., Mandala, G., Kathamuthu, N. D., & Srithar, S. (2025). Big data and fuzzy logic for demand forecasting in supply chain management: A data-driven approach. Journal of Fuzzy Extension and Applications, 6(2), 260-283.
[2] Arguelles JR, P., & Polkowski, Z. (2023). Impact of big data on supply chain performance through demand forecasting. International Journal of Computations, Information and Manufacturing (IJCIM), 3(1), 19-26.
[3] Bhende, M., Goel, A., Pathak, A., Tamrakar, P., Lal, M., Sharma, S., ... & Mali, M. (2024, August). A critical analysis in identifying the major factors of big data analytics in enhancing the supply chain management process for sustainable development–A machine learning approach. In AIP Conference Proceedings (Vol. 3139, No. 1). AIP Publishing.
[4] Ghazal, T. M. (2022). Energy demand forecasting using fused machine learning approaches. Intelligent Automation & Soft Computing, 31(1), 539-553.
[5] Kisku, T., & Dash, S. (2024, July). Optimization and Leveraging of Peak Demand Using LSTM and Fuzzy Logic Approaches in Demand-Side Management. In 2024 IEEE International Conference on Smart Power Control and Renewable Energy (ICSPCRE) (pp. 1-5). IEEE.
[6] Joseph, R. V., Mohanty, A., Tyagi, S., Mishra, S., Satapathy, S. K., & Mohanty, S. N. (2022). A hybrid deep learning framework with CNN and Bi-directional LSTM for store item demand forecasting. Computers and Electrical Engineering, 103, 108358.
[7] Punia, S., & Shankar, S. (2022). Predictive analytics for demand forecasting: A deep learning-based decision support system. Knowledge-Based Systems, 258, 109956.
[8] Douaioui, K., Oucheikh, R., Benmoussa, O., & Mabrouki, C. (2024). Machine Learning and Deep Learning Models for Demand Forecasting in Supply Chain Management: A Critical Review. Applied System Innovation (ASI), 7(5).
[9] Kumar, S., & Singh, R. (2021). A novel approach for demand forecasting using hybrid machine learning techniques. Journal of Ambient Intelligence and Humanized Computing, 12(3), 3459-3470.
[10] Zhang, Y., & Wang, H. (2022). Demand forecasting in retail: A comparative study of machine learning models. Applied Sciences, 12(8), 4015.
[11] Saha, P., Gudheniya, N., Mitra, R., Das, D., Narayana, S., & Tiwari, M. K. (2022). Demand forecasting of a multinational retail company using deep learning frameworks. IFAC-PapersOnLine, 55(10), 395-399.
[12] Cui, L., Chen, Y., Deng, J., & Han, Z. (2024). A novel attLSTM framework combining the attention mechanism and bidirectional LSTM for demand forecasting. Expert Systems with Applications, 254, 124409.
[13] Oukassi, H., Hasni, M., & Layeb, S. B. (2023, April). Long short-term memory networks for forecasting demand in the case of automotive manufacturing industry. In 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET) (pp. 01-06). IEEE.
[14] Kaya, K., Yılmaz, Y., Yaslan, Y., Öğüdücü, Ş. G., & Çıngı, F. (2022). Demand forecasting model using hotel clustering findings for hospitality industry. Information Processing & Management, 59(1), 102816.
[15] Mitra, A., Jain, A., Kishore, A., & Kumar, P. (2022, September). A comparative study of demand forecasting models for a multi-channel retail company: a novel hybrid machine learning approach. In Operations Research Forum (Vol. 3, No. 4, p. 58). Cham: Springer International Publishing.
[16] Punia, S., Nikolopoulos, K., Singh, S. P., Madaan, J. K., & Litsiou, K. (2020). Deep learning with long short-term memory networks and random forests for demand forecasting in multi-channel retail. International Journal of Production Research, 58(16), 4964-4979.
[17] Yeasmin, N., Amin, S. H., & Tosarkani, B. M. (2022). Machine Learning Techniques for Grocery Sales Forecasting by Analyzing Historical Data. Artificial Intelligence in Industrial Applications: Approaches to Solve the Intrinsic Industrial Optimization Problems, 21-36.
[18] Turgut, Y., & Erdem, M. (2022). Forecasting of retail produce sales based on XGBoost algorithm. In Industrial Engineering in the Internet-of-Things World: Selected Papers from the Virtual Global Joint Conference on Industrial Engineering and Its Application Areas, GJCIE 2020, August 14–15, 2020 (pp. 27-43). Springer International Publishing.
[19] Ibrahima, C. S., Xue, J., & Gueye, T. (2021). Inventory management and demand forecasting Improvement of a forecasting model based on artificial neural networks. Journal of Management Science & Engineering Research, 4(2), 33-39.
[20] Nassibi, N., Fasihuddin, H., & Hsairi, L. (2023). Demand forecasting models for food industry by utilizing machine learning approaches. International Journal of Advanced Computer Science and Applications, 14(3), 892-898.
[21] Kharfan, M., Chan, V. W. K., & Firdolas Efendigil, T. (2021). A data-driven forecasting approach for newly launched seasonal products by leveraging machine-learning approaches. Annals of Operations Research, 303(1), 159-174.
[22] Goel, L., Nandal, N., Gupta, S., Karanam, M., Prasanna Yeluri, L., Pandey, A. K., ... & Grabovy, P. (2024). Revealing the dynamics of demand forecasting in supply chain management: a holistic investigation. Cogent Engineering, 11(1), 2368104.
[23] Aamer, A., Eka Yani, L., & Alan Priyatna, I. (2020). Data analytics in the supply chain management: Review of machine learning applications in demand forecasting. Operations and Supply Chain Management: An International Journal, 14(1), 1-13.
[24] Ampazis, N. (2015). Forecasting demand in supply chain using machine learning algorithms. International Journal of Artificial Life Research (IJALR), 5(1), 56-73.
[25] Rodrigues, M., Migueis, V., Freitas, S., & Machado, T. (2024). Machine learning models for short-term demand forecasting in food catering services: A solution to reduce food waste. Journal of Cleaner Production, 435, 140265.
[26] Sundar, G. N., Xavier, K. A., Narmadha, D., Sagayam, K. M., Jone, A. A. A., Pomplun, M., & Dang, H. (2023). Intelligent computational techniques of machine learning models for demand analysis and prediction. International Journal of Intelligent Information and Database Systems, 16(1), 39-61.
[27] Weraikat, D., Thasin, S., Zeidan, C., Ishtiaq, M., & Acquaye, A. (2021). Demand Forecasting with Real Case Analysis for Effective Retail Decision-Making. In 4th European International Conference on Industrial Engineering and Operations Management, IEOM 2021 (pp. 1782-1783).
[28] Taparia, V., Mishra, P., Gupta, N., & Kumar, D. (2024). Improved demand forecasting of a retail store using a hybrid machine learning model. Journal of Graphic Era University, 15-36.
[29] Sharma, M., Kandasamy, I., & Vasantha, W. B. (2021). Comparison of neutrosophic approach to various deep learning models for sentiment analysis. Knowledge-Based Systems, 223, 107058.
[30] Rahaman, M., Haque, R., Salahshour, S., Alam, S., & Prasad, M. S. (2024). Analysis of a demand-driven production inventory model in a trapezoidal neutrosophic number-ruled decision environment. Yugoslav Journal of Operations Research, (00), 52-52.
