Management and Economics
| Open Access |
DOI: AI-Driven Demand Forecasting for Multi-Echelon Supply Chains: Enhancing Forecasting Accuracy and Operational Efficiency through Machine Learning and Deep Learning Techniques.
Mohammad Iftekhar Ayub , Master of Science in Information Technology, Washington University of Science and Technology, USA Arun Kumar Gharami , Master of science in computer science, Westcliff university, USA Fariha Noor Nitu , MS in Management Science & Supply Chain Management, Wichita State University, USA Mohammad Nasir Uddin , Masters of Business Administration, Major in Data Analytics, Westcliff University, USA Md Iftakhayrul Islam , MBA in Management Information Systems, International American University, USA Alifa Majumder Nijhum , MS of Information Technology Project Management, major in project management and Digital Marketing, St Francis College, USA Molay Kumar Roy , Ms in Digital Marketing & Information Technology Management, St. Francis College, USA Syed Yezdani , Master’s in computer science, Saint Leo University, Tampa, Florida.Abstract
Demand forecasting plays a crucial role in optimizing supply chain operations, particularly in multi-echelon supply chains where goods move through various stages, including manufacturers, wholesalers, and retailers. Traditional time-series models like ARIMA and SARIMA have been widely used for demand forecasting, but their limitations in handling complex, non-linear relationships and incorporating external factors such as promotions and weather events have led to the exploration of machine learning (ML) and deep learning (DL) techniques. This study evaluates and compares the performance of AI-driven demand forecasting models, including ARIMA, SARIMA, Random Forest (RF), Gradient Boosting Machines (GBM), and Long Short-Term Memory (LSTM) networks. The results demonstrate that the LSTM model outperforms traditional methods and other machine learning algorithms in terms of accuracy, as measured by lower MAE, RMSE, and MAPE values across all echelons of the supply chain (retailer, wholesaler, and manufacturer). The superior performance of LSTM highlights its ability to capture long-term dependencies and handle the complexity of multi-echelon supply chains. This study provides valuable insights into the effectiveness of AI-driven forecasting models for real-world supply chain applications, particularly in managing dynamic demand patterns and optimizing operations.
Keywords
Demand forecasting, multi-echelon supply chain, Machine learning, Deep learning
References
Bai, C., Zhang, G., & Wu, S. (2019). Machine learning models for demand forecasting in the retail industry: A review. International Journal of Production Economics, 217, 230-243. https://doi.org/10.1016/j.ijpe.2019.03.021
Box, G. E., & Jenkins, G. M. (1976). Time series analysis: Forecasting and control (2nd ed.). Holden-Day.
Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5-32. https://doi.org/10.1023/A:1010933404324
Chen, Y., Wang, D., & Zhang, J. (2021). Deep learning for multi-echelon demand forecasting in supply chains. Computers & Industrial Engineering, 156, 107272. https://doi.org/10.1016/j.cie.2021.107272
Chong, A. Y. L., Ch'ng, E., & Li, B. (2017). Predicting demand for e-commerce: A machine learning approach. Computers & Industrial Engineering, 113, 118-126. https://doi.org/10.1016/j.cie.2017.09.017
Friedman, J. H. (2001). Greedy function approximation: A gradient boosting machine. Annals of Statistics, 29(5), 1189-1232. https://doi.org/10.1214/aos/1013203451
Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9(8), 1735-1780. https://doi.org/10.1162/neco.1997.9.8.1735
Hyndman, R. J., & Athanasopoulos, G. (2018). Forecasting: principles and practice (2nd ed.). OTexts.
Li, C., Zhang, J., & Lee, L. H. (2020). Demand forecasting in multi-echelon supply chains: A deep learning approach. Computers & Industrial Engineering, 149, 106775. https://doi.org/10.1016/j.cie.2020.106775
Shi, Y., Xie, L., & Zhang, L. (2019). Demand forecasting using LSTM network: A case study in retail. Proceedings of the 2019 International Conference on Data Mining and Big Data, 130-135. https://doi.org/10.1109/ICDMW.2019.00036
Xie, Y., Zhang, Y., & Li, L. (2018). A deep learning framework for demand forecasting in supply chain. International Journal of Production Research, 56(19), 6004-6017. https://doi.org/10.1080/00207543.2018.1444279
Zhang, Y., Yu, H., & Zhang, Z. (2018). Hybrid forecasting model based on ARIMA and support vector machine for demand forecasting in supply chain. Applied Soft Computing, 70, 452-462. https://doi.org/10.1016/j.asoc.2018.06.017
Bhattacharjee, B., Mou, S. N., Hossain, M. S., Rahman, M. K., Hassan, M. M., Rahman, N., ... & Haque, M. S. U. (2024). MACHINE LEARNING FOR COST ESTIMATION AND FORECASTING IN BANKING: A COMPARATIVE ANALYSIS OF ALGORITHMS. Frontline Marketing,Management and Economics Journal, 4(12), 66-83.
Hossain, S., Siddique, M. T., Hosen, M. M., Jamee, S. S., Akter, S., Akter, P., ... & Khan, M. S. (2025). Comparative Analysis of Sentiment Analysis Models for Consumer Feedback: Evaluating the Impact of Machine Learning and Deep Learning Approaches on Business Strategies. Frontline Social Sciences and History Journal, 5(02), 18-29.
Nath, F., Chowdhury, M. O. S., & Rhaman, M. M. (2023). Navigating produced water sustainability in the oil and gas sector: A Critical review of reuse challenges, treatment technologies, and prospects ahead. Water, 15(23), 4088.
PHAN, H. T. N., & AKTER, A. (2024). HYBRID MACHINE LEARNING APPROACH FOR ORAL CANCER DIAGNOSIS AND CLASSIFICATION USING HISTOPATHOLOGICAL IMAGES. Universal Publication Index e-Library, 63-76.
Hossain, S., Siddique, M. T., Hosen, M. M., Jamee, S. S., Akter, S., Akter, P., ... & Khan, M. S. (2025). Comparative Analysis of Sentiment Analysis Models for Consumer Feedback: Evaluating the Impact of Machine Learning and Deep Learning Approaches on Business Strategies. Frontline Social Sciences and History Journal, 5(02), 18-29.
Nath, F., Asish, S., Debi, H. R., Chowdhury, M. O. S., Zamora, Z. J., & Muñoz, S. (2023, August). Predicting hydrocarbon production behavior in heterogeneous reservoir utilizing deep learning models. In Unconventional Resources Technology Conference, 13–15 June 2023 (pp. 506-521). Unconventional Resources Technology Conference (URTeC).
Ahmmed, M. J., Rahman, M. M., Das, A. C., Das, P., Pervin, T., Afrin, S., ... & Rahman, N. (2024). COMPARATIVE ANALYSIS OF MACHINE LEARNING ALGORITHMS FOR BANKING FRAUD DETECTION: A STUDY ON PERFORMANCE, PRECISION, AND REAL-TIME APPLICATION. American Research Index Library, 31-44.
Akhi, S. S., Shakil, F., Dey, S. K., Tusher, M. I., Kamruzzaman, F., Jamee, S. S., ... & Rahman, N. (2025). Enhancing Banking Cybersecurity: An Ensemble-Based Predictive Machine Learning Approach. The American Journal of Engineering and Technology, 7(03), 88-97.
Pabel, M. A. H., Bhattacharjee, B., Dey, S. K., Jamee, S. S., Obaid, M. O., Mia, M. S., ... & Sharif, M. K. (2025). BUSINESS ANALYTICS FOR CUSTOMER SEGMENTATION: A COMPARATIVE STUDY OF MACHINE LEARNING ALGORITHMS IN PERSONALIZED BANKING SERVICES. American Research Index Library, 1-13.
Siddique, M. T., Jamee, S. S., Sajal, A., Mou, S. N., Mahin, M. R. H., Obaid, M. O., ... & Hasan, M. (2025). Enhancing Automated Trading with Sentiment Analysis: Leveraging Large Language Models for Stock Market Predictions. The American Journal of Engineering and Technology, 7(03), 185-195.
Mohammad Iftekhar Ayub, Biswanath Bhattacharjee, Pinky Akter, Mohammad Nasir Uddin, Arun Kumar Gharami, Md Iftakhayrul Islam, Shaidul Islam Suhan, Md Sayem Khan, & Lisa Chambugong. (2025). Deep Learning for Real-Time Fraud Detection: Enhancing Credit Card Security in Banking Systems. The American Journal of Engineering and Technology, 7(04), 141–150. https://doi.org/10.37547/tajet/Volume07Issue04-19
Nguyen, A. T. P., Jewel, R. M., & Akter, A. (2025). Comparative Analysis of Machine Learning Models for Automated Skin Cancer Detection: Advancements in Diagnostic Accuracy and AI Integration. The American Journal of Medical Sciences and Pharmaceutical Research, 7(01), 15-26.
Nguyen, A. T. P., Shak, M. S., & Al-Imran, M. (2024). ADVANCING EARLY SKIN CANCER DETECTION: A COMPARATIVE ANALYSIS OF MACHINE LEARNING ALGORITHMS FOR MELANOMA DIAGNOSIS USING DERMOSCOPIC IMAGES. International Journal of Medical Science and Public Health Research, 5(12), 119-133.
Phan, H. T. N., & Akter, A. (2025). Predicting the Effectiveness of Laser Therapy in Periodontal Diseases Using Machine Learning Models. The American Journal of Medical Sciences and Pharmaceutical Research, 7(01), 27-37.
Phan, H. T. N. (2024). EARLY DETECTION OF ORAL DISEASES USING MACHINE LEARNING: A COMPARATIVE STUDY OF PREDICTIVE MODELS AND DIAGNOSTIC ACCURACY. International Journal of Medical Science and Public Health Research, 5(12), 107-118.
Al Mamun, A., Nath, A., Dey, S. K., Nath, P. C., Rahman, M. M., Shorna, J. F., & Anjum, N. (2025). Real-Time Malware Detection in Cloud Infrastructures Using Convolutional Neural Networks: A Deep Learning Framework for Enhanced Cybersecurity. The American Journal of Engineering and Technology, 7(03), 252-261.
Akhi, S. S., Shakil, F., Dey, S. K., Tusher, M. I., Kamruzzaman, F., Jamee, S. S., ... & Rahman, N. (2025). Enhancing Banking Cybersecurity: An Ensemble-Based Predictive Machine Learning Approach. The American Journal of Engineering and Technology, 7(03), 88-97.
Mazharul Islam Tusher, “Deep Learning Meets Early Diagnosis: A Hybrid CNN-DNN Framework for Lung Cancer Prediction and Clinical Translation”, ijmsphr, vol. 6, no. 05, pp. 63–72, May 2025.
Integrating Consumer Sentiment and Deep Learning for GDP Forecasting: A Novel Approach in Financial Industry”., Int Bus & Eco Adv Jou, vol. 6, no. 05, pp. 90–101, May 2025, doi: 10.55640/business/volume06issue05-05.
Tamanna Pervin, Sharmin Akter, Sadia Afrin, Md Refat Hossain, MD Sajedul Karim Chy, Sadia Akter, Md Minzamul Hasan, Md Mafuzur Rahman, & Chowdhury Amin Abdullah. (2025). A Hybrid CNN-LSTM Approach for Detecting Anomalous Bank Transactions: Enhancing Financial Fraud Detection Accuracy. The American Journal of Management and Economics Innovations, 7(04), 116–123. https://doi.org/10.37547/tajmei/Volume07Issue04-15
Mohammad Iftekhar Ayub, Biswanath Bhattacharjee, Pinky Akter, Mohammad Nasir Uddin, Arun Kumar Gharami, Md Iftakhayrul Islam, Shaidul Islam Suhan, Md Sayem Khan, & Lisa Chambugong. (2025). Deep Learning for Real-Time Fraud Detection: Enhancing Credit Card Security in Banking Systems. The American Journal of Engineering and Technology, 7(04), 141–150. https://doi.org/10.37547/tajet/Volume07Issue04-19
Mazharul Islam Tusher, Han Thi Ngoc Phan, Arjina Akter, Md Rayhan Hassan Mahin, & Estak Ahmed. (2025). A Machine Learning Ensemble Approach for Early Detection of Oral Cancer: Integrating Clinical Data and Imaging Analysis in the Public Health. International Journal of Medical Science and Public Health Research, 6(04), 07–15. https://doi.org/10.37547/ijmsphr/Volume06Issue04-02
Safayet Hossain, Ashadujjaman Sajal, Sakib Salam Jamee, Sanjida Akter Tisha, Md Tarake Siddique, Md Omar Obaid, MD Sajedul Karim Chy, & Md Sayem Ul Haque. (2025). Comparative Analysis of Machine Learning Models for Credit Risk Prediction in Banking Systems. The American Journal of Engineering and Technology, 7(04), 22–33. https://doi.org/10.37547/tajet/Volume07Issue04-04
Ayub, M. I., Bhattacharjee, B., Akter, P., Uddin, M. N., Gharami, A. K., Islam, M. I., ... & Chambugong, L. (2025). Deep Learning for Real-Time Fraud Detection: Enhancing Credit Card Security in Banking Systems. The American Journal of Engineering and Technology, 7(04), 141-150.
Siddique, M. T., Uddin, M. J., Chambugong, L., Nijhum, A. M., Uddin, M. N., Shahid, R., ... & Ahmed, M. (2025). AI-Powered Sentiment Analytics in Banking: A BERT and LSTM Perspective. International Interdisciplinary Business Economics Advancement Journal, 6(05), 135-147.
Thakur, K., Sayed, M. A., Tisha, S. A., Alam, M. K., Hasan, M. T., Shorna, J. F., ... & Ayon, E. H. (2025). Multimodal Deepfake Detection Using Transformer-Based Large Language Models: A Path Toward Secure Media and Clinical Integrity. The American Journal of Engineering and Technology, 7(05), 169-177.
Al Mamun, A., Nath, A., Dey, S. K., Nath, P. C., Rahman, M. M., Shorna, J. F., & Anjum, N. (2025). Real-Time Malware Detection in Cloud Infrastructures Using Convolutional Neural Networks: A Deep Learning Framework for Enhanced Cybersecurity. The American Journal of Engineering and Technology, 7(03), 252-261.
Tusher, M. I., Hasan, M. M., Akter, S., Haider, M., Chy, M. S. K., Akhi, S. S., ... & Shaima, M. (2025). Deep Learning Meets Early Diagnosis: A Hybrid CNN-DNN Framework for Lung Cancer Prediction and Clinical Translation. International Journal of Medical Science and Public Health Research, 6(05), 63-72.
Sajal, A., Chy, M. S. K., Jamee, S. S., Uddin, M. N., Khan, M. S., Gharami, A. K., ... & Ahmed, M. (2025). Forecasting Bank Profitability Using Deep Learning and Macroeconomic Indicators: A Comparative Model Study. International Interdisciplinary Business Economics Advancement Journal, 6(06), 08-20.
Paresh Chandra Nath, Md Sajedul Karim Chy, Md Refat Hossain, Md Rashel Miah, Sakib Salam Jamee, Mohammad Kawsur Sharif, Md Shakhaowat Hossain, & Mousumi Ahmed. (2025). Comparative Performance of Large Language Models for Sentiment Analysis of Consumer Feedback in the Banking Sector: Accuracy, Efficiency, and Practical Deployment. Frontline Marketing, Management and Economics Journal, 5(06), 07–19. https://doi.org/10.37547/marketing-fmmej-05-06-02
Hossain, S., Siddique, M. T., Hosen, M. M., Jamee, S. S., Akter, S., Akter, P., ... & Khan, M. S. (2025). Comparative Analysis of Sentiment Analysis Models for Consumer Feedback: Evaluating the Impact of Machine Learning and Deep Learning Approaches on Business Strategies. Frontline Social Sciences and History Journal, 5(02), 18-29.
Jamee, S. S., Sajal, A., Obaid, M. O., Uddin, M. N., Haque, M. S. U., Gharami, A. K., ... & FARHAN, M. (2025). Integrating Consumer Sentiment and Deep Learning for GDP Forecasting: A Novel Approach in Financial Industry. International Interdisciplinary Business Economics Advancement Journal, 6(05), 90-101.
Hossain, S., Sajal, A., Jamee, S. S., Tisha, S. A., Siddique, M. T., Obaid, M. O., ... & Haque, M. S. U. (2025). Comparative Analysis of Machine Learning Models for Credit Risk Prediction in Banking Systems. The American Journal of Engineering and Technology, 7(04), 22-33.
Pabel, M. A. H., Bhattacharjee, B., Dey, S. K., Jamee, S. S., Obaid, M. O., Mia, M. S., ... & Sharif, M. K. BUSINESS ANALYTICS FOR CUSTOMER SEGMENTATION: A COMPARATIVE STUDY OF MACHINE LEARNING ALGORITHMS IN PERSONALIZED BANKING SERVICES.
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Copyright (c) 2025 Mohammad Iftekhar Ayub, Arun Kumar Gharami, Fariha Noor Nitu, Mohammad Nasir Uddin, Md Iftakhayrul Islam, Alifa Majumder Nijhum, Molay Kumar Roy, Syed Yezdani
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