Ibrahim Ahmed Omer, Rehab and Hassan, Raini and S. Abd. Aziz, Madihah (2026) A review and comparative analysis of predictive models for supply chain demand forecasting. In: 10th International Conference on Information and Communication Technology for the Muslim World (ICT4M 2025), 3rd February 2026, Kuala Lumpur, Malaysia.
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Abstract
Accurate demand forecasting is critical to supply chain optimization, influence inventory management, production scheduling, and customer satisfaction. This paper presents a comparative analysis of traditional forecasting models and machine learning approaches for supply chain demand prediction. This study reviews key techniques, including statistical time-series models, supervised and unsupervised learning algorithms, ensemble methods, and deep learning architectures. Empirical evidence from retail, manufacturing, healthcare, and food sectors demonstrates their relative performance and practical applicability. Challenges such as data quality, model interpretability, and system integration are analysed, along with emerging trends in real-time adaptability, hybrid modelling, and explainable AI. By synthesizing current research and implementation insights, this work provides a comprehensive evaluation of existing methods, identifying their strengths, limitations, and future research directions to enhance data-driven demand forecasting in modern supply chains.
| Item Type: | Proceeding Paper (Invited Papers) |
|---|---|
| Additional Information: | 4964/127400 |
| Uncontrolled Keywords: | Predictive analytics, Machine learning, Supply chain |
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science |
| Kulliyyahs/Centres/Divisions/Institutes (Can select more than one option. Press CONTROL button): | Kulliyyah of Information and Communication Technology > Department of Computer Science Kulliyyah of Information and Communication Technology > Department of Computer Science Kulliyyah of Information and Communication Technology Kulliyyah of Information and Communication Technology |
| Depositing User: | Dr. Raini Hassan |
| Date Deposited: | 19 Feb 2026 12:32 |
| Last Modified: | 19 Feb 2026 12:33 |
| Queue Number: | 2026-02-Q2118 |
| URI: | http://irep.iium.edu.my/id/eprint/127400 |
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