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Stream/Area:
-
Subject:
-
Paper ID:
10.55041/IJSREM.AITCON048
Volume:
10 |
4 |
April 2026
Pages:
1-2
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.
AIML
Adarsh Institute Of Technology & Research Centre, Vita
AIML
Adarsh Institute Of Technology & Research Centre, Vita
AIML
Adarsh Institute Of Technology & Research Centre, Vita
AIML
Adarsh Institute Of Technology & Research Centre, Vita
Accurate demand forecasting is essential for efficient production planning and inventory management. Traditional forecasting methods often fail to handle dynamic market changes and large datasets. This study proposes an AI-powered predictive demand system that uses machine learning algorithms to analyze historical sales data and identify patterns for forecasting future demand. The model helps optimize production planning, reduce overproduction, and improve resource utilization. The results demonstrate that AI-based demand prediction can enhance forecasting accuracy and support data-driven decision-making in modern manufacturing systems
KEYWORDS
The AI-Powered Predictive Demand for Production system helps predict product demand using machine learning techniques. By analyzing historical sales data, seasonal trends, and market demand, the system provides accurate demand forecasts. This prediction helps businesses plan production more efficiently, reduce excess inventory, and avoid shortages. The system also provides a simple user interface that allows users to easily enter data and view prediction results.
· M. Atwani, M. Hlyal, & J. El Alami (2024) – AI‑based demand forecasting models: A systematic literature review — Comprehensive review of AI and hybrid demand forecasting models in manufacturing and supply chain contexts. · “Predictive Analytics in Supply Chain Management: The Role of AI and Machine Learning in Demand Forecasting” – Explains how AI/ML improves forecasting over traditional methods with examples of neural networks, SVMs, deep learning etc. · Review & analysis of AI methods for demand forecasting (Procedia CIRP, 2022) – A classification and overview of AI techniques used in supply chain demand forecasting
Publication Date
4/30/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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Conflict of Interest
The authors declare no conflict of interest related to this research work.
Funding Information
No funding information available for this research.
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