Supply Chain Management
Brundha R1, Meenaumadevi M Guide2, K. Sugashini3
1Brundha R, Department of Information Technology Batchelor of Technology, Sri Shakthi Institute of Engineering and Technology (Autonomous) Coimbatore (Tamil Nadu), India.
2Meenaumadevi M Guide, Department of Information Technology Batchelor of Technology, Sri Shakthi Institute of Engineering and Technology (Autonomous) Coimbatore (Tamil Nadu), India.
3Mrs. K. Sugashini, Department of Information Technology Batchelor of Technology, Sri Shakthi Institute of Engineering and Technology (Autonomous) Coimbatore (Tamil Nadu), India.
Manuscript received on 15 November 2024 | First Revised Manuscript received on 22 November 2024 | Second Revised Manuscript received on 22 January 2025 | Manuscript Accepted on 15 February 2025 | Manuscript published on 28 February 2025 | PP: 15-18 | Volume-11 Issue-6, February 2025 | Retrieval Number: 100.1/ijmh.D176811041224 | DOI: 10.35940/ijmh.D1768.11060225
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: The Supply Chain Management (SCM) system is designed to enhance the efficiency, transparency, and resilience of supply chain operations. This project integrates advanced data analytics, machine learning, and real-time tracking to overcome the limitations of traditional SCM systems, such as demand volatility, inventory imbalances, and poor response to disruptions. By utilizing diverse data sources—including historical demand, market trends, and consumer insights. The system provides accurate demand forecasting and optimal inventory management. Key features include real-time visibility across the supply chain, predictive analytics for proactive decision-making, and a responsive structure that mitigates risks such as stockouts and overstocking. The system’s modular design supports scalability, enabling businesses to adapt quickly to market changes, streamline operations, and ultimately improve customer satisfaction and profitability
Keywords: Supply Chain Optimization, Inventory Management, Demand Forecasting, Logistics and Distribution, Real-time Tracking, Data Analytics, Machine Learning Predictive Analytics, Risk Mitigation, Supplier Management.
Scope of the Article: Management