Ai Driven Smart Supply Chain Management System

• Author(s):

  D. Pravin Kumar | K.L. Sri Prasanna | S. Santhosh | P.S. Sarandeep

• Keywords:

• Abstract:

  Demand Forecasting Plays A Pivotal Role In Modern Supply Chain Management, Directly Influencing Inventory Planning, Logistics, And Overall Business Decision-making. Traditional Forecasting Systems Often Rely On Static Machine Learning Models That Gradually Lose Accuracy As Market Conditions Evolve, Leading To Inefficiencies And Poor Resource Utilization. To Address These Limitations, This Paper Proposes An AIOps-driven Demand Forecasting System Built On The MERN Stack. The System Integrates Machine Learning Models Such As LSTM And Prophet With An AIOps Monitoring Layer That Continuously Tracks Performance, Detects Data Drift, And Triggers Automatic Retraining When Necessary. By Combining Scalable Web Technologies With Adaptive AI, The Proposed Solution Delivers A Self- Improving, Real-time Forecasting Platform That Enhances Supply Chain Resilience, Reduces Stockouts, And Supports Intelligent Business Decisions.

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Other Details

• Paper id:

  IJSARTV12I4104857

• Published in:

  Volume: 12 Issue: 4 April 2026

• Publication Date:

  2026-04-04

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