Optimized Warehouse Management System Leveraging Industry 4.0 Technologies

This research presents an integrated Warehouse Intelligence Framework that brings together four AI modules - dynamic route optimization with Traveling Salesperson and A* pathfinding algorithms; fire detection with YOLO, spread prediction with the project framedanger data, and shelf proximity; predictive analytics with ARIMA/Prophet/LSTM and Gradient Boosting models for stock anomalies and worker performance classification; and Best-Fit bin-packing with 3D space visualization - on a unified MERN + Flask platform. The overall system demonstrated 25% less total picking distance; 91% mAP in fire detection; under 8% MAPE in stock forecasting; 92% accuracy in worker classification; and an 18% increase in cubic-meter utilization, all in real time (20+ FPS, sub-second rerouting, 99% uptime) using only existing CCTV infrastructure. This modular, costconscious approach which breaks down silos between efficiency, safety, prediction and space utilization allows warehouses to confidently enter the adaptive Industry 4.0 space without retrofitting or installing proprietary hardware.

• Publication year

  2025

• Venue

  International research journal of innovations in engineering and technology

• Publication date

  Unknown publication date

• Fields of study

  Not labeled

• Identifiers
  DOI 10.47001/irjiet/2025.910024
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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