Authors - Palash Sontakke, Pankaj Chandre, Prashant Dhotre, Ganesh Pathak Abstract - The dyeing segment of textile manufacturing faces pressing demands to curb water, energy and chemical consumption while maintaining colour con-sistency and fast throughput. This paper presents a layered edge‑to‑cloud archi-tecture that employs artificial‑intelligence techniques to manage resources dy-namically across the entire dyeing supply chain. IoT sensors on each dyeing machine stream high‑frequency telemetry to an AI cloud where data are curated in a scalable lake and enriched through feature engineering. Deep‑learning models forecast short‑term demand, yields and process drifts, enabling proac-tive recipe adjustments. A digital‑twin simulation engine mirrors every dye bath and tank, permitting insilico experimentation of eco‑friendly formulas without risking live production. Reinforcement‑learning agents, trained against the twin, generate near‑optimal job schedules and set‑points that minimise water and chemical footprints while respecting delivery constraints. All recommendations pass through an explainable‑AI module that surfaces the key factors driving each decision to foster operator trust and regulatory compliance. A blockchain ledger records batch‑level resource transactions, ensuring tamper‑proof tracea-bility for audits and sustainability certifications. The architecture integrates seamlessly with existing ERP/MES systems, closing the loop between predic-tive intelligence and shop‑floor actuation. Proof‑of‑concept results from a mid‑scale dye house show up to 18 % water savings and 12 % energy reduction, with payback achieved in under nine months. Collectively, the proposed framework demonstrates how advanced AI and Industry 4.0 technologies can deliver both economic and environmental gains in dyeing operations.
