TY - GEN
T1 - EcoChain
T2 - 13th International Conference on Frontiers in Intelligent Computing: Theory and Applications, FICTA 2025
AU - Shreekumar, T.
AU - Ramakrishna, M.
AU - Sadhana, K.
AU - Meghashree, M. B.
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.
PY - 2026
Y1 - 2026
N2 - EcoChain is a top-to-bottom blockchain-powered supply chain management system relying on IoT sensors, edge computing, AI-enabled demand forecasting, smart contracts, and blockchain technology for solving modern-day supply chain issues. It tries to enhance visibility, support instantaneous decision-making, optimize use of resources, and automate requisite procedures. Blockchain provides a secure tamper-evident ledger of decentralized information that enables transactions and IoT sensors to monitor continuous monitoring of conditions on products including location and temperature. Edge computing processes the data in real-time locally to reduce latency and enables real-time anomaly detection. The system utilizes AI models such as ARIMA for accurate demand forecasting and synchronizes production with real-time demand to prevent wastage. It automates critical processes such as payment, restocking inventory, and shipment approvals through smart contracts, reducing human intervention. In a simulated environment, EcoChain showed robust demand forecasting accuracy (96–98%) and minimal decision latency (2.5–4 s per transaction) as against traditional supply chain systems. The end-to-end technology integration in EcoChain is more scalable, open, and efficient in operation.
AB - EcoChain is a top-to-bottom blockchain-powered supply chain management system relying on IoT sensors, edge computing, AI-enabled demand forecasting, smart contracts, and blockchain technology for solving modern-day supply chain issues. It tries to enhance visibility, support instantaneous decision-making, optimize use of resources, and automate requisite procedures. Blockchain provides a secure tamper-evident ledger of decentralized information that enables transactions and IoT sensors to monitor continuous monitoring of conditions on products including location and temperature. Edge computing processes the data in real-time locally to reduce latency and enables real-time anomaly detection. The system utilizes AI models such as ARIMA for accurate demand forecasting and synchronizes production with real-time demand to prevent wastage. It automates critical processes such as payment, restocking inventory, and shipment approvals through smart contracts, reducing human intervention. In a simulated environment, EcoChain showed robust demand forecasting accuracy (96–98%) and minimal decision latency (2.5–4 s per transaction) as against traditional supply chain systems. The end-to-end technology integration in EcoChain is more scalable, open, and efficient in operation.
UR - https://www.scopus.com/pages/publications/105029657501
UR - https://www.scopus.com/pages/publications/105029657501#tab=citedBy
U2 - 10.1007/978-3-032-12830-0_5
DO - 10.1007/978-3-032-12830-0_5
M3 - Conference contribution
AN - SCOPUS:105029657501
SN - 9783032128294
T3 - Smart Innovation, Systems and Technologies
SP - 51
EP - 68
BT - Innovations in Information and Decision Sciences - Proceedings of the 13th International Conference on Frontiers in Intelligent Computing
A2 - Bhateja, Vikrant
A2 - Dey, Maitreyee
A2 - Tang, Jinshan
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 6 June 2025 through 7 June 2025
ER -