Smart Supply Chain Finance Using Blockchain and IoT with Predictive Analytics and Real-Time Monitoring

Authors

  • G. Chandra Sekhar

Keywords:

Supply Chain Finance; Blockchain; Internet of Things; Smart Contracts; Predictive Analytics; LSTM; Real-Time Inventory Tracking; Decision Support Systems

Abstract

Supply chain finance (SCF) plays a key role in providing liquidity, trust and resilience of the multi-entity supply chains. However, current SCF systems have limited real time inventory monitoring, centralised trust management, delay in transaction settlement and excessive risk for fraud. This paper proposes a smart supply chain finance system based on the Blockchain -IoT-driven supply chain system by adding real time inventory tracking, secure decentralized transaction and predictive analytics, to address these issues. IoT sensors monitor the inventory level and environmental factors and blockchain technologies maintain transparent, immutable and tamper-resistant financial transactions in the form of smart contracts. A predictive analytics module is built using a Long Short-Term Memory (LSTM) for predicting the demand of inventory and the level of financial risk for proactive decision-making. A user-friendly dashboard integrates real-time and predictive information for automated and data-driven financial decision-making. The experimental evaluation demonstrates the developed approach has 95% accuracy when inventory, transaction time and cost are reduced, fraud is prevented, and the accuracy of the forecast demand is improved. This research confirms the concept that IoT and predictive analytics could provide a scalable, secure and smart approach to future supply chain finance systems.

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Published

22.06.2024

How to Cite

G. Chandra Sekhar. (2024). Smart Supply Chain Finance Using Blockchain and IoT with Predictive Analytics and Real-Time Monitoring. International Journal of Intelligent Systems and Applications in Engineering, 12(4), 6036–6043. Retrieved from https://www.ijisae.org/index.php/IJISAE/article/view/8210

Issue

Vol. 12 No. 4 (2024)

Section

Research Article

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