An ICT-Integrated Data-Driven Framework for Optimizing the Tobacco Supply Chain Using Blockchain and Cloud Computing

INTRODUCTION: Tobacco supply chain is a complicated multi-level structure that presents cultivation, processing, packaging, logistics, and distribution, which are often limited by inefficiencies, absence of transparency and data integration. OBJECTIVES: To address them, this paper introduces an ICT-Integrated Data-Driven Optimization Framework (IDDOF) that would combine blockchain technology, cloud computing, and machine learning to design an intelligent and transparent supply chain ecosystem. The framework also uses ICT and IoT-driven sensors to collect real-time information on the quality of tobacco leaves, the environment, and the performance of logistics, which are safely transferred to a cloud-based system that allows the accommodation of a large amount of data and its rapid processing. METHODS: Smart contracts based on the Ethereum platform are blockchain-based, which provide secure, tamper-resistant, and transparent data and transactions that encourage trust and accountability among stakeholders. The performance of the proposed framework is assessed using key ICT metrics—latency, throughput, scalability, security index, and energy efficiency. RESULTS: Random Forest enables precise predictive analytics for demand forecasting, quality assessment, and anomaly detection with low latency and optimal energy use, while Particle Swarm Optimization (PSO) enhances logistics scheduling, routing, and resource allocation to improve throughput and scalability. CONCLUSION: The integration of ICT, blockchain, and cloud computing ensures high security, interoperability, and operational efficiency across the tobacco supply network. Moreover, the modular and scalable design of the proposed framework makes it generalizable and adaptable to other agro-industrial supply chains, enabling similar gains in transparency, sustainability, and operational intelligence across diverse agricultural sectors.