Transforming the Global Aquaculture Supply Chain through the Integration of Artificial Intelligence and Big Data for Overcome Asymmetry Information - Hernalom Sitorus; Zaenal Arifin Hasibuan; Bobi Kurniawan; Sri Supatmi (Big Data Analytics and Data Science, 2026)
The global aquaculture sector faces structural challenges in the form of information asymmetry that causes a misalignment between production and market demand. The still-dominant production-driven paradigm leads to supply chain inefficiencies, low transparency, and limited traceability. This research aims to develop an information system integration model based on Artificial Intelligence (AI) and Big Data to transform the supply chain into a market-driven one. The research uses the Design Science Research (DSR) method, which includes needs analysis, data integration architecture design, development of Machine Learning and Deep Learning-based predictive models, and evaluation through prototype implementation. Expected outcomes include a data integration architecture, a supply-demand prediction model, and an AI-based traceability framework. This research contributes to improving the efficiency, transparency, and global competitiveness of the aquaculture sector.