<![CDATA[This study provides the development and implementation of a food supply chain analytics-based Business Intelligence (BI) dashboard for assisting the Indonesian national nutrition programmes, specifically in the monitoring of rice production. Rice is the number one staple food and a key part of nutrition strategies implemented through mass interventions like the National Nutrition Fulfillment Program, therefore, guaranteeing data-driven decision making all along the rice value chain is crucial. The data for this research are secondary data covering rice production, harvested area, rice productivity 2020 – 2024 processed and visualised with the software Tableau Public. The proposed dashboard is a combination of various analytical views: such as temporal trend analysis (line graph), spatial distribution mapping (geographical visualization), comparative regional performance (bar chart) and proportional productivity assessment (donut chart). These visualisations allows stakeholders to discover production patterns and regional inequalities in an interactive and concise format, and possible supply risks. The findings show the efficiency of the BI dashboard in visualizing large amounts of agricultural data into actionable information, which can be used to guide and shape agriculture strategies and policy formulation. The system successfully identifies the major provincial differences in harvested area as well as the differences in production areas and over time, and is important for adopting food production to meet the operational requirements of the nutrition service unit. This research adds to the extensive research work about the parallel between Business Intelligence and public policies and its applications to data-driven agriculture and food supply chain analytics. The study highlights the opportunities of BI-based dashboards to improve transparency, efficiency and responsiveness in national level food systems to foster nutrition program sustainability and scale. This approach can be further enhanced by incorporating real-time data sources and predictive analytics in future studies to further improve the decision support process.]]>
