Business Intelligence and Predictive Analytics for AgribusinessSupply Chain Optimization: A Data-Driven Approach toSustainable Agricultural Management
Keywords:
Business Intelligence, Predictive Analytics, Agribusiness Management, Supply Chain Optimization, Digital Agriculture, Data Analytics, Sustainable Agricultural Development.Abstract
The rapid changes due to Digital Transformation in agriculture have opened up new possibilities to improve the performance of supply chain, decisions making and sustainability of agrifood industry using Business Intelligence (BI) and Predictive Analytics. Agrifood supply chains encounter a number of difficulties, such as, price fluctuations, uncertainty related to demand for products; loss after harvest; lack of resources; poor use of resources; and, absence of market knowledge. This research will explore how Business Intelligence (BI) and Predictive Analytics may be used as data-based tool for the improvement of agrifood supply chain operations, and promote sustainable agricultural management. Data from different sources, e.g., farm level data, market information, weather forecast data, satellite/remote sensing data, and enterprise database are integrated to facilitate the decision making at both strategic and tactical levels. The Business Intelligence (BI) systems that utilize frameworks, dashboards, and reporting capabilities will be assessed for providing timely insight into the processes involved with procurement, production, inventory management, logistics, and distribution. In addition, various predictive analytics methods that include machine learning, data mining, and forecasting models will be reviewed for predicting demand, estimating yields, assessing risks, and optimizing supply chains. The results show that the business systems based on data analysis of agrifood industries can lead to a significant increase in productivity, decrease in operating expenses, an increased transparency, minimized wastage, and improved resiliency of the entire food supply chain. Moreover, it is shown that the application of advance analytical techniques can support sustainable agricultural practices by facilitating an effective utilization of available resources and supporting rational decision-making. Finally, this research demonstrates the potential of Business Intelligence (BI) and Predictive Analytics to create intelligent, resilient and sustainable ecosystem of agrifood industry for further development.
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