Layered Decision Environment for Agricultural Market Intelligence

Recent events such as natural hazards, diet trends, and the COVID-19 pandemic have shed light on several inefficiencies of the traditional fresh fruit and vegetable (FFV) supply chain (SC). Factors that contribute to this problem are the lack of coordination of the SC participants, the inaccessibility of planning tools for agricultural production, and the absence of market information to determine if a product will have a demand. Intelligent SCs are emerging to address some of these issues by using data-driven tools to aid in decision-making. Nonetheless, there has been little work to incorporate market intelligence in the new SC model to solve the lack of market information in the traditional model. It is essential to include market intelligence in the new SC model to decrease food waste, reduce losses related to low market prices and demands, and avoid scarcity events in which food availability and affordability decrease, while aiding small growers by alerting them of potential market opportunities. This work aims to develop a market intelligence framework for the FFV SC and incorporate it into the intelligent SC. A layered system approach is used with the goal of collecting relevant data to monitor and diagnose the market's state and provide recommendations to the SC participants. The layered system framework aims to decompose the overall problem into several layers with distinct goals such as data collection, processing, monitoring, diagnostics, among others. This work will focus on the monitoring aspect of the system.