ENHANCING DECISION-MAKING IN LIVESTOCK TRADE USING HIERARCHICAL MODELS FOR PRIORITIZING ECONOMIC STABILITY, LOGISTICS, AND CULTURAL FIT

Abstract

The globalization of agricultural markets presents both opportunities and challenges for Brazilian livestock producers seeking expansion. International Market Selection (IMS) requires balancing economic prospects, logistical efficiencies, and cultural compatibility while complying with diverse regulatory frameworks, such as those of the European Union and Islamic markets. This study proposes an innovative decision-making approach using the Analytic Hierarchy Process (AHP) to assist Brazilian livestock producers in evaluating potential international markets. A Composite Indicator for International Market Selection (????????????????????) was developed by aggregating key performance indicators (KPIs) across three critical dimensions: (1) economic stability & growth, (2) logistics, and (3) culture. The model was applied to five pre-selected markets—Germany, Belgium, France, the United Arab Emirates, and Turkey—using data spanning from 2020 to 2023. The results highlight that economic stability and GDP growth are primary determinants in market selection, with Turkey ranking highest due to its favorable economic indicators. France and Belgium also emerged as viable options, particularly given their cultural alignment with Brazil. Despite strong economic conditions, the United Arab Emirates ranked lowest due to logistical and cultural challenges. The study underscores the importance of considering sustainability, ethical practices, and regulatory compliance alongside economic factors in IMS. Additionally, it demonstrates how AHP provides a transparent, data-driven framework for prioritizing market entry strategies. The findings suggest that Brazilian livestock producers should adopt a diversified market approach, leveraging economic growth potential while mitigating risks through cultural and logistical considerations. Future research may explore integrating machine learning techniques to enhance IMS models and incorporate broader stakeholder perspectives for more comprehensive decision-making.