MARKET ANALYTICS IN THE U.S. LIVESTOCK AND POULTRY INDUSTRY: USING BUSINESS INTELLIGENCE FOR STRATEGIC DECISION-MAKING

Abstract

This study addresses a practical problem in the U.S. livestock and poultry industry: managers face volatile input costs and market shocks, yet many business-intelligence (BI) programs remain reporting-centric rather than decision-centric. The purpose is to quantify how market signals and exogenous shocks map to prices and margins, and to test whether BI maturity operates as a protective capability. Using a quantitative cross-sectional, case-based design, we assemble a harmonized 12-month snapshot of cloud-enabled and enterprise cases spanning beef, pork, broiler, and turkey supply chains. Key variables include outcomes standardized price, a cost-adjusted margin proxy, and volume and drivers species-specific feed bundle, futures or basis, export intensity, weather anomaly, disease intensity plus BI maturity from multi-item 5-point Likert scales. The analysis plan proceeds from descriptives and correlation with false-discovery-rate control to OLS models with HC3 errors, and a moderation test of Feed × BI maturity with simple-slope and Johnson–Neyman probes. Drawing on a structured review of 120 peer-reviewed papers and practitioner sources, the models predict three headline findings: futures and basis are strong positive correlates of realized prices; feed exposure is the dominant adverse driver of cost-adjusted returns; and higher BI maturity is associated with higher margins and a statistically reliable reduction in sensitivity to feed shocks after rich controls. Managerial implications are immediate: treat BI as an exposure-management system by investing in data freshness, governed KPI coverage, and decision-process integration tied to playbooks for hedging, scheduling, and pricing. Conceptually, the results reposition BI as a slope-shifting capability that flattens the mapping from shocks to outcomes rather than only lifting mean performance.