Abstract

Forecasting Meat Quality to Optimise Production Planning in Commercial Livestock Processing

Efficient production planning in the red meat industry requires reliable forecasting of carcass quality traits prior to slaughter. Accurate early prediction of attributes such as carcass weight, marble score, and dentition is essential for aligning supply with customer demand, minimising waste, and maximising value capture across the supply chain. This study presents a data-driven forecasting framework, developed in collaboration with a large commercial meat processor in Australia, that leverages enterprise and external data sources to improve forward-looking production planning.

Methodology: The framework integrates a wide range of structured data, including supplier information, breed, sex, historical grading records, seasonality, weather conditions, disease indicators (e.g., liver fluke prevalence), feed inputs, and land use characteristics. We evaluate forecasting models across a continuum of approaches, from traditional statistical methods such as multinomial logistic regression to machine learning techniques including random forests, gradient boosting machines, and XGBoost. This comparative design allows us to benchmark performance across different levels of model complexity while ensuring consistency with the operational requirements of the meat processing industry. The framework employs a rolling seven-day forecast horizon, allowing predictions to be dynamically updated as new data becomes available. A key focus is the treatment of forecast uncertainty, where probability distributions and prediction intervals are used to communicate confidence in predicted carcass attributes.

Key findings: Initial results indicate that the proposed framework provides valuable early insight into the likely composition of incoming livestock batches. Forecasts enable processors to anticipate product eligibility, yield potential, and brand compliance prior to grading. By incorporating uncertainty into operational workflows, planners can optimise batch formation and allocate products more effectively, thereby reducing premature downgrading and improving utilisation of high-value product lines. The rolling forecast approach enhances responsiveness to both supply fluctuations and market demand.

Broader applications: While the immediate benefits are clear for the red meat industry—improved yield, reduced waste, and enhanced alignment with customer requirements—the methodology has wider relevance across agricultural and food processing sectors. Data-driven forecasting approaches of this kind can underpin more sustainable and efficient supply chains by enabling evidence-based planning and decision-making. The integration of uncertainty quantification into predictive modelling also offers a transferable framework for managing risk in other areas of digital agriculture.

This work contributes to ongoing efforts to harness statistical and machine learning tools for practical decision support in agriculture. By combining enterprise data, environmental signals, and advanced forecasting methods, the framework demonstrates how predictive analytics can translate into tangible operational improvements, ultimately supporting a more resilient and efficient agri-food system.

Keywords: Livestock processing, meat quality prediction, production planning, statistical modelling, machine learning, supply chain optimisation, uncertainty quantification