P.PSH.1581 - Optimising red meat supply chains using data and AI applications

Summary

This research aimed to address the question of how artificial intelligence (AI) and structured data optimisation can improve carcase allocation, production scheduling, and value recovery in beef processing operations. The work targeted a large-scale processor and supply chain stakeholder, where complex decision environments and carcase variability create measurable inefficiencies in yield performance and economic returns. The results are intended to inform future developments of an AI-enabled optimisation system and provide an evidence base for future research priorities across the red meat processing industry.

Objectives

The aims of the project were to:

•    Establish a validated baseline of current sorting and allocation performance for one subprimal.
•    Develop and test possible AI-driven allocation and optimisation algorithms under simulated commercial constraints.
•    Quantify hypothetical economic uplift and operational improvements relative to existing practices.
•    Define a pathway to further R&D, commercial commissioning and industry scale-up.

Key findings

The project established a quantitative performance baseline and demonstrated validated optimisation opportunities through future AI-enabled allocation modelling. Simulation results showed improved sub-batch consistency, enhanced compliance with customer specifications, and measurable potential for increased carcase value recovery. Findings confirmed that improved data integrity and algorithmic decision support materially enhance production efficiency and economic performance.

Benefits to industry

The project provides the foundations of a scalable optimisation R&D framework which may be capable of improving carcase utilisation, scheduling accuracy, and value capture across processing operations. Adoption of such systems could reduce value leakage per head, strengthen processor–producer alignment through improved feedback mechanisms, and enhance overall supply chain competitiveness within the Australian red meat sector.

MLA action

Continue to partner with red meat supply chains to implement systems such as these that can create and capture additional value.

Future research

The project uncovered a plethora of future applied R&D opportunities. This future work should prioritise further study into the three identified optimisation areas of yield prediction, dynamic batching, and optimised process scheduling, and aim to run full commercial testing and validation of the economic gains for each under live operating conditions. Expansion of optimisation coverage across additional primals is also advised to enable whole-of-carcase value optimisation. Continued investment in automated data capture and real-time analytics capability will also be critical to supporting industry-wide scalability and long-term impact.