The Red Meat Industry identifies data as "one of the industry's most powerful tools" and commits to a journey of digital transformation in data, artificial intelligence (AI) and digital tools leading to new opportunities for growth, efficiency and insight. This transformation will improve traceability, transparency, and decision-making across the supply chain. The red meat industry however, lacks the skills, expertise and human capacity to effectively drive industry wide transformation, now and into the future. There is currently no pipeline to create the workforce of the future so industry can realise the value of digital transformation.
Charles Sturt University has partnered with Meat & Livestock Australia (MLA) to deliver the NextGen Data Capability Program, a strategic initiative that brings graduate students into the fold to work on real-world data challenges. The program accelerates data strategy initiatives for the red meat industry while building the knowledge, skills, confidence and capabilities of the future workforce needed to solve emerging industry opportunities.
Integrity Systems Company, a subsidiary of MLA has access to an extensive and continually expanding dataset of customer insights consisting of numerous verbatim comments from a broad base of account users, sourced from a wide range of channels. This presents an exciting opportunity for a student to work with a substantial real world dataset and contribute to meaningful industry outcomes.
With rapid advancements in AI and natural language processing, there is significant potential to analyse sentiment, uncover key themes, and generate actionable insights from this customer feedback. A student undertaking this project would have the opportunity to apply modern analytical techniques, explore cutting edge AI tools, and help translate complex data into insights that inform product strategy, enhance user experience, and strengthen engagement across the livestock supply chain.
Overall, this project offers a unique opportunity for a student to gain hands on experience in data analysis, AI driven sentiment modelling, and industry-focused research, while contributing to initiatives that deliver tangible benefits across the red meat industry.
Data analysis / query: SQL, visualisation dashboard design and development, Data model design
Business process analysis: Translate the understanding of customer feedback process into data and insights solution
Data code development
AI (large language models) prompt engineering and API
The LPA program is the on-farm assurance program that underpins food safety, biosecurity and market access for Australian red meat.
A review of program data has highlighted an opportunity to better leverage the information collected through participant self assessments and supporting planning documents. These materials contain rich and structured data that, if analysed more systematically, could provide valuable insights to strengthen program oversight and support continuous improvement.
Enhancing the analytical approach would help build a clearer picture of broad compliance patterns across different production contexts, highlight areas where additional support or education may be beneficial, and improve visibility of developing trends within the broader operating environment. This would enable more informed decision making, ensure resources are directed effectively, and reinforce the long term integrity and resilience of the program.
Unlock the potential of Australia's livestock data to create new insights into animal performance and value outcomes. This project will bring together animal health, carcase feedback, pricing, and processing data to develop a national benchmark for understanding the factors associated with carcase value. By combining advanced analytics with emerging technologies such as imaging systems, the student will help build tools that support more informed decision-making across the supply chain and contribute to a more data-driven, transparent red meat industry.
How can data help produce high-quality beef more efficiently? This project explores the opportunity to combine genetics, eating quality, animal performance, health, and supply chain data to identify pathways for reducing days on feed while maintaining or improving product quality. The student will work with large-scale livestock datasets and predictive modelling techniques to develop insights that could improve productivity, sustainability, and profitability across the beef industry while supporting evidence-based management decisions.
Data scientist with machine learning experience including data preparation and cleansing, predictive model development and training.