Improvement of feedlot operations through statistical learning and business analytics tools

The purpose of this work is to incentivize the implementation of modern statistical learning tools in cattle management operations, especially within low-to-mid scale operations that traditionally rely on the expertise of its workers and have limited cattle and process information. Access to 'off-the-shelf' statistical learning tools, requiring minimal user-interaction, not only enhances prediction accuracy but helps automate operational decisions. This results in higher process efficiencies and improved standardization practices, while also helping identify profit opportunities. Finally, integrating these components into a single operating framework allows the tool to adapt to changes in data characteristics, which is especially important within non-standardized processes. We show the application of this tool through a case study implementation on a mid-scale operation in the northwestern state of Sonora, Mexico. From our case-study results, it was found that the modeling tool can satisfactorily predict growth patterns based on a low-dimensional set. It also can also capture historic decision-making when segmenting cattle into homogenous groups during their feeding process. Furthermore, it can help identify profit opportunities when estimating optimal cattle system times under varying market conditions.