Artificial intelligence tools and data collection on the shop floor are enhancing flexibility and productivity in industry, addressing labor shortages and skills attrition by leveraging the tacit knowledge of workers. This study focuses on the cream cheese production sector, where operator expertise is essential for controlling the ultrafiltration concentration factor, a critical parameter affecting product moisture content. To ensure continuous and flexible production despite workforce challenges, a machine-learning tool was developed using the CRISP-DM approach to maximize cream cheese yield on a Canadian production line. A decision tree algorithm applied to real production and quality data yielded promising results, with an RMSE of 0.061 and an R \(^2\) of 0.91 when predicting the ultrafiltration concentration factor used by an experienced operator to maximize yield while complying with quality standards. The implementation saw positive operator acceptance due to comprehensive training and an inclusive approach. This research marks a pioneering effort to harness tacit knowledge in the dairy industry for machine parameter control, highlighting data acquisition and quality as key areas for further investigation to enhance tool performance and adaptability.