(untitled) (Innovation & Development in Engineering and Applied Science, 2025)
The objective of this research is to model and analyze production processes in the food industry, specifically in the manufacture of dairy products, using multivariate statistical techniques. The multivariate techniques MANOVA and PERMANOVA compare groups with multiple dependent variables. Productivity indicators evaluate efficiency and performance, facilitating improvements and strategic decisions. Simulation optimizes processes, reducing costs and improving efficiency through accurate analysis and predictions. To this end, the unit operations of the fruit yogurt production process were identified and documented, developing a structured database with the processing times for each process. The integrity of the database was validated, imputing 3.3% of the missing data using the KNN method, and the absence of outliers was verified using Mahalanobis distance. The analysis of the execution times determined the most appropriate probability distributions for each process, with validation using the Chi-square goodness-of-fit test. The simulation process was validated using a Student’s t-test, comparing the actual process with the simulated one, which ensured its representativeness. Subsequently, improvement scenarios were simulated using FlexSim software and Lean Manufacturing techniques (TPM, 5S, Poka Yoke, and muda elimination), evaluating the impact on production times and the quantity of finished products. The results were analyzed using the PERMANOVA method, given that the assumptions of normality, homoscedasticity, and independence for MANOVA were not met. The analyses revealed statistically significant differences between the simulation scenarios, with the 5S scenario standing out as the most efficient in reducing production time and increasing the number of products. The model explained 44% of the variability in the results, highlighting the effectiveness of the 5S methodology in optimizing the process.