Two key elements for improving supply chain performance are total productive maintenance (TPM) and overall equipment effectiveness (OEE). This paper proposes a TPM strategy for managing and autonomously maintaining four die-cutting machines for cardboard boxes to enhance production flow. Using the DMAIC cycle (define, measure, analyse, improve, control), maintenance activities were classified and efficiency measured through OEE and sigma metrics. After the first improvement cycle, the OEE and sigma metrics increased by an average of 21%, boosting productivity, quality, and efficiency. The study concludes that strategies based on preventive, predictive, and corrective maintenance significantly enhance supply chain performance in a Lean Six Sigma framework, and could be applied to other processes.

In a process that manufactures polyurethane soles by casting, a number of problems lead to different types of defects on the sole, causing significant economic losses for the company. In order to improve the product quality and decrease the number of defects, this study conducts an experimental design in the context of robust design. Since the response variable is binary, the statistical analysis was performed using generalised linear models. The operational methodology reduced the percentage of defects, while combining the experimental technique and control systems to achieve superior quality and a consequent reduction in costs

Within Taguchi methods, robust parameter design is a widely used tool for quality improvement in processes and products. The loss function is another quality improvement technique with a focus on cost reduction. Traditional Taguchi methods focus on process improvement or optimization with a unique quality characteristic. Analytical approaches for optimizing processes with multiple quality characteristics have been presented in the literature. In this investigation, a case of analysis for two quality characteristics in rubber for shoe sole is presented. A methodology supported in robust parameter design in combined array is used in order to obtain optimal levels in the vulcanization process. Optimization techniques based on the loss function and the use of restricted nonlinear optimization with genetic algorithms are proposed.

This research aims to analyze and optimize the antifungal property of a composite material made of polyurethane (PUR) and silver nanoparticles (AgNPs) against the fungus Trichophyton rubrum to find the optimal parameters that allow the highest inhibition of the fungal growth to be used in healthcare aimed at the population vulnerable to fungal infections, such as people suffering from diabetes mellitus, thus creating an opportunity for the generation of antifungal products for healthcare applications. This study analyzes the effect of three variables on the antifungal properties of the composite material: nanoparticle size, concentration, and the application of an ultrasonic treatment as a method to aid the dispersion of the AgNPs into PUR matrix. The proposed methodology includes tests in accordance with a standar to evaluate the growth inhibition of the fungi on a culture medium. A categorical logistic regression model was adjusted from 2³ factorial design with five replicates, which was optimized by the use of multi-objective genetic algorithms. The experimental factors showed a significant effect on the growth inhibition of the fungus, and the optimal levels were determined.