Conference Agenda

In recent years, the textile sector has increasingly focused on adopting strategies centered around continuous improvement. Among the most relevant approaches are Lean Manufacturing and Six Sigma, which aim to enhance internal processes, eliminate unnecessary activities, and improve the overall quality of finished products. Despite their advantages, applying these methodologies involves certain difficulties, especially for small and medium enterprises, where limited technical capacity and resistance to change are common barriers to success. To explore this topic, a systematic review was carried out, guided by clearly defined inclusion and exclusion criteria. A total of sixty-two open-access scientific articles were examined, selected from widely recognized academic databases such as Scopus, Web of Science, ScienceDirect, and EBSCOhost. The analysis incorporated both qualitative and quantitative perspectives in order to understand the effects of implementing these methods in the textile industry. Results showed that combining Lean principles with the Six Sigma methodology helped reduce production defects by forty-four percent, increase productivity by twenty-seven percent, and enhance both delivery reliability and operational efficiency. These improvements were also associated with lower production costs. Furthermore, frequent challenges in the industry, such as inconsistent processes and poor inventory control, were addressed effectively, contributing to greater competitiveness and long-term sustainability. In summary, when organizations commit to overcoming cultural resistance and invest in proper training, the integration of Lean Manufacturing and Six Sigma becomes a valuable strategy for strengthening quality, increasing productivity, and supporting sustainable growth in the textile industry. This approach also enables better responsiveness to the evolving needs of global markets

This work evaluates the technical and economic feasibility of investing in robotic automation of industrial sewing operations using simulation as a decision-making support tool. A multi-robot system based on SCARA manipulators was modeled in an industrial simulation environment, representing key sewing operations such as tapestry and shoulder. The simulation scheme allowed for the analysis of robotic trajectories, process synchronization, and production flow stability through the use of intermediate buffers. From the simulated scenarios, production performance indicators such as cycle time and production rate were obtained, which were used to evaluate the operational behavior of the system under controlled conditions. Based on these results, a financial analysis was performed to estimate the economic viability of the proposed system, considering the investment costs and benefits associated with improved productivity. The results suggest that robotic automation of sewing operations can achieve stable production flows and competitive productivity levels, supporting favorable economic indicators such as return on investment and payback period. Overall, the proposed simulation-based approach contributes to reducing uncertainty in investment decisions prior to the physical implementation of the system.

This study presents a digital transformation initiative within a private Uruguayan K-12 institution, focusing on the modernization of core academic management systems and introducing innovative views to facilitate data-driven decision-making (DDDM), with direct participation of personnel as well as students preferences. While optimization research in education has extensively addressed logistical challenges—such as facility allocation and complex scheduling in higher education— there remains a significant gap regarding social group dynamics at the K-12 level. This research addresses that, by developing an optimization model for social dynamics and, crucially, integrating it directly into the school’s daily operational environment.