## 4 Results
### Case Study 3: 25% Increase  
If there is a 25% increase, the impact generated is 125% and is, therefore, at level 5. Whenever a process is at level 5, the user can see that it has exceeded the legal limit in some parameter. It is, therefore, advisable to analyze the production process to see where improvements can be made to reduce the environmental impact. Figure 9 shows the data used for this case, as well as the results obtained. The study's findings consider the practical implications for organizations seeking to implement decision support systems, aiming to restrain adverse environmental impacts within the textile and clothing industry. A comprehensive strategy that encompasses raw material analysis, facility and equipment updates, cost considerations, human resources training, marketing and market approach changes, and digitalization is recommended to guide organizations toward sustainable and environmentally responsible practices. Figure 8: Case Study 2: (a) data; (b) results. Figure 7: Case Study 1: (a) data; (b) results. In terms of raw material analysis, organizations should adopt a rigorous, scientifically grounded approach. Decision support systems can play a crucial role in identifying environmentally friendly alternatives through methodologies like lifecycle assessments. These assessments, incorporating parameters such as energy consumption, emissions, and resource use, provide a holistic understanding of the ecological footprint associated with each material. Raw material assessment is required to apply ecolabels. This will require tighter quality analysis. Facility and equipment updates require a systematic approach guided by decision support systems. Scientifically validated assessments of existing infrastructure, including energy efficiency, waste generation, and pollutant emissions, should inform strategic modifications. Employing advanced technologies for facility monitoring, including IoT sensors and digitalization, ensures real-time data acquisition and performance optimization. Updating facilities and equipment to comply with eco-friendly production is costly and long, as it is an action point that can take some time to implement. Human resources training is a critical component that should be conducted through scientifically curated programs embedded within decision support systems. These programs should focus on ecological literacy, educating employees on understanding and implementing sustainable practices. The emphasis is on fostering a culture of environmental responsibility through continuous education and scientific awareness. Market strategies should be scientifically informed and implemented. Analyzing market trends and consumer preferences with digital tools allows organizations to strategically position and communicate their environmentally conscious practices. Scientifically validated marketing campaigns, emphasizing the transparent communication of eco-friendly attributes, can positively influence consumer behavior. The integration of digitalization into companies and, consequently, into decision-support systems requires a strategic approach. Organizations should invest in cutting-edge technologies such as IoT, big data analytics, and blockchains. Scientifically driven digital solutions can provide real-time insights into environmental performance metrics, ensuring data accuracy and reliability. Incorporating machine learning algorithms can optimize processes, further reducing environmental impacts. In summary, adopting decision support systems in the textile and clothing industry requires a multidimensional and scientifically guided approach. Organizations should follow and structure the order of procedures for raw material analysis, facility and equipment changes, human resources training, and marketing and market approach changes--each one considered with cost and human resources impacts in a strategic and scientifically informed manner--to achieve meaningful reductions in adverse environmental impacts while maintaining economic viability.