## 5 Future Developments
& [132] A study on the production of a metal component in the aerospace manufacturing sector is proposed. The research results highlight the usefulness of integrating LCA with tools that can capture and reveal the impact of the dynamic and stochastic behavior of production systems. & [133] Implementing a Lean and Green (L\&G) approach to assess the applicability of Lean Manufacturing (LM) tools and studying how Lean affects ecological performance in a real business case located in Brazil that manufactures PVC pipes and fittings. DES modeling is used to analyze L\&G scenarios and performance. The results of the study emphasize the importance of integration with a DES model to test different scenarios, visualize results considering different levels of Kanban, and study which situation to apply, based on reality, particularly the number of acceptable configurations. & [134] A simulation model based on data and agents within a digital DT framework. The proposed approach is applied to a real semiconductor manufacturing system. Research results show that DTs go beyond traditional simulation with the help of real-time synchronization through industrial IoT technologies. Specifically, simulation supports off-line experimentation and planning, while DTs offer synchronous execution and modification. improvement and environmental sustainability. Data collected in real time keep digital models aligned with operating conditions, improving the accuracy of simulations and enabling the development of strategies consistent with the current state of processes and the surrounding environment. This technology integration enables multidimensional optimization, balancing operational efficiency and ecological impact. Manufacturers can thus simultaneously pursue goals such as increasing productivity, reducing cycle times, and decreasing energy consumption and carbon emissions. The use of artificial intelligence, machine learning and simulation also enables the development of predictive models capable of anticipating critical issues and opportunities, enabling proactive interventions in resource management and strategic planning. Constantly updating simulation data supports continuous adaptation of business strategies, optimizing processes in response to changes in context. Simulating alternative scenarios prior to implementation enables assessment of the environmental and operational impact of different solutions, reducing risk and improving the quality of decisions. In addition to short-term operational benefits, the integrated adoption of discrete-event simulation and digital twin enables the pursuit of long-term sustainable benefits. These tools support more efficient and rational use of resources, contributing to the systematic reduction in waste, the design of more resilient production systems, and large-scale energy optimization. In particular, the digital twin enables continuous life-cycle monitoring of production assets, fostering predictive maintenance practices, waste reduction, and improved operating conditions. Looking forward, these approaches enable industrial strategies geared toward environmental sustainability and compliance with increasingly stringent green regulations, integrating economic goals with ecological ones and enhancing business competitiveness in the long term. However, in an increasingly automated and data-driven manufacturing ecosystem, the human role assumes central importance. The evolution toward intelligent production models requires a rethinking of skills: operators can no longer be limited to executive  
Figure 11: Benefits for decision-making in integrating KET 4.0 and other advanced technologies. tasks, but must be able to interact with digital systems, interpret data and contribute consciously to decision-making. In this context, reskilling and upskilling initiatives are key to transforming traditional skills into digital skills that can effectively dialog with automation and simulation.