## 5 Discussion and Conclusions
To address these issues, ERP systems can be tightly coupled with APS solutions in order to provide planners with robust decision support in order to significantly reduce workflow complexity, limit dependence on manual tools like spreadsheets, and enhance coordination across organizational units. By demonstrating how AI-enhanced metaheuristics can improve scheduling responsiveness through a more flexible response to dynamic changes, reduce manual overhead, and support energy-efficient operations, the study provides actionable insights for decision-makers. For instance, fashion manufacturers can use these techniques to dynamically group products, reduce transition losses, and better align production with customer demand--even in short-run or custom-order scenarios. Moreover, the study emphasizes that AI-based scheduling tools are not only technically effective but also operationally viable: they require minimal manual configuration, are scalable across different production sizes, and can evolve over time through adaptive learning. Companies reading this study can thus gain both a strategic and operational understanding of how to transition toward intelligent scheduling systems that support both efficiency and sustainability objectives. The integration of AI and metaheuristics into APS systems yields both operational and strategic benefits. These include enhanced flexibility to respond to disruptions (e.g., machine breakdowns or urgent orders), real-time rescheduling capabilities, and the ability to group similar products to minimize transition costs. Such capabilities are particularly relevant in sectors like fashion manufacturing, characterized by volatile demand, short product lifecycles, and complex, multi-stakeholder supply chains. In these environments, traditional ERP and scheduling tools often fall short of aligning production with customer requirements. AI-enhanced scheduling systems offer adaptive and scalable alternatives that support energy-efficient operations and improve scheduling accuracy without requiring costly infrastructure changes. In conclusion, this research agenda aims to extend these findings by achieving the following:  
* Bridging the gap between technological innovation and real-world decision-making, ensuring that AI tools are designed as effective decision-support systems that empower human planners rather than replace them;
* Embedding human-centric values into production scheduling, consistent with I5.0's emphasis on collaboration, fairness, and employee well-being, ensuring ethical and inclusive automation;
* Advancing the ethical, sustainable, and inclusive vision of I5.0 by promoting transparency, accountability, and environmental responsibility in AI-driven production systems;
* Providing sector-specific solutions for complex, multi-actor ecosystems such as fashion, addressing the unique challenges of fast-changing markets, short product lifecycles, and diverse supply chain participants. Therefore, this research bridges a critical gap between theory and practice in the field of intelligent production scheduling by aligning the related strategies with both operational objectives and emerging ethical expectations. It contributes to the academic dialogue by consolidating knowledge and advancing hybrid AI/metaheuristic frameworks tailored to industry needs while also offering clear, applicable insights for manufacturers navigating the challenges of I5.0. Moreover, it provides actionable insights for manufacturers seeking more adaptive, efficient, and ethically grounded production planning solutions. While the study presents a comprehensive literature-based framework, it lacks empirical validation through real-world industrial applications. As such, the practical effectiveness and generalizability of the proposed approaches remain to be tested across diverse manufacturing environments. In addition, further work should also explore how emerging AI paradigms, such as generative AI, could be integrated into APS systems to unlock new capabilities in dynamic, high-variability production contexts.