## 7 Discussion
### Limitations and Future Work
In environments without access to advanced computing resources, an effective strategy involves reducing model complexity by clustering SKUs into product families with similar operational characteristics, significantly decreasing the dimensionality of the setup time matrix. Another practical approach is the implementation of heuristic methods or predefined assignment rules for sequencing, which, although sacrificing some degree of optimality, can provide sufficiently good solutions with lower computational effort. Moreover, instead of executing the model continuously or daily, wineries may apply weekly planning cycles or utilize preconfigured scenarios based on typical operating conditions, substantially reducing the processing load. Predictive models based on historical data can also be explored to estimate processing times or hydraulic behavior without fully solving the optimization model, thereby enabling faster and more feasible decision making in resource-limited contexts. Additional practical implementation strategies suitable for small scale wineries include limiting the number of bottle formats to reduce line-adjustment complexity, grouping wine batches with similar hydraulic properties to minimize clogging effects, and adopting operational rules based on predefined changeover matrices, thereby decreasing the need to solve the full model daily. These approaches allow for the practical adaptation of the model to smaller-scale operations, maintaining its utility and contributing to enhanced production planning in the sector. Such strategies underscore the potential of integrating simplified heuristic methodologies and predictive models to support decision making in computationally constrained environments. Aligning model complexity with available infrastructure and incorporating data-driven predictive tools helps maintain high operational efficiency, ensuring wineries of varying scales can benefit from advanced optimization techniques. Future research could focus on integrating these alternatives into a unified decision-support framework, complemented by rigorous sensitivity analyses and validations based on historical data, to fine-tune model performance and further enhance production-planning processes in the wine industry. In addition to the simplifications proposed for small-scale operations, it is also necessary to address the computational complexity introduced by the binary decision variables associated with sequence-dependent, particularly in larger or more intricate production environments. To this end, we suggest exploring structural decomposition techniques that separate configuration-related decisions (such as equipment compatibility and format changes) from operational sequencing. This layered approach can reduce the dimensionality of the optimization space and facilitate more efficient solving routines. Moreover, the integration of heuristic and metaheuristic methods, including Genetic Algorithms, Tabu Search, or GRASP (Greedy Randomized Adaptive Search Procedure), could offer robust approximations in significantly shorter computation times. These techniques are especially suitable when the number of SKUs and transitions becomes large, enabling planners to preserve solution quality while improving scalability and responsiveness. Future research could focus on benchmarking these alternative approaches against the exact method, identifying trade-offs between optimality and practicality across different planning scenarios. Finally, the economic evaluation of the impact of a model such as the one proposed in this study represents a particularly relevant avenue for future research. Assessing the implications of the model in terms of tangible economic benefits would contribute significantly to its adoption in the industry, while also enhancing the efficiency and competitiveness of operations.