## 5 Final Remarks  
The purpose of the study was to propose a quantitative modeling method, employing [PERSON]'s entropy model as a proxy to assess the complexity in SCs. This study's object was four SCs in the agrifood and metalworking industries of Southern Brazil. The study's findings suggest that companies that prioritize cost and quality tend to have lower SCM complexity than those that prioritize flexibility and dependability. The study observed significant differences in the average entropies of specific paths related to engineered products and deliveries. These observations indicate that organizational complexities vary based on competitive priorities. The findings have implications for industrial practitioners. Companies that prioritize cost and quality may benefit from lower complexity, which leads to streamlined processes. In contrast, those who prioritize flexibility and dependability may need more complex organizational structures to meet dynamic market demands. In summary, the study provided valuable insights for industrial practitioners, including how to align competitive priorities with the inherent complexity of SCs. The study's findings suggest that prioritizing cost and quality can lead to lower SCM complexity, while flexibility and dependability may lead to higher organizational complexities. Looking ahead, future research can delve deeper into testing the hypothesis that companies prioritizing cost and quality exhibit lower SCM complexity. The study also suggests exploring the broader applicability of information entropy in assessing and controlling complexity across diverse industries, other types of systems, and global SCs. This exploration would contribute to the evolving field of SCM. In terms of external validity, the current methodology demonstrates both generalizability and extensibility across various production system typologies. The core principle--quantifying informational flow entropy as an indicator of complexity--remains universally applicable, grounded in Shannon entropy's theoretical framework for assessing informational complexity. When extending the methodology to other systems, several considerations should be included. (i) Information granularity and diversity: Future models could integrate a wider and more varied array of informational elements, such as sustainability metrics, regulatory compliance parameters, or cybersecurity data. Additionally, incorporating intermediate states (information partially required) could refine entropy measurement and address further nuances of uncertainty. (ii) Multitier interactions: For broader applicability, the mapping scope should include interactions beyond the focal company to encompass second- and third-tier agents. (iii) Large-scale data acquisition: While interview-based methodologies prove effective for detailed case studies, achieving broader generalizability requires collecting transactional data from enterprise resource planning (ERP) and SCM systems. Automated data collection may provide probability calculations with greater statistical significance. Future research should also include the meta-strategy of the SC. One possibility is to lead four similar surveys, each one focusing on a different meta-strategy to gather enough data to compare outcomes. Another possibility is the use of multicriteria methods to calculate weights in the case of multiple strategies and multiple priorities. At least one study [30] uses confidence intervals to address the inherent uncertainty observed in multicriteria studies focused on investigating the strategic aspects of and preferences in SCs. One important topic that the study should have addressed, which could be the focus of future research, is the relationship between complexity and performance in SCs. Among other approaches, performance can be measured using the framework provided by the SCOR (Supply Chain Operations Reference) model, as demonstrated in [56].