# Machine Selection for Inventory Tracking with a Continuous Intuitionistic Fuzzy Approach
## 9 Discussion and Limitations  
The results of this study highlight how lean manufacturing principles and digital transformation goals converge in machine selection for inventory management. The High-Speed Automated Scanner achieved the highest score due to its superior performance in critical areas such as speed and operational efficiency. This corresponds with earlier studies highlighting operational efficiency and cost-effectiveness within decision-making frameworks [42]. In contrast, the Mobile Inventory Tracker received a lower ranking because of its high initial cost, even though it is cost-effective in the long run. A sensitivity analysis showed that the rankings remained consistent across different weight variations, confirming the robustness of the proposed framework. Compared to traditional methods, the integration of CINFU AHP and CODAS was more effective in handling uncertainties, making the decision-making process more reliable in real-world scenarios. These results underscore the capacity of the proposed framework to connect theoretical MCDM methods with the practical challenges of decision-making, especially in sectors aiming to meet lean manufacturing and digital transformation objectives. By answering these research questions, the study bridges the gap between theoretical multi-criteria decision-making methods and real-world industrial applications, contributing to both the academic field and practical decision-making frameworks. Future research could expand the scope by incorporating dynamic criteria weighting and applying the framework to broader contexts. However, this study has some limitations that should be considered. First, the evaluations were based on a panel of only three experts. While these experts were carefully selected, this small group may not represent the full diversity of perspectives. Second, the criteria and alternatives were specific to a particular context, which could limit the generalizability of the findings. Third, although mitigated through fuzzy logic, the subjectivity involved in the weights assigned by the experts may not fully capture the dynamic nature of organizational priorities. Finally, the lack of long-term validation through real-world implementation raises questions about the practical effectiveness of the selected machine in dynamic manufacturing environments. Moreover, the methodology presents certain drawbacks. The model presumes static weights for the criteria, which may not accurately represent the changing priorities found in real-world situations. Although AHP was utilized with a single-level hierarchy of criteria, more intricate multi-level hierarchies might more effectively capture subtle relationships. The dependence on expert opinions, even when analyzed through fuzzy logic, introduces subjectivity that could influence the reliability of the results. Lastly, the computational complexity of CINFU AHP and CINFU CODAS might create difficulties for practitioners unfamiliar with fuzzy decision-making methods, underscoring the necessity for more accessible software or tools. The variations in rankings are likely the result of a mix of how uncertainty is approached, the interpretation of criteria weights, and the mathematical principles underlying both methods. To grasp these distinctions, one must closely scrutinize each methodology's specific procedures and assumptions. CINFU AHP&CODAS takes into account uncertainty and vagueness more directly than conventional methods. This could lead to differences in the computation of criteria weights or alternative scores, particularly in scenarios that involve subjective judgments. AHP&CODAS may utilize precise or deterministic data, resulting in less flexible evaluations, while CINFU might modify rankings to consider ambiguous or contradictory information. The weight assigned to each criterion may be interpreted or combined differently in the two approaches. Minor weight adjustments can significantly impact rankings, especially for alternatives with similar scores.