# Machine Selection for Inventory Tracking with a Continuous Intuitionistic Fuzzy Approach
## 8 Conclusions  
This research enhances the existing literature by tailoring the CINFU AHP and CINFU CODAS methods to a particular industrial challenge: selecting machines for inventory management. Unlike previous studies, this research's criteria and decision model prioritize lean manufacturing and digital transformation. Including a thorough sensitivity analysis further sets this work apart, creating a valuable tool for decision-makers. This research connects theoretical models with practical applications by testing the framework with a real-world case study, providing a replicable method for tackling similar issues in other manufacturing environments. The study sought to answer the following research questions: \begin{table}
\begin{tabular}{l l l l l l l l} \hline \hline  & & **C1** & **C2** & **C3** & **C4** & **C5** & **C6** \\ \hline \multirow{3}{*}{C1} & E1 & 5 & 6 & 7 & 6 & 6 & 7 \\  & E2 & 5 & 6 & 7 & 6 & 6 & 8 \\  & E3 & 5 & 6 & 6 & 7 & 5 & 6 \\ \hline \multirow{3}{*}{C2} & E1 & 4 & 5 & 5 & 7 & 4 & 6 \\  & E2 & 4 & 5 & 5 & 6 & 4 & 6 \\  & E3 & 4 & 5 & 6 & 7 & 7 & 6 \\ \hline \multirow{3}{*}{C3} & E1 & 3 & 5 & 5 & 5 & 6 & 5 \\  & E2 & 3 & 5 & 5 & 6 & 6 & 5 \\  & E3 & 4 & 4 & 5 & 6 & 7 & 6 \\ \hline \multirow{3}{*}{C4} & E1 & 4 & 3 & 5 & 5 & 7 & 4 \\  & E2 & 4 & 6 & 4 & 5 & 7 & 4 \\  & E3 & 3 & 3 & 4 & 5 & 7 & 4 \\ \hline \multirow{3}{*}{C5} & E1 & 4 & 6 & 6 & 7 & 5 & 6 \\  & E2 & 4 & 6 & 6 & 7 & 5 & 6 \\  & E3 & 5 & 3 & 7 & 7 & 5 & 7 \\ \hline \multirow{3}{*}{C6} & E1 & 3 & 4 & 5 & 6 & 4 & 5 \\  & E2 & 8 & 4 & 5 & 6 & 4 & 5 \\ \cline{1-1}  & E3 & 4 & 4 & 4 & 6 & 3 & 5 \\ \hline \hline \end{tabular}
\end{table} 
Table 17: Linguistic scale to weight criteria with crisp scale. \begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Criterion** & **Weight** & **Alternative** & **Score** & **Rank** \\ \hline C1 & 0.24 & A1 & 0.428 & 1 \\ C2 & 0.15 & A2 & 0.153 & 3 \\ C3 & 0.15 & A3 & 0.091 & 4 \\ C4 & 0.09 & A4 & 0.328 & 2 \\ C5 & 0.26 & & & \\ C6 & 0.11 & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 18: Criteria weights and alternative scores. _RQ 1: In what ways can the integration of CINFU AHP and CODAS methods enhance decision-making under conditions of uncertainty and ambiguity compared to traditional methods?_  
The results demonstrate that the proposed framework effectively integrates the strengths of CINFU AHP and CINFU CODAS to handle uncertainty and subjectivity in decision-making. The CINFU AHP method determined the weights of six criteria, with Speed (C1) receiving the highest importance (0.25) due to its critical role in lean manufacturing. The CINFU CODAS method ranked four machine alternatives, with the High-Speed Automated Scanner (A1) consistently achieving the highest score, regardless of variations in expert optimism levels (\(\zeta\)). Compared to traditional decision-making methods, the proposed framework proved more robust and reliable, effectively addressing uncertainty in expert evaluations and ensuring consistent rankings across diverse scenarios. The CINFU CODAS method ranked four alternative machines (High-Speed Automated Scanner (A1), Multi-Functional Robotic Arm (A2), Mobile Inventory Tracker (A3), and Cost-Efficient Fixed Inventory Counter (A4)) based on their performance across the six weighted criteria. The analysis considered various \(\zeta\) values (0.1 to 0.9) representing the expert's optimism level. Despite the varying \(\zeta\) values, the ranking of the alternatives remained consistent across all scenarios. A1 was ranked as the most preferred machine for inventory tracking, consistently achieving the highest assessment score. A3 was identified as the least preferred machine, consistently receiving the lowest assessment score. A2 and A4 were ranked between A1 and A3, with A2 generally outperforming A4 due to its higher score. Regardless of the expert's optimism level, this consistent ranking indicates the results' robustness and reinforces the selection process's reliability.