# Machine Selection for Inventory Tracking with a Continuous Intuitionistic Fuzzy Approach
## 8 Conclusions
Although the A1 and A3 rankings are stable in both methods, the change in rankings between A2 and A4 points to a difference in how these options are assessed. This implies that the criteria or calculations driving the two methods might emphasize certain factors differently, resulting in discrepancies in the final rankings. _RQ 2: What are the most critical criteria influencing machine selection for inventory tracking within lean manufacturing systems?_  
The proposed framework offered a structured and robust decision-making approach for selecting the most suitable machine, considering multiple criteria and expert opinions under uncertainty. The CINFU AHP method was used to determine the weights of the six criteria: Speed (C1), Setup Time (C2), Ease to Operate and Move (C3), Ability to Handle Multiple Operations (C4), Maintenance and Energy Cost (C5), and Lifetime (C6). The normalized weights, representing the relative importance of each criterion in the machine selection decision, are Speed: 0.25, Setup Time: 0.08, Ease to Operate and Move: 0.13, Ability to Handle Multiple Operations: 0.23, Maintenance and Energy Cost: 0.18, Lifetime: 0.12. These weights highlight that Speed (C1) is the most crucial factor in selecting a machine for inventory tracking, followed by Ability to Handle Multiple Operations (C4) and Maintenance and Energy Cost (C5). Setup Time (C2) carries the least weight in the decision-making process. Table 5 gives some ideas about the criteria. The importance of comparing Speed to Lifetime emphasized that for this specific application, the immediate operational efficiency provided by a high-speed machine outweighs concerns about its long-term durability. This might reflect the company's manufacturing needs, technical choices, or a strategic principle on short-term gains. The IVIFN for Setup Time vs. Ability to Handle Multiple Operations indicates that quick setup times and multi-functionality are considered vital, potentially reflecting a necessity to balance fast manufacturing modifications with the ability to perform various jobs. The IVIFN for Ease to Operate and Move vs. Maintenance and Energy Cost suggests that minimizing operational expenses, represented by maintenance and energy costs, is a priority problem for the company, in particular, surpassing the benefits of user  
friendliness and portability. This might be because of funding limitations, sustainability goals, or a long-term cost optimization plan. _RQ 3: How does modifying the significance of criteria through sensitivity analysis affect the ranking of machine alternatives?_  
The sensitivity analysis examined the impact of varying the weight of each criterion individually on the final ranking of the alternatives. This analysis involved 81 different scenarios for each criterion, with the weight incrementally increasing from 0.1 to 0.9 while proportionally adjusting the weights of the remaining criteria. The ranking of A1 remained stable as the most preferred machine until the weight of Speed fell below 0.5. Beyond this threshold, the ranking fluctuated, with A2 eventually surpassing A1 as the most preferred choice when the weight of Speed increased. This finding highlights the significant influence of Speed on the decision and the varying sensitivity of different alternatives to this criterion. The sensitivity analysis results for the remaining criteria are presented in Appendix A (Figures A1-A5). These figures provide detailed insights into how changes in the weight of each criterion affect the relative rankings of the alternatives. In general, the robustness of the ranking for A1 and A3 maintained their relative positions across a wide range of weight variations for most criteria. There was potential for ranking fluctuations for A2 and A4, notably when the weights of specific criteria (like Speed) were significantly altered. It is essential to consider the relative sensitivity of different alternatives to specific criteria when interpreting the results and making the final selection decision.