## 6 Discussion  
In this paper, a combined dataset was created using LPI data published by the World Bank for the years 2010 to 2023, and the logistics performance of 118 countries was assessed utilizing a combined MCDM approach. The Entropy method was employed to determine the weights of the criteria. Infrastructure (C2) was recognized as the most crucial criterion for all years. This conclusion aligns with comparable findings in the literature, where [39] also identified \"Infrastructure\" as the key criterion. In addition, ref. [12] also determined \"Infrastructure\" to be the criterion with the highest significance. In [PERSON] et al. 's [41] study, which evaluated the logistics performance of 160 OECD countries, \"Infrastructure\" was again identified as the most important criterion. Similarly, ref. [50] determined \"Infrastructure\" to be the most important criterion, with criterion weights calculated using the Entropy method. Additionally, in the study conducted by [4], \"Infrastructure\" was also identified as the criterion with the highest importance. Furthermore, ref. [40] used 11 different MCDM techniques to assess the logistics performance of EU countries, and criterion weights were determined using genetic algorithms (GA). That study concluded that \"Infrastructure\" was the most important criterion. The performance scores of the alternatives were determined using four MCDM methods: MCRAT, SAW, TOPSIS, and FUCA. Combining MCDM methods that are based on different theoretical principles can result in a more robust and complete decision-making process than using a single method [61]. Although there are minor variations in the results obtained from different MCDM methods, the position of the top-ranked alternative remained unchanged, and the correlation coefficients between the method results were high on a yearly basis. Specifically, Germany was in the top position for the years 2010, 2014, 2016, and 2018, while Singapore maintained the top position in 2012 and 2023. The countries ranked in the top five and bottom five positions, based on the six-year average of the MCDM and LPI rankings, are presented in Table 7.  
Figure 5: Mean standard deviations based on MCDM methods. According to Table 7, the MCDM methods and LPI rankings are largely similar, though some minor differences exist. The most significant common result is that Germany ranks first across all rankings. This result is consistent with the findings of previous studies. For example, ref. [42] ranked Germany first in his analysis of the logistics performance of OECD countries in 2016, utilizing the improved TODIM method. In their study employing the MEREC-MARCOS model, ref. [37] identified that Germany possessed the highest LPI score. Using the PIV-SWARA-CRITIC model, ref. [39] also determined that Germany achieved the highest ranking. Furthermore, ref. [12] examined the logistics performance of OECD countries between 2010 and 2018 employing fuzzy AHP and ARAS-G methods, with Germany securing the top position. In addition, ref. [51] evaluated the logistics performance of EU and OECD countries for the period from 2010 to 2018, utilizing the COPRAS and COPRAS-G methods, and again ranked Germany as the leading country. Finally, ref. [52] measured the logistics performance of OECD countries using the SWARA-EDAS model, integrating results from the WB LPI from 2012 to 2018, and identified Germany as the highest-performing country. In the third stage of this study, applying the innovative sensitivity analysis approach as described in Section 5, the impact of the criterion weights created in 105 scenarios on the results of the MCDM methods was tested, considering the fixed external factor. The Entropy-based MCRAT-SAW-TOPSIS-FUCA rankings were used as the fixed factors for the MCRAT, SAW, TOPSIS, and FUCA methods, respectively, and standard deviation values were calculated using the correlations among the rankings. In this section, World Bank LPI scores are included in these calculations.