## 7 Conclusions  
This study aims to assess the logistics performance of 118 countries using a MCDM approach, specifically the Entropy-MCRAT model, based on six criteria defined by the World Bank for the years 2010-2023. According to the Entropy method criterion weight results, \"Infrastructure\" (C2) emerged as the most significant criterion across all years, while the least important criteria were \"International Shipments\" (C3) for the years 2010, 2014, and 2023, and C6 for the years 2012, 2016, and 2018. The results of the Entropy-MCRAT model indicate that, in 2010, Germany, the Netherlands, and Singapore ranked in the top three positions, while Cuba, Rwanda, and Guinea-Bissau occupied the bottom three. For 2012, Singapore, Hong Kong SAR, China, and Germany had the highest LPI scores, while Haiti, the Republic of Congo, and Djibouti ranked lowest. In 2014, Germany, the Netherlands, and Singapore were again among the top three, whereas Afghanistan, the Republic of Congo, and the Democratic Republic of Congo had the lowest LPI scores. In 2016, Germany, Sweden, and the Netherlands ranked highest, while Lao PDR, Haiti, and the Syrian Arab Republic had the lowest performance. By 2018, Germany, Japan, and Sweden were the top performers among 118 countries, while Haiti, Angola, and Afghanistan had the lowest LPI scores. In 2023, Singapore, Switzerland, and Finland had the highest logistics performance, while Cameroon, Afghanistan, and Libya ranked at the bottom. To validate the proposed methodology, a recently proposed innovative sensitivity analysis approach was applied. As part of this analysis, the impact of different weighting techniques on MCRAT results was assessed, with the Entropy-MCRAT scores treated as a fixed external factor. In this context, the influence of varying criterion weights on MCRAT results was tested across 105 scenarios. A similar analysis was conducted as part of the comparative analysis for the SAW, TOPSIS, and FUCA methods, yielding over 2.500 outputs. In this section, the Entropy-based SAW-LPI rankings, which share similar computational procedures, have been evaluated within the same category. The findings demonstrated that the method with the lowest standard deviation exhibited a high correlation with the fixed external factor. Based on this information, while the SAW/LPI ranking was identified as the least sensitive compared to other methods in 2010, FUCA outperformed all other rankings when considering all years. Conversely, MCRAT exhibited the worst performance across all years. This study's contributions to the literature are as follows: (i) The Entropy-MCRAT model has been applied to logistics performance assessment for the first time. (ii) It has been demonstrated that non-normalized results from the MCRAT, SAW, TOPSIS, and FUCA methods are as accurate as normalized results. (iii) An extensive comparative assessment of the logistics performance of various countries was conducted based on six years of data published by the World Bank. (iv) The proposed methodology was validated using an innovative sensitivity analysis approach. (v) Through an extensive sensitivity analysis,  
Figure 6: Mean standard deviations based on MCDM methods and LPI ranking. the impact of criterion weights on MCRAT results was examined across 105 scenarios. Additionally, the analyses were repeated for the SAW, TOPSIS, and FUCA methods in the comparative analysis. (vi) The findings are anticipated to assist policymakers and businesses, providing a useful resource for comprehending a country's standing in global competition. Moreover, this study is anticipated to provide a reference for researchers evaluating the logistics performance of countries. This study presents a methodology that enables countries to assess various logistics infrastructure projects, thereby aiding decision-makers in identifying the project that would most effectively improve overall logistics performance.