## 1 Introduction
However, using a method with high sensitivity implies robustness, reliability, and stability problems. This may cause doubts about the obtained ranking results and especially the best alternative. In addition, the aggregation method based on equal weight coefficients used by the World Bank in the LPI measurement is problematic. Because the aggregation methods for the MCDM methodology are highly compensatory, a country that scores high in one or more criteria will compensate for possible low scores in other criteria. In other words, the criteria used are not independent. In this context, many alternative methods with low compensatory power in the MCDM family can be used. MCDM methods can be classified as compensatory, partially compensatory, and non-compensatory. It can be argued that MCDM methods based on the utility theory are more compensatory than methods based on the 'outranking' school [15]. Because the compensatory effect means that, for example, a low value in one criterion in a decision matrix can be compensated by a high value in another criterion, it is certain that this is unfair and, at minimum, evokes negativity [16]. The calculation used for classical LPI performance rankings is a simple weighted aggregation method and has compensability. For example, a country that is weak in 'Logistics Competence and Quality' and 'Infrastructure' will compensate for its weakness by focusing on criteria such as 'Timeliness' and 'Tracking and Tracing'. Therefore, a country that scores high in one or two criteria can get ahead of its competitors by obtaining a higher score overall. In this sense, \"superiority methods\" or \"distance-based methods\" may have a softer compensatory effect compared to classical additive methods. As is well known, sensitivity analyses in MCDM methods are generally used to examine the effect of weights on the results. However, in cases where a single dataset or a small number of weight coefficients are used, the results may differ and even lead to erroneous results. So far in the literature, such inconsistencies may not be noticed in analyses performed with small datasets and a small number of weight coefficients. Our findings show that consistency increases when more weighting coefficients and larger datasets are used. This raises the question: Could previous studies in the literature that used small datasets, and a small number of weighting coefficients have misjudged the performance of the methods? Using a small number of weight coefficients on small datasets may mean that decision models cannot fully explore the entire range of potential outcomes. When working with a small number of samples, it becomes difficult to filter the effect of noise in the data. This may make the results of methods, such as the sensitivity analysis, inconsistent. In other words, when small data are used, the results are more affected by random variations. If the results of sensitivity analyses vary too much with the size of the dataset, weight combinations, or method selection, this will undermine the reliability of the analyses. Therefore, when testing the robustness of MCDM methods, it is necessary to also test the robustness of sensitivity analyses. Considering all these justified reasons, addressing LPI rankings with MCDM insights and methodology is an important research gap. In this study, an integrated MCDM model has been presented to assess the logistics performance of countries. Criteria weights were determined using the Entropy method, which offers the advantage of reducing the influence of decision-makers' personal judgments and enhancing objectivity. Objective weighting techniques offer significant benefits in computational efficiency, and applying these methods is crucial for achieving more significant results and improving decision-making quality [17]. Conversely, in this study, the MCRAT method has been selected, as it is a relatively new and infrequently utilized technique for assessing alternatives.