Logistics Performance Of Countries Research Articles

How Criteria Weights Influence Performance in Evaluating Logistic Productivity: An Application in the Emerging Markets Logistics Index

Purpose: The differences between the criteria affecting the logistics performance of countries and their importance levels are meaningful in terms of policy development processes. It has been determined that the criteria are weighted equally in the emerging markets logistics index. For this reason, the study reweighted the criteria of the Emerging Markets Logistics Index and investigated the effects of weighting on the ranking. In this respect, the study aims to make the index more objective. Methodology: In the study, Multi-Criteria Decision Making methods were utilized. Within this context, MEREC (Method Based on the Removal Effects of Criteria) was used to determine the criteria weights, while MABAC (Multi Attributive Border Approximation Area Comparison) and MAIRCA (Multi Attributive Ideal Real Comparative Analysis) methods were preferred to rank the alternatives. Findings: In the study, it was concluded that the weighted values of the criteria are more consistent with the literature. Additionally, the new weights obtained have an effect on the ranking values of the countries. Orginality: It is important that emerging markets provide an opportunity to develop infrastructure to increase logistics productivity and provide a platform for the implementation of new technologies in logistics operations. Furthermore, these markets enable the diversification and development of logistics services through the expanding consumer demand. This study differs from other studies in the literature because it preferred the Agility Emerging Markets Logistics Index (AEMLI) instead of the Logistic Performance Index (LPI) and used MEREC-based MABAC-MAIRCA methods.

Evaluation of Logistics Performances of G20 Countries Using SD-Based COPRAS and SAW Methods

One of the important issues in the economic development of countries is their effectiveness in logistics activities. Countries gain competitive advantage by maintaining effective and efficient logistics processes. Therefore, determining logistics performance is important for both businesses and countries. The main aim of this study is to examine the logistics performances of countries in the context of G20 countries and to determine how they change over time. Within the framework of this aim, the Logistics Performance Index published by the World Bank has been used to determine the logistics performance of countries (LPI (2018) and LPI (2023)). Standard Deviation (SD) method has been used in weighting the criteria “customs, infrastructure, international shipments, logistics competence and quality, timeliness, tracing and tracking” included in the LPI and in determining the performance of G20 countries. Data for 2018 and 2023 have been examined using the methods COPRAS (Complex Proportional Assessment) and SAW (Simple Additive Weight). The results obtained from the methods have been compared with LPI (2018) and LPI (2023). As a result of the analysis, according to the COPRAS method, Germany, Japan, and the United Kingdom rank first in 2018, while the Russian Federation, Argentina and Brazil rank last, respectively. According to 2023 data, Germany ranks first according to both methods, while Canada and Japan follow Germany in line with the COPRAS method. According to the SAW method, Japan and Canada follow Germany. Russia and Argentina rank in last place in both methods, similar to the current index.

Addressing the Global Logistics Performance Index Rankings with Methodological Insights and an Innovative Decision Support Framework

This study examines the Logistics Performance Index (LPI) rankings developed by the World Bank from a methodological perspective and proposes an alternative decision support framework. LPI serves as an interactive tool that helps countries identify challenges, innovative solutions, and opportunities in their trade and logistics sectors. In this study, the efficiency of logistics operations in 118 countries was evaluated using an integrated multi-criteria decision-making (MCDM) model objectively weighted by the Entropy method. Countries were ranked using the MCRAT, SAW, TOPSIS, and FUCA methods. According to the findings, large datasets provide more robust insights for sensitivity analyses, and wider weighting coefficient combinations make the data more meaningful. In addition, it is suggested to use low-compensation methods instead of classical additive methods for LPI. Unlike other studies in literature, this research applied an innovative sensitivity analysis to test the robustness of the model and comprehensively examined the effects of weighting techniques based on over 2500 different MCDM results. The findings suggest that the FUCA method should be recommended to decision-makers for calculating LPI rankings due to its simplicity, practicality, low compensatory power, and low sensitivity. This study offers methodological improvements when evaluating logistics performance and provides significant contributions to decision-making processes. The findings are expected to provide a valuable resource for policymakers and businesses in understanding a country’s position in global competition, as well as serving as a reference for researchers evaluating the logistics performance of countries.

Logistics Performance Index of Africa: An Indicator for Türkiye and Africa Trade Relations?

The logistics performance of countries is important in trade flows. As part of the supply chain process, logistics is the backbone of trade processes. In this direction, the Logistics Performance Index (LPI) put forward by the World Bank can be considered as an indicator. With the LPI, countries have the opportunity to evaluate the effectiveness of their activities in trade and logistics. The aim of the study is to rank and analyze African countries with the integrated Entropy-MOORA Reference Approach using the LPI values for 2023. In order to increase the depth of the study, an evaluation is made based on the trade data between Türkiye and African countries for 2022. The study aims to rank African countries on the basis of six criteria defined by the MOORA Reference Approach and to provide a perspective on the development of Türkiye’s trade relations with African countries in line with trade figures. According to the results of the study, South Africa ranks first, Botswana second, and Libya last in the ranking of LPI countries. Türkiye’s trade volume with the continent, South Africa ranks fifth, Botswana fifty-third, and Libya third. Therefore, there is no correlation between LPI and trade data in current situation.

Evaluation of customs, infrastructure and logistics services with multi-criteria decision-making methods: A comparative analysis for the top 10 countries in the logistics performance index

Purpose- The aim of this study is to examine the performance of customs, infrastructure and logistics services of the countries ranked in the top 10 in the Logistics Performance Index (LPI) ranking in 2023 with Multi-Criteria Decision Making Methods (MCDM) and to rank and compare them. The World Bank's LPI criteria and weights were taken into consideration while determining the criteria considered within the scope of the study. Methodology- When employing the MCDM methods, various elements are taken into account during the decision-making process, and options are graded in accordance with these criteria. By taking into account the importance of many criteria and the performance of alternatives, MCDM techniques provide researchers with an organized and objective method for making difficult judgments. In this study Topsis and Edas methods, which are among the MCDM methods, were used to rank the countries. Findings- When the results of the analysis are summarized, it is observed that the first three ranks differ from the results of the LPI report. According to the Topsis method, Finland ranks first, Singapore ranks second and Austria ranks third. According to the Edas method, Singapore and Finland ranked in the first two places as in the LPI 2023 report, while Switzerland ranked third. The findings of the study show that the rankings may be different even when the criteria and criteria weights used within the scope of the analysis are the same in MCDM methods. Conclusion- The findings demonstrate that, even though the criteria and weights employed within the parameters of the study are the same, the rankings in MCDM approaches may differ. This is due to the possibility of various computation strategies or theoretical underpinnings for the procedures being used. The results of this study can therefore be applied to assist decision-makers in this field and improve a country's logistics performance. By illustrating the utility of MCDM techniques in making such decisions, it also makes an important contribution to ongoing research in this field. Keywords: Logistics performance, LPI, Multi-Criteria Decision Making, MCDM, Edas, Topsis. JEL Codes: D61, D91, L91

An application of machine learning regression to feature selection: a study of logistics performance and economic attribute

This study demonstrates how to profit from up-to-date dynamic economic big data, which contributes to selecting economic attributes that indicate logistics performance as reflected by the Logistics Performance Index (LPI). The analytical technique employs a high degree of productivity in machine learning (ML) for prediction or regression using adequate economic features. The goal of this research is to determine the ideal collection of economic attributes that best characterize a particular anticipated variable for predicting a country’s logistics performance. In addition, several potential ML regression algorithms may be used to optimize prediction accuracy. The feature selection of filter techniques of correlation and principal component analysis (PCA), as well as the embedded technique of LASSO and Elastic-net regression, is utilized. Then, based on the selected features, the ML regression approaches artificial neural network (ANN), multi-layer perceptron (MLP), support vector regression (SVR), random forest regression (RFR), and Ridge regression are used to train and validate the data set. The findings demonstrate that the PCA and Elastic-net feature sets give the closest to adequate performance based on the error measurement criteria. A feature union and intersection procedure of an acceptable feature set are used to make a more precise decision. Finally, the union of feature sets yields the best results. The findings suggest that ML algorithms are capable of assisting in the selection of a proper set of economic factors that indicate a country's logistics performance. Furthermore, the ANN was shown to be the best effective prediction model in this investigation.

Impacts of trade facilitation on modal choice and international trade flows

Over the last decade, the use of air transport to facilitate global merchandise trade has grown significantly, driven by the increasing demand for ‘just in time’ delivery of intermediate goods as well as due to the growing importance of trade between more distant countries. In this paper we analyze the effect of improved global logistics and trade facilitation on transport mode choice in international trade using the Global Trade Analysis Project (GTAP) model of global trade which we modify to incorporate modal choice. To parameterize this model, we develop a continuous modal choice model and estimate the associated elasticities of substitution between transport modes. We find that modal substitution elasticities have values ranging from 0.57 to 2.14, indicating significant response to changes in the relative cost of different modes of goods transport. The quality of logistics infrastructure is also found to influence modal choice in international trade with improved logistics performance generally leading to increased reliance on air transportation.We validate the modified and parameterized GTAP model by simulating the impact of improvements in logistics, measured as a change in the Logistics Performance Index (LPI). We find that improvement in LPI in the poorest countries of the world reduces the overall cost of transport and amount of services required to transport a given product along a given route by a given mode. Importantly, the reduction in modal cost of transport results in modal substitution. We use the framework to explore the consequences of an improvement in low income country logistics performance for GDP and economic welfare.

Digital Technologies for Improving Logistics Performance of Countries

Abstract The logistics performance has a crucial role in the industrial and economic development of countries. This study aims to underline implications for policy makers in improving the logistics performance of countries in terms of Industry 4.0. For this purpose, the effect of digitalisation on logistics performance is analysed by using correlation and multiple regression analysis. The empirical study builds upon dimensions and indicators of the Digital Economy and Society Index (DESI) and Logistics Performance Index (LPI) of the World Bank. The results indicate that governmental policies should target to deliver sound framework conditions for the generation of human capital (here: ICT specialists), sustainable usage of internet services (e.g. professional social networks, online sales, etc.), integration of digital technologies (e.g. Big Data, Cloud computing, etc.), as well as digital connectivity (here: fixed broadband and 4G coverage) in order facilitate improvement of logistics performance.

Exploring National Culture Impact on Logistics Performance

Abstract The increased logistics complexity due to supply chain globalization requires deeper understanding of a country's logistics performance. This performance is usually captured through infrastructure, services, and procedure elements and is understood in operational and economical terms. This exploratory research adds a social view to understand logistics performance variation across countries through focusing on their national culture dimensions. Using secondary data from the international Logistics Performance Index (LPI) report as well as the scores from Hofstede national culture dimension surveys, the impact of national culture on logistics performance was explored. Statistical results showed Power Distance and Uncertainty Avoidance dimensions were found to have a negative correlation with LPI while Individualism as well as Long-Term Orientation were both positively correlated to LPI. This cultural understanding adds to the growing social literature research on logistics performance and can guide logistics managers in their decisions regarding where and when to locate their resources.

An unstructured big data approach for country logistics performance assessment in global supply chains

PurposeThe purpose of this study is to explore the potential for the development of a country logistics performance assessment approach based upon textual big data analytics.Design/methodology/approachThe study employs design science principles. Data were collected using the Global Perspectives text corpus that describes the logistics systems of 20 countries from 2006–2014. The extracted texts were processed and analysed using text analytic techniques, and domain experts were employed for training and developing the approach.FindingsThe developed approach is able to generate results in the form of logistics performance assessments. It contributes towards the development of more informed weights of the different country logistics performance categories. That said, a larger text corpus and iterative classifier training is required to produce a more robust approach for benchmarking and ranking.Practical implicationsWhen successfully developed and implemented, the developed approach can be used by managers and government bodies, such as the World Bank and its stakeholders, to complement the Logistics Performance Index (LPI).Originality/valueA new and unconventional approach for logistics system performance assessment is explored. A new potential for textual big data analytic applications in supply chain management is demonstrated. A contribution to performance management in operations and supply chain management is made by demonstrating how domain-specific text corpora can be transformed into an important source of performance information.

Possibilities of using Incoterms clauses in a country logistics performance assessment and benchmarking

This study aims to contribute to the improvement of a country's transport and logistics (TL) performance assessment by exploring the possibility of using International commercial terms (Incoterms) clauses in international sales contracts. Incoterms clauses are commercial terms that clearly determine sellers' logistics commitments. They are also the “channels” that connect trade and logistics. The research explores the relationship between the international TL performance indicators and the Incoterms score (IS) which represents the extent of sellers' logistics commitments in bilateral trade at a national level. As per empirical results, the logistics performance indexes (LPIs) show the significant impact of a country's logistics performance on the IS. The country's economic development is also correlated with the IS and the domestic logistics performance indicators. This correlation is not seen with regards to the global competitiveness of transport infrastructure. Additionally, while the geographic distance does not have a direct correlation, it impacts buyers' or sellers' obligations, when considered along with the LPIs in the gravity model.According to the empirical results, a conceptual framework for the usage of applied Incoterms clauses for logistics performance assessment and benchmarking is proposed. The policymakers may use ISs as additional indicators when assessing a country's logistics performances as well as to estimate the possibilities of expanding the TL market for promoting international trade flows. Such assessment enables both national and international benchmarking to rely on the national database. The usage of Incoterms clauses for monitoring, assessing, and benchmarking a country's TL performance must be considered more profoundly by the policymakers in the future.

Effect of Knowledge Spillover on the Logistics Performance

This study aims is to investigate whether FDI inflows and innovative activities act as an knowledge spillover channel that increases the logistics performance of countries. In the analysis, the effects of these activities on the logistics performance were questioned according to the development levels of countries and six different logistics performance indicators by the Ordinary Least Squares (OLS) Estimator method. The estimation results show that FDI, R&D investments and patent applications, which are considered as the knowledge spillover channels, have a statistically significant and positive effect on the logistics performance of countries. In addition, it is concluded that this effect is higher in developing countries compared to developed ones.

Analyzing two-way interaction between the competitiveness and logistics performance of countries

Logistics has crucial importance in national and international trade and, hence, in the development and competitiveness of a country. On the other hand, making investments in different pillars of competitiveness, such as infrastructure, higher education, etc., is expected to enhance logistics performance. In this study, this two-way interaction between the competitiveness and logistics performance of countries is investigated using a hybrid methodology. Initially, the causal directions between the competitiveness of countries and their logistics performance are established by using a Bayesian Net (BN). Subsequently, the cause-effect information gathered from the BN is taken as the input in a Partial Least Square (PLS) path model to highlight the competitiveness pillars that are more critical in contributing to countries’ logistics performance. As the last step, an importance-performance map analysis (IPMA) is applied to specify the importance of the pillars that have a significant effect on logistics performance. As a result, a roadmap is provided to policymakers that specify which pillars to focus on, thus delivering a significant and immediate improvement in the logistics performance and highlighting which logistics performance indicators will lead to improvements in the competitiveness of the countries. An empirical study is conducted based on two basic indexes, as follows: (1) the Global Competitiveness Index (GCI) and its pillars are used to track the competitiveness performance, and (2) the Logistics Performance Index (LPI) is used to analyze the logistics performance. According to the results, the most important GCI pillars that affect the logistics performance of a country are determined to be “Business Sophistication”, “Financial Market Development”, “Infrastructure” and “Good Market Efficiency” and “Higher Education and Training”. On the other hand, the improvement in the logistics performance index, in its turn, will especially influence the Market Size pillar of a country.

Reálna konvergencia logistickej výkonnosti krajín sveta, EÚ a V4

Using the database of logistics performance of countries in the period 2007-2016, we study the convergence of countries in terms of logistics performance across the world, the European Union and V4. In comparison with other beta-convergence studies using the OLS estimation method, we also implement a conditional quantile regression (Koenker and Bassett, 1978). The use of the quantile regression allows us to study the convergence of countries not only from the point of view of the average, but also from the perspective of different quantiles of conditional distribution. We identify countries with different leveles of convergence and quantify different levels of convergence across countries of the world. The results provide empirical evidence of the logistics convergence of countries across the world, and also demonstrate that the countries of the European Union and V4 achieve a higher level of convergence than the average convergence of countries across the world during the review period.

Study of Port Tariff Structure and Port Pricing Approach

In order to support National Logistics System continuity there are at least three (3) main components that should synergize well, namely the sea side (sea transport), the land side (hinterland transport) and the part that connects between the two (port). Port performance will greatly affect the performance of the national logistics system as a whole. One of the indicator of a country's logistics performance evaluation is the ease of arranging shipments of goods at competitive prices (shipment). One of the components of logistics costs which become concern of many parties is the port costs, in order to support the national high logistics cost reduction, study related to the port tariff structure and its associated regulations is needed, especially a matter to formulate and determining policy related to pricing port services. The survey and analysis will be carried out in an attempt to identified the port tariff structure as basis to determining the port pricing model. Port tariff structure analysis include the Identification of port services and cost component (user and port operator point of view). Based on the analysis of transport logistics cost, the shipping cost contribute 48%, port cost 40% and hinterland cost 12%. The port cost on the container terminal, the stevedoring costs contribute 42%, followed by the cargodoring cost 58%. It takes further analysis for stevedoring tariff and tariff lift-on / lift-off container given a large contribution to the overall cost of loading and unloading at the port. Port pricing formulation problem associated with cost (competitiveness), performance (level of services) and value added (value added to the customer), so that the best approach for determinining port cost and tariff are: (1) the first best pricing approach is MC = MR = P, (2) the second best pricing approach is LRMC. We suggest that the pricing policy for ports where tariff formulation needs to consider the external factors (currency, rates, fuel price, minimum salary and etc) and differentiated based on the level of port service. Relevant quality level of port service factors are the time in port, and the punctuality of handling the vessel and its cargo. Port Tariff = f (Cost of Goods Manufactured (production unit cost), Margin, Level of Service (LS)) and Maximum Port Tariff = 1.25 % ofproduction unit cost .

Country logistics performance and disaster impact.

The aim of this paper is to deepen the understanding of the relationship between country logistics performance and disaster impact. The relationship is analysed through correlation analysis and regression models for 117 countries for the years 2007 to 2012 with disaster impact variables from the International Disaster Database (EM-DAT) and logistics performance indicators from the World Bank. The results show a significant relationship between country logistics performance and disaster impact overall and for five out of six specific logistic performance indicators. These specific indicators were further used to explore the relationship between country logistic performance and disaster impact for three specific disaster types (epidemic, flood and storm). The findings enhance the understanding of the role of logistics in a humanitarian context with empirical evidence of the importance of country logistics performance in disaster response operations.

Ulusal İnovasyon Göstergeleri ile Ulusal Lojistik Performansı Arasındaki İlişki: AB Ülkeleri Üzerine Bir Araştırma

Innovation is an emerging subject nearly for 50 years. Nations use innovation strategies as a tool for sustainable competitive power just like firms do. But innovation strategies yield different advantages for business sectors. The aim of this study is determine the relation between national logistics performance and national innovation indicators and analyze the effect of national innovation indicators on logistics performance of countries. Given the importance of logistics sector for countries, this study will provide a significant contribution to literature by studying increasing measures for logistics performance from the innovation perspective. In addition, this issue, which is not examined in the literature before, also increases the importance of this study. After the analysis performed, it is found that there is a positive correlation between logistics performance and innovation metrics datasets. Finally, positive and statistically significant effect of human resources and intellectual assets on logistics performance is presented as innovation policy proposal