Evaluating the Logistics Performance of G8 Nations Using Multi-Criteria Decision-Making Models

Logistics has undergone significant advancements in history. It began with basic operations to carry goods between designated addresses in one direction and evolved into an international system of integrated multifaceted logistics networks and revolutions in technology. Increasing globalization has heightened the significance of logistics operations in global commerce, rendering it a pivotal factor in a nation's progress [1]. Consequently, there has been a demand for an approach to measure and evaluate the efficacy of logistics. For this purpose, in 2007, the World Bank established the Logistics Performance Index (LPI) as a means to evaluate the efficiency of nations in terms of logistics. It is an instrument specifically developed to assist nations in identifying the obstacles and prospects they encounter in their trade and logistics operations, while also providing suggestions on how countries might enhance their efficiency. It employs six fundamental variables to evaluate and classify nations based on their overall logistics performance [2]:

$~~$· The effectiveness of customs and border administration procedures.

$~~$· The standard of facilities pertaining to commerce and transportation.

$~~$· The convenience of organizing cost-effective global shipments.

$~~$· The proficiency and excellence of logistical services.

$~~$· The capability to monitor and follow shipments.

$~~$· The rate at which shipments are delivered to recipients within the designated or anticipated timeframe.

The logistics sector is experiencing rapid growth and has significant impacts on the financial state of nations. Assessing and appraising the logistics performance of nations can help them attain sustained advantages in competition by recognizing the advantages and disadvantages of logistics providers across the whole supply chain. The objective of this study is therefore to assess and prioritize the logistics performance of G8 nations.

Logistics performance evaluation involves the consideration of various factors that may clash with each other. Therefore, it is necessary to utilize methods that incorporate MCDM procedures. MCDM is a framework that allows for the identification of the most appropriate option from a set of established options when faced with several competing criteria.

There have been a few studies evaluated logistics performance with MCDM methods in the literature. Rezaei et al. [3] utilized the Best Worst Method (BWM) to determine the significances of six criteria employed by the World Bank in calculating LPI for various nations. Ulutaş and Karaköy [4] combined a subjective method called Stepwise Weight Assessment Ratio Analysis (SWARA) with an objective method known as Criteria Importance Through Intercriteria Correlation (CRITIC) to determine the weights of criteria. Alternatives are ranked by Proximity Indexed Value (PIV). This approach allows for a balanced consideration of the benefits and drawbacks of both weighting methods. Biswas and Anand [5] utilized Preference Selection Index (PSI) method and PIV methods to compare logistics performance of G7 and BRICS nations. Isik et al. [6] prioritized the logistics performance of 11 given nations in Central and Eastern Europe using Statistical Variance (SV) and Multi-Attributive Border Approximation Area Comparison (MABAC) approaches. Mercangoz et al. [7] utilized grey Complex Proportional Assessment (COPRAS-G) approach to evaluate the logistics performance ratings of 28 member states and five prospective nations for the European Union (EU). Yildirim and Mercangoz [8] examined and compared the logistical performance of OECD nations from 2010 to 2018 with the current LPI rankings using fuzzy Analytic Hierarchy Process (AHP) and grey Additive Ratio Assessment (ARAS). Ulutaş and Karaköy [9] used grey SWARA and grey Multi-Objective Optimization with Ratio Analysis (MOORA) methods to rank logistics performance of transition countries. Mešić et al. [10] assessed LPI of Western Balkan nations with CRITIC and Measurement Alternatives and Ranking according to Compromise Solution (MARCOS) techniques. Çalık et al. [11] evaluated the logistics performance of OECD countries using AHP, Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), Vise Kriterijumska Optimizacija I Kompromisno Resenje (VIKOR), and Combinative Distance-Based Assessment (CODAS) methods. Miškić et al. [12] created a comprehensive evaluation model for the LPI of EU nations using Method based on the Removal Effects of Criteria (MEREC) and Measurement of Alternatives and Ranking according to Compromise Solution (MARCOS). Nevertheless, our understanding is that there is a limitation of research in the literature about the logistical performance assessment of G8 countries. Hadzikadunic et al. [13] examined the influence of weights of criterion on the calculation of overall LPI scores, utilizing CRITIC and Full Consistency Method (FUCOM) to calculate the weights, with MARCOS to assess the performance of EU nations. Özbek and Özekenci [14] assessed the success of the digital logistics market in emerging nations through integrated MCDM approaches using Logarithmic Percentage Change-driven Objective Weighting (LOPCOW) approach was employed to ascertain criteria weights, whereas the alternatives were ordered utilizing Multi Attribute Utility Theory (MAUT), TOPSIS, MARCOS, and Combined Compromise Solution (CoCoSo). Oğuz [15] evaluated the customs, facilities and logistics services provided by the most successful nations in the 2023 LPI using Evaluation based on Distance from Average Solution (EDAS) and TOPSIS methods. Çıray et al. [16] intended to provide a more precise and resilient technique for a comprehensive analysis of LPI using the World Bank’s 2023 report with Entropy and Organisation, Rangement Et Synth&e De DonnCes Relarionnelles (ORESTE) method. Gürler et al. [17] employed 11 methodologies (ARAS, CoCoSo, CODAS, COPRAS, EDAS, Grey Relational Analysis (GRA), MABAC, MARCOS, MOORA, Operational Competitiveness Rating (OCRA), and Weighted Aggregated Sum Product Assessment (WASPAS)) to assess the logistics performance of EU members based on 33 factors to ensure robustness and uniformity. They also used Genetic Algorithms (GAs), CRITIC, Entropy, and Equal Weights to ascertain weights. Ju et al. [18] assessed LPI of EU countries using CRITIC and fuzzy Range of Value (ROV) methods.

The assessment framework that needs to be built has established goals that follow.

$~~$· To calculate the weights of the LPI with SD;

$~~$· To determine the logistics performance ranking of the G8 nations under evaluation with AROMAN;

$~~$· To offer managerial insights into the forthcoming model.

The primary contribution of this study lies in offering actionable insights for nations seeking to improve their logistics performance, thereby gaining a competitive advantage. These insights enable countries to allocate financial resources strategically based on the study’s recommendations. As a result, the proposed strategies may yield economic benefits by enhancing logistical efficiency.

This study is organized into six sections. The following section outlines the methodology employed. The "Results" section presents the analysis findings, including the criteria weights determined through the SD method and the rankings of alternatives generated by the AROMAN approach. The "Conclusion" section summarizes the key findings and offers recommendations for future research directions.

This section explains the use of the SD method for determining the criteria weights and the AROMAN approach for ranking the alternatives.

SD is an objective method to calculate criteria weights in MCDM. It uses following steps to calculate the weights of the criteria based on their respective SDs [19].

Step 1: The decision matrix is arranged. Eq. (1) illustrates the decision matrix ($A$).

Step 2: The decision matrix is normalized according to Eq. (2).

Step 3: The criteria weights are derived from Eq. (3).

where, ${{s}_{j}}$ denotes the SD of the $j-$th criterion and it can be obtained using Eq. (4).

This novel method, recently developed by Bošković et al. [20], [21], combines vector and linear normalization techniques to deliver precise data structures that can be used for further computations.

Step 1: A decision matrix is constructed, as presented in Eq. (1).

Step 2: The data in decision matrix are normalized with Eqs. (2) (linear) and (5) (vector).

Step 3: Eq. (6) is used to compute the aggregated normalization.

In Eq. (6), $\beta$ represents the aggregated averaged normalization, which is set to 0.5 for this study.

Step 4: Eq. (7) is used to compute the weighted normalization.

Step 5: Eq. (8) is used to sum the weighted normalized scores for the beneficial criteria.

Step 6: Eq. (9) is used to determine the final ranking of the available options.

The primary objective of this study is to evaluate the logistics performance of G8 nations. A decision matrix, consisting of countries and LPI criteria, is constructed. The decision matrix is presented in Table 1.

This decision matrix is normalized using Eq. (2). Table 2 indicates the normalized matrix.

The weights of the criteria are determined using Eq. (3) according to the SD method. Table 3 demonstrates the weights of the criteria.

According to the results of the SD method, the criteria are ranked by weight as follows: Timeliness, Tracking & Tracing, Infrastructure, International Shipments, Logistics Competence, and Customs. The AROMAN method is applied to determine the rankings of the countries. The decision matrix serves as the initial step of the AROMAN method and is presented in Table 1. Normalization of the decision matrix is performed using Eq. (2) and Eq. (5). The outcomes of normalization with Eq. (2) are provided in Table 4, while the results using Eq. (5) are shown in Table 5.

The resultant normalized values are integrated using Eq. (6). The outcomes of the amalgamation are displayed in Table 6.

Eq. (7) provides the weighted normalized values, which are presented in Table 7.

The weighted normalized values are aggregated using Eq. (8), and the rankings of the countries are derived from Eq. (9). The results, along with the country rankings, are presented in Table 8.

Based on the results of the AROMAN method, the countries are ranked as follows: Germany, Canada, Japan, France, the United States, Italy, the United Kingdom, and Russia. To evaluate the accuracy of the AROMAN method, its results are compared with those obtained from other MCDM methods, including COPRAS, ARAS, WASPAS, and MARCOS. All MCDM methods employed produced rankings identical to those generated by the AROMAN method. Therefore, it is concluded that the AROMAN method yields accurate findings.

In this study, the SD and AROMAN methods were employed to evaluate the logistics capabilities of G8 nations, yielding reliable and trustworthy outcomes. The AROMAN method produced results that were consistent with those obtained from other MCDM methods, including COPRAS, ARAS, WASPAS, and MARCOS. This consistency indicates that the AROMAN method is a dependable tool for assessing logistics performance.

The findings reveal that Germany possesses the most efficient logistics system among the G8 nations, reflecting its superior quality in logistics services, customs clearance, and infrastructure. Conversely, Russia ranks lowest, highlighting the significant potential for improvement within its logistics systems and the logistical challenges it faces. This underscores the necessity for countries like Russia to make strategic investments aimed at enhancing their logistics networks.

This study serves as a valuable resource for policymakers and managers interested in improving the logistics sector through comparative analyses of national logistics performance. The results can assist G8 countries in formulating strategies for optimizing resource allocation, given that logistics performance significantly influences economic growth and competitiveness.

Future research could further explore the impact of logistics performance across various sectors and industries. Additionally, integrating the AROMAN method with other MCDM approaches may enhance the literature in this field and provide insights into logistics performance improvements in different countries. Such developments can help nations create a competitive advantage in global commerce by optimizing their logistics capabilities.

This study presents a comprehensive analysis of the logistics performance of G8 countries, employing the SD and AROMAN methods for evaluation and ranking. The findings from the SD method indicate the following ranking of LPI criteria based on their weights: Timeliness, Tracking & Tracing, Infrastructure, International Shipments, Logistics Competence, and Customs. In terms of logistics efficacy, Germany is ranked highest, while Russia ranks lowest. The results of the AROMAN method are consistent with those obtained from other MCDM methods, including COPRAS, ARAS, WASPAS, and MARCOS, demonstrating the reliability and validity of the AROMAN method in evaluating logistics performance.

Logistics performance is a critical determinant of a nation's economic competitiveness and its ability to engage in global trade, a finding that is supported by the results of this study. Consequently, nations can enhance their competitiveness in the global marketplace by optimizing their logistics systems. Specifically, countries with inadequate logistics performance are at risk of increased vulnerabilities in this domain and should consider investing in their logistics infrastructure and processes.

It is recommended that future research explore the logistics performance of countries across distinct geographical regions and examine the impact of this performance on various economic and social indicators in greater detail. Such research will facilitate the development of strategies aimed at improving logistics performance, ultimately contributing to the more sustainable and efficient management of global supply chains.