Multimodal logistics systems in Kazakhstan: comparative assessment of logistics performance and international transport corridors

1 Introduction

1.1 Multimodal logistics corridors in a global context

In the context of the deepening international division of labor and the continued growth in global trade volumes, transport and logistics systems are becoming key drivers of spatial and economic development. Contemporary freight flows are characterized by increasing complexity, requiring the coordinated integration of multiple transport modes within a single logistics chain (Makarova et al., 2023).

Multimodal transport involves the use of two or more modes of transport within an integrated logistics framework, enabling freight operations to adapt to geographical, infrastructural, and institutional conditions. As noted by Balbaa (2022), such integration leads to the formation of multimodal logistics corridors that combine transport infrastructure, terminals, transport nodes, and supporting services into an integrated functional system.

These corridors play a crucial role in territorial development by facilitating regional integration into global supply chains, stimulating the formation of new economic linkages, and increasing the investment attractiveness of regions. At the same time, Makarova et al. (2023) emphasize that the development of multimodal corridors is associated with several risks, including infrastructure imbalances, institutional constraints, limited capacity, and insufficient coordination among logistics stakeholders.

In recent years, particular attention has been paid to the transformation of international transport corridors in the context of the reconfiguration of Europe-Asia supply chains. Within the Belt and Road Initiative, transport corridors are considered a strategic tool for strengthening Eurasian connectivity and diversifying transport routes (Tsui et al., 2017). In this context, synchro modal and multimodal logistics approaches are becoming increasingly relevant, as they enable flexible management of transport flows and optimization of interactions between different modes of transport (Motowidlak, 2022).

Thus, multimodal logistics corridors should be viewed as complex socio-economic and infrastructural systems, the functioning of which depends on the interaction of spatial, transport, institutional, and organizational factors. This determines the need for further research aimed at identifying the mechanisms of formation, functioning, and transformation of multimodal logistics corridors in specific national and regional contexts.

1.2 Infrastructure efficiency and the functional role of multimodal logistics corridors

The efficiency of multimodal logistics corridors is largely determined by the level of development and spatial organization of transport and logistics infrastructure. As demonstrated in the study by Pokrovskaya et al. (2020), logistics facilities such as terminals, intermodal hubs, dry ports, and distribution centers represent key elements shaping the throughput capacity and operational sustainability of transport corridors. Their spatial distribution and functional specialization directly influence cargo consolidation processes, the redistribution of flows between different modes of transport, and the continuity of logistics chains.

In the literature, intermodal and multimodal logistics centers are considered critical nodes that integrate various transport modes within a unified transport and logistics system. Wagener (2017) notes that such centers perform not only technical functions related to cargo transshipment and storage, but also coordination functions that ensure interaction between carriers, logistics operators, and public authorities. The level of institutional and technological maturity of these centers significantly determines the overall efficiency of freight corridors.

From the perspective of territorial development, multimodal logistics corridors have a complex socio-economic impact. This includes improving transport accessibility, creating new economic linkages, and supporting sustainable development. According to Filina-Dawidowicz et al. (2022), the positive effects of multimodal transport systems, including reduced logistics risks and improved system resilience, depend on the coordinated development of infrastructure and logistics services along transport corridors.

Comparative analyses of trans-Eurasian transport corridors reveal significant spatial differences in infrastructure efficiency across countries and regions. Rygzynov et al. (2023) show that territories located along major international transport routes tend to demonstrate higher levels of infrastructure efficiency due to the concentration of freight flows and export-oriented economic structures. At the same time, peripheral and border regions face infrastructural and institutional constraints that limit the realization of multimodal corridor potential.

The existing body of research indicates that assessing the performance of multimodal logistics corridors should not be limited to the availability of transport infrastructure. It is necessary to consider the functional connectivity of logistics facilities, the degree of coordination between transport modes, and the broader regional socio-economic conditions under which these corridors are formed and developed.

1.3 The transit potential and competitiveness of Kazakhstan’s transport and logistics system

Kazakhstan’s transport and logistics system is developing under conditions of increasing dependence on transit traffic and foreign trade flows, which determines its strategic importance in the Eurasian transport space. Augan and Alipbayev (2023) argue that the competitiveness of the national transport and logistics system is determined not only by its geographical position between Europe and Asia, but also by its ability to effectively integrate into international transport corridors, ensuring the stability and predictability of freight flows.

Empirical studies focusing on Kazakhstan’s transit potential highlight the key role of infrastructure development in supporting transit functions. Tashenova et al. (2022) report that between 2022 and 2023 the volume of transit freight transported through Kazakhstan exceeded 30 million tonnes, with the dominant share accounted for by rail transport operating within multimodal logistics chains. The authors emphasize that the effectiveness of transit operations is directly linked to the development of terminals, logistics hubs, and cargo consolidation centers, which constitute the structural basis of multimodal supply chains.

From the perspective of infrastructure optimization, Kenzhebay et al. (2024) demonstrate that the modernization of transport and logistics facilities, as well as the development of multimodal logistics centers in border areas and industrial zones, contributes to reducing cargo handling time and increasing corridor capacity. According to their estimates, these measures can increase the transit potential of international transport routes by 20%–25%.

At the regional level, Zhiltsov (2024) highlights the growing role of Kazakhstan in the system of transport linkages in Central Asia. The country’s active participation in trans-Eurasian transport initiatives, including the Trans-Caspian International Transport Route and the North–South Corridor, has contributed to the growth of container traffic and the emergence of new logistics hubs. In 2023, transit volumes through Kazakhstan increased by more than 20%, reflecting the strengthening of its position in the regional transport and logistics system.

The findings of existing studies indicate that the competitiveness of Kazakhstan’s transport and logistics system is shaped by a combination of infrastructural, operational, and institutional factors. This underscores the importance of a comprehensive analysis of the functioning of multimodal logistics corridors, particularly in the context of increasing competition between international transport routes.

1.4 The logistics performance index and methods for assessing the performance of logistics corridors

Modern research increasingly emphasizes the quantitative assessment of transport and logistics systems, particularly in the context of international transport corridors. One of the most widely used tools for such evaluation is the Logistics Performance Index (LPI), developed by the World Bank, which enables cross-country comparison of logistics performance based on a set of standardized indicators.

In their cross-continental analysis, Jayathilaka et al. (2022) examine the relationship between logistics performance, economic development, and global trade. Using data from multiple regions, the authors identify a strong positive correlation between LPI scores, gross domestic product, and foreign trade volumes. Their findings indicate that countries with higher LPI values tend to demonstrate more intensive trade flows and a higher level of integration into global supply chains, confirming the validity of LPI as a tool for assessing the competitiveness of transport and logistics systems.

The LPI is not a single aggregate indicator, but rather a composite index consisting of several components that reflect different dimensions of logistics performance. Sharawi et al. (2025) provides a detailed analysis of the contribution of the six key LPI components, namely, customs efficiency, infrastructure quality, ease of international shipments, logistics service competence, tracking and tracing capabilities, and timeliness, to the overall index. Based on regression analysis of data from 105 countries over the period 2007–2023, the study demonstrates that infrastructure quality and logistics service performance are the most influential factors, explaining more than 90% of the variation in LPI values.

Furthermore, Sharawi et al. (2025) identifies significant regional differences in the importance of individual LPI components. In developed regions such as Europe and Asia, infrastructure and logistics competence are the primary determinants of logistics performance, whereas in developing economies greater importance is attached to institutional constraints, particularly customs procedures and the availability of digital tracking systems. These results highlight the need to adapt logistics performance assessment approaches to specific regional and national contexts.

The application of LPI in transport and logistics research provides a robust basis for international comparison and allows for the identification of structural constraints affecting the performance of logistics corridors. However, when analyzing multimodal logistics corridors, LPI should be complemented by additional indicators that capture corridor-specific characteristics, such as transit route configuration, node capacity, and the level of coordination between different transport modes. This creates a more comprehensive methodological framework for evaluating corridor performance. This study examines the development of multimodal logistics systems in Eurasia by analyzing logistics performance indicators and identifying structural disparities across selected countries, particularly Kazakhstan.

Accordingly, the central research problem of this study is how the effectiveness of multimodal logistics systems in Kazakhstan can be assessed when national logistics performance, corridor infrastructure, company-level operations and route-specific risks are considered together rather than separately. The research gap lies in the limited integration of macro-level LPI dynamics, spatial corridor comparison, company-level operational evidence and operational risk assessment within a single analytical framework. Based on this problem, the study addresses three research questions.

• RQ1: How does Kazakhstan’s logistics performance compare with benchmark and regional countries in terms of LPI indicators?
• RQ2: What operational and organizational bottlenecks affect multimodal logistics performance at the company level?
• RQ3: How do route modelling and FMEA-based risk assessment explain the efficiency and vulnerability of selected multimodal corridor configurations?

To achieve this, the study addresses the following tasks.

• analyzing key logistics performance indicators based on the Logistics Performance Index (LPI);
• comparing logistics development across selected Central Asian countries, China and Turkey;
• examining the temporal dynamics of logistics performance between Kazakhstan and China over the period 2007–2023;
• evaluating spatial and structural differences in logistics systems;
• illustrating the practical functioning of multimodal logistics through a case study of Globalink Logistics;
• assessing multimodal route efficiency using a modelling approach;
• supporting the case study with company-level diagnostics based on questionnaires, including an assessment of operational efficiency and FMEA-based risk assessment.

This study contributes to the literature by integrating comparative quantitative analysis, spatial comparison, temporal assessment, case study evidence, questionnaire-based diagnostics, route modelling and FMEA-based risk assessment within a unified analytical framework. In this way, it provides a more comprehensive understanding of logistics system development in Eurasia and of the factors shaping multimodal corridor effectiveness.

The article is structured as follows. Section 1 introduces the research background and outlines the study’s objectives. Section 2 reviews the relevant literature. Section 3 presents the data sources and research methodology. Section 4 discusses the empirical findings, including comparative results, model-based analysis and company-level diagnostics. Section 5 concludes the study.

2 Literature review

2.1 Optimization models and decision-making in multimodal transport systems

Liu (2023) develops and substantiates a multimodal transport route optimization model for cold chain container transportation under time-varying network conditions, explicitly incorporating carbon emissions as a key constraint. The study is based on a multi-objective optimization framework in which the total transportation cost is minimized while accounting for environmental, temporal, and operational factors. The proposed objective function is formulated as:where C 1 represents transportation costs, C 2 denotes carbon emission costs, C 3 corresponds to time window penalty costs, and C 4 reflects cargo damage costs associated with temperature-sensitive goods. This formulation enables the model to capture the trade-off between economic efficiency and environmental sustainability within multimodal logistics systems.

m i n C = C 1 + C 2 + C 3 + C 4

The study further incorporates dynamic parameters affecting transport performance, including traffic congestion, weather conditions, and vehicle operational characteristics. For instance, vehicle speed under congestion is modeled as:where μ is the steering coefficient reflecting intersection complexity, and β 1 represents the congestion coefficient influenced by multiple external factors.

V m = V i × 1 − 0.1 μ × β 1

This approach allows the model to reflect real-world variability in transport conditions and its impact on delivery time and emissions.

To solve the optimization problem, Liu (2023) applies an improved genetic algorithm combined with a queen bee evolutionary strategy, which enhances global search capability and convergence speed. The algorithm introduces mechanisms such as elite retention, adaptive crossover, and mutation operations, ensuring more stable and efficient optimization under complex dynamic conditions.

Empirical testing based on the Yangtze River Delta transport network demonstrates that the optimal total cost in a dynamic environment reaches 14,757.7 yuan, compared to 13,286.03 yuan in a static scenario. This represents an increase of approximately 11.07%, indicating that neglecting time-varying factors may lead to a substantial underestimation of actual logistics costs. At the same time, the improved algorithm shows an increase in convergence efficiency of over 12% compared to traditional genetic algorithms, confirming its effectiveness in solving large-scale multimodal transport problems.

The findings highlight that road transport is more sensitive to external environmental factors, while railway transport exhibits greater stability under dynamic conditions. This suggests the strategic importance of shifting freight flows toward rail-based multimodal solutions to enhance system resilience and reduce environmental impact.

The study contributes to the development of integrated decision-making models for optimizing multimodal transport systems, providing a methodological basis for improving efficiency, reducing emissions, and enhancing the sustainability of logistics operations under uncertain and dynamic conditions. In addition to optimization models focused on transport routes and cost–emission trade-offs, recent studies have increasingly emphasized the role of advanced data-driven approaches in improving decision-making within multimodal systems.

Luo et al. (2024) examine machine-learning-based data fusion methods for analyzing multimodal data by integrating textual, visual, and audio information. The study highlights that considering the interdependence between different data modalities significantly enhances the reliability of analytical outcomes, particularly in evaluating the trustworthiness of microenterprises. By applying deep learning techniques, the authors demonstrate that multimodal data fusion improves both predictive accuracy and interpretability compared to single-source analysis. Furthermore, the findings confirm that combining heterogeneous data sources enables more robust and comprehensive assessments in digital business environments, thereby supporting more informed strategic decision-making processes (Luo et al., 2024).

Recent studies emphasize that multimodal transport optimization must account for uncertainty in key parameters such as demand, travel time, and transshipment conditions. For instance, Li et al. (2023), using fuzzy nonlinear programming and cooperative game theory, demonstrate that incorporating uncertain variables into the optimization model allows for more realistic and adaptive route planning. The inclusion of time windows, stochastic demand, and variable transportation costs significantly affects the selection of optimal routes and transport modes. Moreover, comparative analyses of different algorithms, such as MOPSO, NSGA-II, and cooperative game theory-based weighted sum methods, show that hybrid and dynamically adjusted approaches provide more stable and efficient solutions under uncertain conditions. These findings highlight that addressing uncertainty is essential for improving the robustness, reliability, and sustainability of multimodal transport systems.

Multimodal transport hubs play a critical role as transfer points in transport networks, integrating various modes and handling large passenger flows. Xu et al. (2022) provide a comprehensive review of passenger emergency evacuation from multimodal transportation hubs, highlighting that evacuation processes typically include both indoor pedestrian evacuation and multimodal transportation evacuation stages. The study emphasizes that most existing research focuses on evacuating passengers from inside hub buildings, while evacuation at transfer points remains insufficiently studied. Furthermore, the authors note that many models attempt to generalize evacuation scenarios; however, more efficient results can be achieved by developing models tailored to specific emergency types and hub characteristics. The research also identifies limitations in current data collection methods and stresses the need for more accurate and innovative approaches to improve evacuation modeling and decision-making.

2.2 Transport corridors and their impact on economic and spatial development

Lakhminarayanan et al. (2024) emphasize that transport corridors play a crucial role in shaping regional economic development through spatial connectivity and infrastructure integration. Their study highlights that well-developed transport networks facilitate the movement of goods, services, and labor, thereby strengthening economic interactions between regions and contributing to balanced territorial development.

The importance of governance and institutional efficiency in corridor development is further discussed by Okonge and Nyaronga (2025), who demonstrate that transport corridors function not only as physical infrastructure but also as coordinated systems requiring effective policy frameworks, streamlined transit procedures, and regional cooperation. Their findings show that improvements in corridor management significantly reduce transport costs and enhance trade facilitation, ultimately supporting economic growth.

The spatial-economic effects of transport corridors are analyzed through the interactions between urbanized regions, demonstrating that logistics corridors reshape economic space between urban agglomerations and generate new growth poles. These corridors contribute to reducing regional disparities by redistributing economic activity and enhancing interregional connectivity. Furthermore, the intensity and direction of transport flows directly influence the spatial distribution of economic activities, reinforcing the strategic role of transport systems in regional development (Luo et al., 2022).

Finally, Chen & Hasan (2023) examine the determinants of logistics performance with a particular focus on liner shipping connectivity and global competitiveness. The results indicate that the impact of these factors is largely mediated by the quality of port infrastructure. In other words, the benefits of strong transport connectivity can only be fully realized when supported by efficient and high-quality port systems, while inadequate infrastructure significantly constrains logistics performance.

2.3 Infrastructure, logistics hubs, and efficiency of transport systems

Transport infrastructure is a fundamental determinant of logistics system efficiency, directly influencing connectivity, transit capacity, and economic performance. In this regard, Izteleuova et al. (2024) highlight that the development of transport and logistics infrastructure is closely linked to national economic growth and requires coordinated interaction between all transport modes and stakeholders. Efficient infrastructure enables the smooth functioning of logistics systems, reduces transportation costs, and enhances service reliability, while its deficiencies directly constrain system performance and limit economic potential.

At the macro level, infrastructure efficiency is strongly supported by international cooperation and investment flows. Kazakhstan actively collaborates with global institutions such as the European Union, United Nations, OSCE, NATO, and WTO, which contribute to strengthening transport connectivity and institutional integration. The European Union alone accounts for nearly 40% of Kazakhstan’s external trade, with total trade turnover reaching $41.9 billion in 2022. In addition, large-scale infrastructure investments further emphasize the strategic importance of transport development: projects supported by international financial institutions, including the European Bank for Reconstruction and Development, have reached billions of euros, while future investments under regional development programs are expected to exceed $10 billion. These figures demonstrate that infrastructure development is not only a national priority but also part of broader global economic integration processes.

The structure of transport systems also plays a critical role in determining logistics efficiency. In Kazakhstan, rail transport dominates freight flows, accounting for approximately 68% of total freight turnover, while road transport contributes around 20%, air transport 8%, and water transport 4%. This distribution reflects the strategic importance of rail-based corridors and highlights the need for multimodal integration to ensure flexibility, resilience, and efficiency of logistics systems. The dominance of a single transport mode, however, also indicates potential vulnerabilities and emphasizes the necessity of balanced infrastructure development across all transport sectors.

At the urban level, infrastructure efficiency is increasingly shaped by spatial organization and logistics distribution patterns. Mansurova and Jumaboev (2025) demonstrate that rapid urbanization in Astana has led to significant logistical challenges, including congestion, uneven infrastructure distribution, and limited accessibility in peripheral districts. The city’s population has already exceeded 1.5 million and is projected to reach 2.5 million by 2035, accompanied by a 67% increase in motorization levels. This rapid growth places additional pressure on transport infrastructure, intensifies traffic congestion, and reduces the efficiency of freight and passenger movement.

Empirical findings further confirm that spatial imbalances have a significant impact on logistics performance. Using advanced analytical approaches, Syzdykbayeva et al. (2025) reveal that transport and logistics infrastructure in urban areas is distributed unevenly, resulting in disparities in connectivity and accessibility between districts. Highly developed areas tend to experience congestion due to excessive load, whereas peripheral zones remain underdeveloped and insufficiently integrated into logistics networks. Notably, the results of optimization studies suggest that redistributing logistics facilities and relocating freight terminals to outer districts could reduce travel time by 18%–22%, thereby significantly improving system efficiency and reducing congestion levels. These findings emphasize the importance of balanced spatial planning and decentralized logistics infrastructure.

In complementing spatial factors, the development of logistics hubs and intermodal transport systems plays a pivotal role in enhancing infrastructure efficiency. Modern logistics systems increasingly rely on dry ports, multimodal terminals, and synchromodal transport solutions to facilitate the seamless integration of different transport modes. These systems strengthen the connection between inland regions and seaports, improve cargo handling efficiency, and optimize freight distribution. A study by Saha and Khalil (2025) shows that implementing dry ports and intermodal systems can significantly reduce transportation costs, transit time, and CO₂ emissions while increasing the flexibility and sustainability of logistics networks. These results confirm that logistics hubs are essential elements of efficient transport systems and key drivers of regional connectivity.

Bodaubayeva and Turkeeva (2023) further specify the role of logistics hubs in the context of Kazakhstan’s cross-border cooperation with Uzbekistan. Their study shows that the formation of industrial and logistics hubs in border regions can support the development of alternative Eurasian corridors, improve cargo processing, and strengthen regional trade connectivity. At the same time, the authors identify several barriers that limit the effectiveness of such hubs, including delays in customs procedures, inconsistencies in the legal regulation of transport processes, underdeveloped infrastructure, low levels of digitalization, weak information systems, and non-tariff barriers. They propose an integrated hub model consisting of industrial, logistics, trade, and certification zones, supported by a digital platform that links all functional components of the hub.

At the same time, the effectiveness of transport infrastructure is closely linked to institutional, technological, and human factors. Sumbal et al. (2024) identify significant performance gaps in logistics systems of developing economies, which are primarily associated with weak infrastructure, inefficient governance, limited adoption of information and communication technologies, and insufficient workforce qualifications. The study shows that even substantial investments in physical infrastructure may not lead to expected improvements without parallel development of digital systems, institutional frameworks, and human capital. This highlights the need for a comprehensive approach to logistics system development.

The importance of infrastructure quality is further reinforced by analyses based on global performance indicators. According to İnaç et al. (2025), key components of the Logistics Performance Index, particularly infrastructure, logistics service quality, and tracking systems, have a direct impact on the effectiveness of economic corridors and a country’s position in global logistics rankings. Improving these components enables more efficient trade flows, reduces delays and enhances overall supply chain performance. This strengthens the competitiveness of national economies.

The reviewed studies demonstrate that transport infrastructure, logistics hubs and system efficiency are interconnected elements that determine transport system performance. Large-scale investments, the development of multimodal logistics hubs, the spatial optimization of infrastructure and the integration of digital technologies all contribute to improving logistics efficiency and strengthening economic corridors. However, persistent challenges such as infrastructure bottlenecks, uneven regional development and institutional limitations indicate that further comprehensive and coordinated efforts are required. Addressing these issues is essential for enhancing the sustainability, resilience and global competitiveness of transport systems.

2.4 Methods and indicators for assessing logistics performance

The assessment of logistics performance has evolved into a multidimensional and integrated process, as modern logistics systems encompass not only transportation activities but also efficiency, service quality, sustainability, and digital transformation. In this context, accurate measurement becomes a fundamental prerequisite for effective analysis and decision-making.

One of the most widely used tools for evaluating logistics performance at the macro level is the Logistics Performance Index (LPI), which enables cross-country comparison of logistics capabilities. However, recent studies emphasize that relying solely on LPI is insufficient, and a combination of efficiency metrics, sustainability indicators, and digitalization measures is required (Ohakwe and Wu, 2025; Wang et al., 2025; Ye et al., 2025).

In the table below, the main groups of logistics performance indicators are presented, including traditional LPI dimensions and extended metrics related to efficiency, sustainability, and digitalization.

As illustrated in Table 1, the assessment of logistics performance extends beyond the traditional LPI indicators to incorporate operational, environmental, and technological dimensions.

TABLE 1. Key indicators for assessing logistics performance (LPI and extended metrics).

Traditional approaches to logistics performance measurement are often based on labor productivity, typically calculated as the ratio of value added to labor input. However, such approaches fail to capture the complexity of modern logistics systems, especially under conditions of globalization and digital transformation (Wang et al., 2025).

Therefore, contemporary research increasingly adopts multi-dimensional evaluation frameworks, integrating various performance indicators into a unified analytical system. Figure 1 depicts the systemic input-process-output logic of logistics performance assessment and its support for benchmarking across different entities.

Another important aspect of logistics performance evaluation is the use of comparative and analytical methods, such as DEA and MCDM models. For instance, the common weight H-DEA approach proposed by Yu and Rakshit (2025) provides a more objective evaluation of LPI indicators by integrating hierarchical decision-making structures. A combination of traditional and advanced analytical methods is required for logistics performance assessment, as outlined in Table 2.

TABLE 2. Methods for evaluating logistics performance.

In recent years, the importance of digital technologies in logistics has grown significantly. Intelligent logistics systems supported by artificial intelligence, big data, and the Internet of Things (IoT) contribute to improving operational efficiency and reducing uncertainties in supply chains (Ye et al., 2025; Habibullah, 2025).

Figure 2 summarizes the broader set of policy, economic, service-related, technological, sustainability and compliance factors influencing logistics performance.

Additionally, macroeconomic conditions and global disruptions play a significant role in shaping logistics performance. Studies indicate that factors such as economic stability, supply chain disruptions and post-pandemic recovery dynamics can have a direct impact on LPI outcomes (Ohakwe and Wu, 2025; Bayraktar et al., 2025; Mittal and Mittal, 2025).

In conclusion, logistics performance assessment is a complex, multi-layered process that integrates the following.

• - LPI indicators for macro-level evaluation
• - Efficiency metrics for operational analysis
• - Sustainability indicators for environmental assessment
• - Digital metrics for technological advancement

Only by combining these approaches can logistics systems be evaluated comprehensively and accurately, enabling evidence-based decision-making in policy and practice.

2.5 Multimodal logistics corridors in Eurasia and Kazakhstan: empirical studies

Recent research increasingly conceptualizes Eurasian multimodal transport corridors as integrated systems that enhance logistics efficiency, geopolitical connectivity, and economic integration simultaneously. In the context of Central Asia, for example, multimodal transport has been demonstrated to reduce lead times significantly and improve supply chain performance by optimizing route configurations and minimizing transshipment delays (Samieva, 2025). Supporting this, simulation-based evidence from Kazakhstan shows that the efficiency of multimodal transport flows depends heavily on the coordinated interaction of logistics nodes. Agent-based modeling reveals the critical role of digital coordination in optimizing transport networks and reducing systemic inefficiencies (Khussanov et al., 2025).

At a broader regional scale, the development of transcontinental transport corridors is closely linked to investment dynamics and international economic cooperation. Notably, the interaction between Kazakhstan and China in terms of investment plays a pivotal role in shaping Eurasian transport corridors, facilitating infrastructure expansion, and strengthening transcontinental connectivity (Yerimpasheva et al., 2026). Within this evolving framework, the Middle Corridor (Trans-Caspian International Transport Route) has emerged as a strategically important alternative route between Europe and Asia. This is driven by the need to diversify logistics pathways and mitigate geopolitical risks. Empirical findings suggest that this route provides faster delivery times and greater resilience in times of global instability, thus enhancing Central Asia’s transit potential (Nurseiit, 2026).

Kulyntay et al. (2025) extend this discussion by examining Central Asia’s transport and communication potential from the perspective of European Union interests. Their study emphasizes that the strategic importance of the Middle Corridor is linked not only to physical infrastructure development, but also to route diversification, customs simplification, harmonization of technical standards, and the creation of digitally integrated transport corridors. The authors argue that tools such as GPS tracking, RFID, blockchain-based platforms, and integrated digital transport systems can reduce delays, improve transparency, and strengthen the competitiveness of Central Asian transit routes. This perspective is particularly relevant for Kazakhstan, whose role in Eurasian connectivity depends on the combined modernization of hard infrastructure and soft institutional mechanisms.

The strategic role of multimodal corridors must also be assessed in terms of resource logistics and structural dependencies. Although Kazakhstan occupies a pivotal position in Eurasian energy logistics, particularly in supplying oil to the European Union, a substantial proportion of export flows still depend on traditional transit routes. Alternative multimodal pathways, including those associated with the Middle Corridor, continue to face infrastructural and economic constraints, which limit their capacity to fully replace established systems in the short term (Akhmedov et al., 2026). This suggests that, for now, multimodal corridors perform a diversification rather than a replacement function within the regional logistics system.

Concurrently, the integration of digitalization and globalization processes is transforming the functional role of transport corridors. Digital technologies, such as electronic customs systems, real-time tracking, and data-driven logistics platforms, significantly enhance operational efficiency by reducing transaction costs, accelerating border procedures, and increasing transparency in supply chains. Empirical evidence from Turkic states shows that developing digitally integrated trade corridors not only optimizes logistics but also increases investment attractiveness and entrepreneurial activity. This reinforces the role of corridors as catalysts of regional economic integration (Amanova et al., 2025).

The reviewed literature indicates that multimodal transport corridors in Eurasia are evolving into complex, multi-layered systems integrating infrastructural, economic, technological, and geopolitical dimensions. These systems’ effectiveness is determined not only by physical connectivity but also by the level of digital integration, institutional coordination and international cooperation. Despite existing constraints relating to infrastructure capacity, regional imbalances, and dependency structures, these corridors play a vital part in improving logistics resilience, supporting trade flows, and strengthening Kazakhstan’s and Central Asia’s strategic position within the global transport network.

2.6 Research gaps and conceptual limitations in the existing literature

Despite the growing body of research on multimodal transport systems and logistics performance, the existing literature remains fragmented. LPI-based studies mainly focus on macro-level logistics indicators such as customs efficiency, infrastructure quality, logistics competence, tracking and tracing, and timeliness. Corridor-oriented studies examine transport routes, logistics hubs, dry ports, intermodal nodes and Eurasian connectivity, including the Middle Corridor. Digital logistics studies emphasize platforms, real-time tracking, IoT, RFID, intelligent transport systems and electronic data exchange. These research streams provide important insights, but they usually analyse logistics performance, corridor infrastructure and digital transformation separately rather than as interconnected components of multimodal logistics systems.

This fragmentation is particularly visible in studies on Kazakhstan and Central Asia. Imasheva et al. (2026) examine the role of digital technologies in Kazakhstan’s transport logistics, including digital platforms, IoT, RFID, intelligent transport systems and unmanned technologies. Tazhiyev et al. (2024) focus on digital transport and logistics ecosystems in truck freight management, emphasizing integrated platforms, digital corridors, real-time tracking and electronic data exchange. Liu et al. (2025) analyse logistics modernization in Central Asia and highlight infrastructure coordination, digital transformation, customs harmonization and regional policy alignment. However, these studies mainly address separate dimensions of logistics development and do not fully connect macro-level indicators, corridor-level route efficiency, company-level operational evidence and risk assessment within one analytical framework.

A further limitation concerns the weak integration of company-level operational constraints into corridor-level analysis. Existing studies often discuss infrastructure, digitalization and corridor connectivity at the national or regional level, while issues such as company-level coordination, document flow, information exchange, service quality and operational risk receive less attention. As a result, the relationship between national logistics performance, multimodal corridor configuration and internal logistics operations remains insufficiently explained.

The present study addresses this gap by combining LPI-based comparative analysis, longitudinal assessment, route modelling, a case study of Globalink Logistics, questionnaire-based operational diagnostics and FMEA-based risk assessment within a unified framework. This approach links macro-level logistics performance, corridor-level route efficiency, company-level operational bottlenecks and risk factors, thereby providing a more integrated understanding of multimodal logistics systems in Kazakhstan and wider Eurasian transport corridors.

3 Materials and methods

3.1 Data sources and indicators

Based on the 2023 United Nations Convention to Combat Desertification, 2023, Central Asia comprises the following five countries: Kazakhstan, Uzbekistan, Kyrgyzstan, Tajikistan and Turkmenistan.

For the purposes of this study, these countries are considered the core regional group. China and Turkey, however, are included as external comparative actors due to their strategic involvement in Eurasian multimodal transport corridors. The table below presents the Logistics Performance Index (LPI) and its key components for selected Central Asian countries, as well as China and Turkey for comparative analysis.

Table 3 shows that China demonstrates the strongest logistics performance across most indicators, particularly infrastructure and logistics competence. Kazakhstan occupies an intermediate position among Central Asian countries, outperforming several regional peers but lagging behind major global players. These differences highlight the region’s structural disparities in logistics development.

TABLE 3. Logistics performance indicators (LPI) for selected countries (2023).

Source: World Bank (2023), Logistics Performance Index (LPI) database. Retrieved from https://lpi.worldbank.org.

The LPI is a composite index based on six key dimensions, including customs efficiency, infrastructure quality, international shipments, logistics competence, tracking and tracing, and timeliness.

Although the LPI provides a standardized basis for cross-country comparison, it also has several methodological limitations. First, the index is partly perception-based and reflects the assessments of logistics professionals rather than only objective operational data. Second, cross-country comparability may be affected by differences in market size, institutional context, infrastructure density, and the structure of international trade. For this reason, the LPI is used in this study as a macro-level diagnostic and benchmarking tool, not as the sole measure of corridor performance. Its results are interpreted together with case-study evidence, questionnaire findings, and route modelling.

The spatial distribution of logistics performance across Central Asia and selected comparator countries reveals significant disparities in LPI rankings, highlighting the uneven development of transport and logistics systems in the region.

As illustrated in Figure 3, China exhibits the most robust logistics performance among the selected countries, ranking 19th globally. Turkey follows closely behind in 38th place. Among the Central Asian countries, Kazakhstan occupies a relatively high position (79th), outperforming Uzbekistan (88th) and Tajikistan (97th). Kyrgyzstan shows the weakest performance, ranking 123rd. These differences highlight significant disparities in logistics system efficiency, infrastructure development and institutional capacity across the region.

3.2 Research design

To ensure a systematic and transparent research process, this study integrates quantitative data analysis with comparative and visualization-based approaches. The analysis is based on Logistics Performance Index (LPI) data obtained from the World Bank, focusing on selected Central Asian countries, with China and Turkey included as benchmark cases.

China was not treated as a symmetric comparator for Kazakhstan. Rather, it was included as a benchmark case because it represents a leading logistics economy and plays a central role in Eurasian transport corridors and China–Europe freight flows. The comparison with China was therefore used to identify the scale of the performance gap and to benchmark structural dimensions of logistics performance, rather than to infer equivalence in market size, infrastructure density, or logistics maturity. Turkey was included as an additional benchmark case, while Uzbekistan, the Kyrgyz Republic, and Tajikistan were used to provide a regional Central Asian comparison.

The study examines key logistics indicators, including customs efficiency, infrastructure quality, international shipments, logistics competence, tracking and tracing, and timeliness, in order to evaluate differences in logistics performance across countries. A combination of comparative analysis, benchmarking, and visualization techniques (maps, radar charts, and longitudinal analysis) is applied to identify spatial and structural disparities in logistics systems.

In addition, a case study approach is employed to illustrate the real-world application of logistics processes through the example of Globalink Logistics, a multimodal transport operator in Eurasia.

The mixed-method design was selected to connect macro-level logistics performance with corridor-level and company-level evidence. The LPI-based comparison identifies national and regional performance gaps, while spatial visualization and radar charts show structural differences across logistics indicators. The Globalink Logistics case study illustrates how these macro-level constraints are reflected in the practical operation of a Eurasian multimodal transport operator. Route modelling and questionnaire-based diagnostics further support the interpretation by linking corridor efficiency, operational bottlenecks and company-level coordination problems. Figure 4 presents the overall logic of the study, including its data sources, analytical steps and case-study extension.

3.3 Methods of analysis

The analytical part of the study was conducted in three stages. First, a cross-country comparative analysis was conducted using the overall Logistics Performance Index (LPI) score and its six component indicators: customs efficiency, infrastructure quality, international shipments, logistics competence, tracking and tracing, and timeliness. This identified Kazakhstan’s relative position compared with selected Central Asian countries, as well as benchmark countries such as China and Turkey.

Secondly, radar charts were employed as a multidimensional visualization tool to compare the internal structure of logistics performance across countries. This approach enabled the simultaneous evaluation of the strengths and weaknesses of all six LPI dimensions.

Thirdly, a longitudinal analysis was conducted for Kazakhstan and China between 2007 and 2023 to examine changes in logistics performance over time. This step aimed to reveal the dynamics of infrastructure development, customs efficiency, logistics competence and other LPI-related dimensions over time.

To complement the macro-level comparison, the study employed a case-study approach based on Globalink Logistics. This company-level extension was used to illustrate the practical operation of multimodal logistics systems, and was supplemented by route modelling, a questionnaire-based assessment and operational diagnostics.

3.4 Case study approach

3.4.1 Case study description

To complement the quantitative analysis, a case study approach was applied using Globalink Logistics as an illustrative case of a multimodal logistics operator active in Kazakhstan and wider Eurasian transport corridors. The company was selected on the basis of four criteria: direct involvement in multimodal transport operations; relevance to Eurasian corridor-based freight flows; broad operational scope covering freight forwarding, customs clearance, warehousing and cargo management; and the availability of company-level diagnostic data for questionnaire-based assessment. The case study was not intended to represent the entire logistics sector of Kazakhstan. Rather, it was used to illustrate how multimodal logistics processes operate at the firm level and how company-level practices are connected with broader corridor performance. Therefore, the findings from this single case are interpreted as illustrative evidence and are not generalized to all logistics operators in Kazakhstan.

Globalink Logistics operates an extensive international network, with offices located in numerous countries across Europe, Asia, and the Middle East, including Kazakhstan and other Central Asian states. According to the company’s official website (Globalink Logistics, 2023), it provides a wide range of logistics services, including road, rail, air, and sea transportation, as well as warehousing, customs clearance, project logistics, and cargo management.

The company actively applies multimodal logistics solutions by integrating different transport modes (rail-road, sea-rail, air-road) into unified supply chains. This allows for the optimization of delivery routes in terms of cost, time, and efficiency, particularly along major Eurasian transport corridors.

Within Kazakhstan, Globalink Logistics plays a key role in facilitating transit flows between China, Central Asia, and Europe, contributing to the country’s position as a strategic logistics hub within the Belt and Road Initiative. The spatial scope of the company’s logistics network is illustrated in Figure 5.

As illustrated in Figure 5, Globalink Logistics maintains a wide and interconnected transport network covering key regions of Europe, Asia, and the Middle East. The company’s routes align with major international transport corridors, including transcontinental rail and road connections linking China with Europe through Central Asia.

The presence of multiple offices and established logistics routes enables efficient coordination of multimodal transport operations. This network structure supports flexible route planning, reduces transit times, and enhances supply chain reliability.

The case of Globalink Logistics demonstrates that the effectiveness of multimodal logistics systems depends not only on infrastructure availability but also on the level of integration between transport modes and the operational capacity of logistics providers. This confirms the importance of institutional and organizational factors in shaping logistics performance at both national and regional levels. Figures 6, 7 illustrate the operational structure of Globalink Logistics’ air, sea, road and rail freight networks, providing further examples of the company’s multimodal logistics approach. This network reflects the company’s hub-based approach to international cargo transportation, with key logistics centers coordinating flows across different regions.

It is organized around several major global hubs, including Frankfurt (Europe), Dubai (the Middle East), Hong Kong (the Asia-Pacific region), Baku (the Caucasus region) and Almaty (Central Asia). These hubs serve as strategic nodes that enable the consolidation, redistribution and routing of cargo across intercontinental corridors.

Almaty plays a particularly important role in this system as a regional logistics hub, connecting Central Asia with Europe, the Middle East, and East Asia. This network structure enables the efficient integration of air transport into multimodal logistics chains, thereby enhancing connectivity and reducing transit times.

Figures 6, 7 illustrate two complementary aspects of Globalink Logistics’ multimodal network in Eurasia. Figure 6 illustrates the hub-based air freight structure, while Figure 7 presents the integrated sea, road and rail freight network. Together, they demonstrate the company’s operational flexibility and ability to combine different transport modes within a unified logistics system.

The map highlights several key logistics hubs, including Hamburg, Istanbul, Dubai, Karachi, and major Chinese ports. These hubs serve as central nodes for cargo distribution. These hubs are interconnected through various transport modes, enabling flexible routing options based on cost, distance, and delivery time requirements.

Central Asia, particularly Kazakhstan, occupies a strategically important position within this network. Cities such as Almaty, Astana, and Aktau act as transit points, linking China with Europe, the Middle East, and South Asia. The presence of multiple corridor options through the region enhances redundancy and resilience in supply chains.

Integrating sea, rail, and road transport enables efficient multimodal logistics operations, reducing dependency on a single transport mode and improving supply chain performance overall. This demonstrates that Kazakhstan’s role in Eurasian logistics is both geographical and functional, as it facilitates the coordination of complex intermodal transport flows.

3.4.2 Model formulation for multimodal transport optimization

To formalize the process of selecting an optimal multimodal transport route between origin A and destination B, a mathematical model based on probabilistic and heuristic optimization is applied.

The model was applied as a scenario-based analytical framework for comparing alternative multimodal route configurations under predefined cost, time and network assumptions. Its objective was to identify the most efficient route under the specified parameters rather than to predict real-time corridor performance. The model is based on the assumption that route efficiency depends on the interaction of transport mode, transport cost, transportation time, node connectivity, and route attractiveness. The main input variables include logistics nodes, possible links between nodes, available transport modes, transport cost or time parameters, heuristic route attractiveness, and weighting coefficients. The main output of the model is the selection of the most efficient multimodal route configuration between the origin and destination. Since the model was not calibrated using real-time operational data, its validation is limited to scenario comparison and internal consistency of the obtained results. Therefore, the modelling results should be interpreted as analytical indications of route efficiency within the specified scenarios rather than exact forecasts of logistics performance.

To improve the transparency of the scenario-based route modelling procedure, Table 4 summarizes the source basis and justification of the main input parameters used in the model.

TABLE 4. Evidence basis of the main route modelling inputs.

The inputs are used for comparative scenario modelling and are not interpreted as real-time tariffs, confidential company data or exact delivery forecasts.

The model considers a transport network consisting of logistics nodes and possible routes between them.

Objective formulation. The probability of selecting a transport path between nodes i and j is defined as:where: K i j t– probability of selecting route from node i to node j at time t;

K i j t = μ i j ɑ t × ŋ i j β ∑ k ϵ a l l o w e d μ i k a t × ŋ i k β

μ i j ɑ t– intensity of accumulated system experience (analogous to “pheromone”); ŋ i j β– heuristic attractiveness of the route (inverse of transport cost or time); ɑ, β–weighting coefficients of experience and heuristic factors.

Pheromone update equation. The update of route attractiveness over time is described as:

μ i j t + n = 1 − ρ μ i j t + Δ μ i j t , t + n

Additional update component:where: ρ–evaporation coefficient (0 < ρ < 1); Δ μ i j– increment reflecting route efficiency; m–number of evaluated transport alternatives.

Δ μ i j t , t + n = ∑ k = 1 m Δ μ i j k t , t + n

Interpretation for logistics system. The model allows.

• - evaluation of alternative multimodal routes between A and B;
• - consideration of transport cost, time, and network structure;
• - identification of optimal logistics paths through iterative improvement.

The obtained solution represents the most efficient route in terms of integrated logistics performance.

To ensure the replicability of the route modelling procedure, the model was applied in three consecutive steps. First, alternative multimodal routes were identified and described according to their transport mode composition, distance, delivery time and cost parameters. Second, the main route-specific variables were entered into the model, including transport cost, delivery time, network structure and route efficiency coefficients. Third, the obtained route alternatives were compared in terms of integrated logistics performance, where lower cost, shorter delivery time and fewer operational constraints indicated a more efficient route configuration. The modelling results were then interpreted together with the survey findings and FMEA-based risk assessment.

3.4.3 Questionnaire design, survey procedure and respondent profile

In January 2026, a questionnaire survey was conducted among company employees to strengthen the case study of Globalink Logistics. The survey aimed to gather company-level evidence on the internal functioning of the logistics system, including the efficiency of logistics processes, coordination between operational units, managerial support, digitalization, service quality and practical improvement suggestions.

A total of 79 employees participated in the survey (Table 5). The questionnaire included both closed and open questions. The closed-ended section was designed to provide structured evaluations of important operational aspects, while the open-ended section aimed to gather practical suggestions for process optimization, coordination, technology utilization, and enhancing competitiveness.

TABLE 5. Selected background characteristics of questionnaire respondents in Globalink Logistics (%, n = 79).

Based on the authors’ questionnaire survey conducted among Globalink Logistics employees in January 2026.

The survey was conducted as an exploratory company-level diagnostic rather than as a statistically representative survey of the entire logistics sector. Respondents were selected according to their functional relevance to logistics operations, including employees from logistics, warehousing, customs clearance, and other related departments. The sample size of 79 respondents was considered appropriate for the purpose of internal operational diagnostics, as the survey was intended to identify perceived strengths, bottlenecks, and improvement priorities within the selected case company.

The closed-ended responses were processed using descriptive statistical methods. Response frequencies and percentage shares were calculated based on the total sample of 79 respondents. The results were used to identify the most frequently reported operational constraints and improvement priorities within the selected case company. As a basic robustness check, selected responses were compared by department and work experience to determine whether the main patterns were consistent across respondent groups. The open-ended responses were coded thematically to identify recurring recommendations related to digitalization, coordination, technical resources, process control and competitiveness. No inferential statistical tests were applied because the survey was not designed to test population-level hypotheses, but to provide supplementary company-level diagnostic evidence within the case-study framework.

The respondent profile shows that the survey mainly reflects the views of employees directly involved in logistics operations. The majority of respondents were from the logistics department, with smaller proportions representing warehousing, customs clearance and other units. In terms of work experience, the sample was dominated by relatively inexperienced employees, although those with more experience were also represented.

The questionnaire was used as a supplementary diagnostic tool within the broader case-study design, rather than as an independent survey. Its purpose was to provide internal, company-level evidence for the subsequent analysis of operational strengths, bottlenecks and organizational factors affecting multimodal logistics performance.

As indicated in Table 5, the respondent structure was concentrated in logistics-related functions, which increases the relevance of the collected evidence for analyzing multimodal operations at the firm level.

4 Results and discussion

4.1 Cross-country and temporal analysis of logistics performance

To further explore the temporal evolution of logistics performance, a longitudinal analysis of LPI indicators for Kazakhstan and China was conducted over the period 2007–2023. This analysis enables dynamic trends in key logistics dimensions, such as infrastructure development, customs efficiency, logistics competence, and timeliness, to be identified. Examining changes over time enables us to assess the current performance gap between the two countries and the trajectory of their logistics system development. This approach provides deeper insights into structural improvements and persistent constraints within Kazakhstan’s logistics sector compared to China’s.

Figure 8 indicates that China outperforms Kazakhstan across all six LPI components in 2023. The most visible differences are observed in infrastructure, logistics competence, and tracking and tracing, whereas Kazakhstan demonstrates a more moderate and relatively balanced profile. This indicates that the performance gap between the two countries is structural rather than limited to the overall LPI score.

Figure 9 provides a temporal analysis that reveals distinct trajectories in the development of logistics performance in Kazakhstan and China between 2007 and 2023. Kazakhstan’s LPI scores demonstrate significant fluctuations, reflecting periods of rapid improvement followed by decline. For example, the country’s overall LPI score increased sharply from 2.12 in 2007 to 2.83 in 2010, indicating initial progress in developing the logistics system, particularly with regard to international shipments and logistics competence. However, this positive trend was followed by a slight decline in 2012, suggesting structural instability and uneven reform implementation.

A more stable growth pattern emerged after 2014, driven by gradual improvements in infrastructure and tracking systems, resulting in moderate performance increases. Nevertheless, certain components, such as customs efficiency and timeliness, continue to exhibit volatility, indicating persistent bottlenecks in border procedures and service coordination.

In contrast, China demonstrates a consistent upward trend across all LPI dimensions. From 2007 to 2023, China’s LPI score increased steadily from 3.32 to 3.70, with particularly strong performance in infrastructure development and logistics competence. Unlike Kazakhstan, China shows minimal fluctuations, reflecting a more stable institutional framework and continuous investment in logistics infrastructure.

Overall, these comparative dynamics highlight diverging development paths: while Kazakhstan exhibits episodic improvements with noticeable variability, China maintains sustained and systematic growth. These findings suggest that long-term policy consistency, infrastructure investment, and institutional capacity play a decisive role in shaping logistics performance trajectories.

To further explore structural differences in logistics performance across the region, a comparative radar analysis was conducted between Kazakhstan and selected countries, including China, Uzbekistan, Kyrgyz Republic, Tajikistan, and Turkey. This approach allows for a multidimensional assessment of logistics systems by examining disparities across all six LPI components. The results are presented in Figure 10.

As illustrated in Figure 10, there are significant differences in the structure of logistics performance across the selected countries. China and Turkey demonstrate consistently higher values across all LPI components, particularly in terms of infrastructure, logistics competence and tracking and tracing. This reflects their more advanced and integrated logistics systems.

In contrast, Kazakhstan demonstrates a relatively balanced performance across most indicators, but lags behind the leading countries in terms of customs efficiency and international shipments. Compared to its Central Asian neighbors, Kazakhstan outperforms the Kyrgyz Republic and Tajikistan in most areas, indicating a relatively stronger logistics system within the region.

Uzbekistan exhibits a performance comparable to that of Kazakhstan in several indicators, but demonstrates slightly lower scores in terms of logistics competence and tracking systems. Meanwhile, the Kyrgyz Republic and Tajikistan display weaker performance across nearly all components, particularly in infrastructure and logistics competence, highlighting structural limitations in their logistics sectors.

Overall, the comparative radar analysis confirms substantial structural disparities between countries. While Kazakhstan holds a leading position within Central Asia, it still faces a significant gap compared to major logistics hubs such as China and Turkey.

4.2 Results of multimodal route modelling and sensitivity analysis

Empirical application of the proposed modelling framework indicates that the efficiency of multimodal transport depends on the balance between transport mode shares, route configuration, and node connectivity. The results indicate that integrating multiple transport modes creates more opportunities to reduce delivery constraints than relying on a single-mode route structure. In particular, cost behavior varies substantially across scenarios involving direct transportation, waterway hubs, and railway hubs, which confirms the importance of flexible route design in multimodal logistics systems. In addition to cost sensitivity analysis, the spatial distribution of logistics nodes is critical in determining feasible routing structures. The positioning of nodes A and B within the transport network directly influences route selection, intermodal transitions, and the overall efficiency of cargo flows.

Figures 11, 12 present two complementary outputs of the route modelling procedure. Figure 11 shows the sensitivity analysis of transport mode shares under waterway and railway cost scenarios, while Figure 12 illustrates the spatial distribution of logistics nodes A and B within the modelled transport network.

As Figure 11 indicates, cost dynamics differ markedly depending on the route’s modal structure. Under the waterway cost scenario, the share of operations routed through waterway hubs increases progressively, while the contribution of railway hubs declines. Direct transportation shows moderate fluctuations, suggesting that it remains relevant only under specific parameter combinations rather than as a universal solution.

In contrast, under the railway cost scenario, railway hubs play a more stable role in most cases, whereas direct transportation and waterway hub participation remain lower and more variable. These results suggest that railway-based multimodal combinations provide greater structural stability within the model network.

Overall, the graphical results indicate that multimodal efficiency is not associated with one universally dominant transport mode. Instead, the optimal configuration depends on the interaction between modal cost coefficients and the distribution of transport shares. This confirms that route optimization in multimodal systems should be based on comparative scenario evaluation rather than on a fixed transport preference.

To complement the graphical analysis, the model incorporates transportation time parameters specific to each route between logistics nodes. These values enable the temporal performance of alternative modal links to be compared and the route segments that constrain the efficiency of the overall logistics chain to be identified.

Table 6 summarizes the transportation time parameters for the main links in the model network.

TABLE 6. Transportation time parameters between logistics nodes by transport mode.

Values represent relative time parameters specified by the authors for scenario-based comparison within the route modelling framework.

The time parameters shown in Table 6 demonstrate significant differences between transport modes. Road transport offers the greatest flexibility for short-distance connections, with comparatively low journey times on routes O-A3 and A3-A4. Railway transport, by contrast, exhibits more balanced performance across the network, particularly on intermediate links, providing moderate travel times combined with route continuity. By contrast, water transport is characterized by the highest temporal cost, particularly on the O-A1 and A1-A2 links. This confirms its lower speed, despite its potential value in large-volume multimodal operations.

These results suggest that the temporal structure of the network favors the selective rather than uniform use of transport modes. Fast road segments remain advantageous for terminal and connecting sections, whereas railway links are the main stabilizing component of the logistics system. Water transport is only rational where its inclusion is justified by broader route optimization rather than by speed considerations alone.

Based on the model constraints and calculated parameter combinations, the optimal route was identified as O-A3-A4-D: a multimodal chain with a railway–waterway configuration. This result confirms that the most efficient route is not necessarily the shortest in purely spatial terms, but rather the one that strikes the best balance between cost and time under multimodal conditions. Therefore, these results demonstrate that optimizing routes in Eurasian logistics corridors requires the coordinated use of transport modes, node functions and time parameters rather than the isolated evaluation of individual route segments.

4.3 Company-level questionnaire findings and operational diagnostics

In January 2026, a questionnaire-based assessment was conducted among company employees to strengthen the case study of Globalink Logistics. The survey aimed to identify the company’s internal strengths and weaknesses, evaluate the quality of its work processes, gauge employee satisfaction with organizational conditions, and gather practical suggestions for enhancing company performance. A total of 79 employees participated in the survey. The questionnaire included both closed and open questions, enabling a combination of percentage-based evaluation and qualitative interpretation.

The results of the questionnaire were incorporated into the case study as supplementary company-level diagnostic evidence. The survey results were interpreted using descriptive statistical logic, with emphasis on percentage distributions across grouped response categories and the identification of the most frequently reported operational constraints. Given the exploratory and company-level character of the questionnaire, these results are used as diagnostic evidence rather than as a basis for population-level hypothesis testing. Unlike the macro-level LPI analysis, the survey revealed how logistics performance is perceived within the company, particularly with regard to process efficiency, coordination, managerial support, digitalization and customer-oriented service quality. For the purposes of this article, the closed-ended responses are presented in condensed form as a grouped bar chart, while the open-ended answers are summarized as key insights from respondents.

The figure below summarizes the main results of the employee questionnaire conducted at Globalink Logistics. To avoid excessive fragmentation of the case-study evidence, several related operational dimensions have been combined into one grouped bar chart. The figure shows the percentage distribution of responses relating to the efficiency of logistics processes, coordination between warehousing and transportation, managerial support and feedback, digitalization and automation, and the perceived impact of service quality on customer satisfaction.

As shown in Figure 13, the overall internal assessment of Globalink Logistics is generally positive, although the responses also identify several areas where operational improvement is still needed. Logistics procedures were predominantly evaluated as efficient: 36.4% of respondents rated these procedures as strongly positive, 54.5% as positive or sufficient, and 9.1% as moderate. No respondents selected a low or weak assessment. These results suggest that the company’s core logistics processes, including route planning, cargo monitoring and document flow, are perceived as functioning at a stable level.

At the same time, coordination between warehousing and transportation was identified as the area with the lowest level of efficiency. Only 27.3% of respondents gave a strongly positive assessment of this area, while 18.2% evaluated it as positive or sufficient. The largest proportion, 54.5%, rated coordination as moderate, with no low ratings recorded. This distribution indicates that interdepartmental synchronization remains one of the key internal constraints affecting the smooth performance of multimodal logistics operations.

Managerial support and feedback were assessed more favorably. According to the responses, 36.4% of employees rated this dimension as strongly positive, 45.4% as positive or sufficient, and 18.2% as moderate. Once again, no respondents selected a negative assessment. These findings suggest that, while the company’s management and communication environment is generally stable, a significant proportion of employees still perceive room for improvement in internal support and feedback mechanisms.

The assessment of digitalization and automation also indicates an overall positive pattern, although some limitations remain. A total of 45.5% of respondents described the current level as “strongly positive”, 45.4% as “positive” or “sufficient”, and 9.1% as “low” or “weak”. No respondents selected the “moderate” category. This suggests that, while the company already has a well-developed digital and automation base, a small proportion of employees still regard the level of technological support as insufficient for fully efficient logistics operations.

The strongest result concerns the perceived impact of service quality on customer satisfaction. Overall, 63.6% of respondents gave a strongly positive assessment, 27.3% gave a positive or sufficient assessment, and 9.1% gave a moderate assessment. No weak assessments were reported. This confirms that employees clearly associate internal service quality with external customer outcomes. Therefore, improvements in operational quality are likely to strengthen customer retention and the firm’s competitive position.

The questionnaire findings indicate that Globalink Logistics exhibits notable proficiency in logistics procedures, managerial support, digitalization, and customer-oriented service quality. However, coordination between warehousing and transportation was found to be comparatively weaker than the other assessed dimensions, identifying it as the area in which further operational improvements are most needed.

The responses were also reviewed by department and work experience to determine whether the main diagnostic patterns were consistent across respondent groups. The main pattern remained unchanged: logistics procedures and service quality received stronger assessments, while coordination between warehousing and transportation remained the weakest operational dimension. More experienced respondents placed greater emphasis on coordination, document flow and information exchange problems. This supports the use of descriptive analysis as a suitable diagnostic approach for identifying company-level operational bottlenecks.

Table 7 presents a descriptive subgroup review by department and work experience, showing the consistency of the main diagnostic patterns across respondent categories.

TABLE 7. Descriptive subgroup patterns in questionnaire responses.

The table summarizes descriptive patterns from the subgroup review of questionnaire responses by respondent profile. It is not intended as inferential statistical testing.

The subgroup comparison confirms that coordination, document flow and information exchange remain the main operational bottlenecks across respondent categories, with these issues being more pronounced among warehouse-related and more experienced respondents.

In addition to the results of the closed-ended survey, employees provided open-ended recommendations on how to improve work quality, modernize logistics processes and enhance the company’s competitive position. For the purposes of this article, these responses have been synthesized into a concise set of insights reflecting the themes that were mentioned most frequently (Table 8).

TABLE 8. Key respondent insights on improving the operational and competitive performance of Globalink Logistics.

Based on the authors’ synthesis of open-ended questionnaire responses collected from Globalink Logistics employees in January 2026.

The synthesized open-ended responses indicate that employees view the company’s future development from internal and external perspectives. Internally, the most frequently mentioned priorities are deeper digitalization, the automation of logistics procedures, the improvement of technical resources and stronger process control. Externally, respondents emphasize the need to increase marketing activity, attract new partners and strengthen the company’s competitive position in the market.

These responses are important because they take the case study beyond a purely descriptive company profile. They demonstrate that improving multimodal logistics performance depends not only on infrastructure and route availability, but also on digital maturity, cross-departmental coordination and organizational management. In this respect, the respondent insights align with the article’s broader argument that the competitiveness of multimodal logistics systems is influenced by factors at both the corridor and firm levels.

The company’s internal performance was further assessed using an operational efficiency coefficient. The coefficient is based on five key indicators relevant to logistics operations: delivery timeliness, service quality, customer satisfaction, document accuracy, and personnel efficiency. Each indicator was assessed on a 10-point scale, after which the arithmetic mean was used to calculate the overall operational efficiency coefficient.where: K c o = operational efficiency coefficient; K i = assigned to each indicator; n = number of indicators (Table 9).

K c o = ∑ K i n

TABLE 9. Internal indicators used for calculating the operational efficiency coefficient.

Scores are diagnostic estimates based on the case-study profile, questionnaire evidence and FMEA-based risk assessment; they are not audited KPI, values.

Based on the selected indicators, the operational efficiency coefficient is calculated as follows:

K c o = 8 + 7 + 8 + 6 + 7 5 = 7.2

If the maximum possible value is 10, the operational efficiency level is calculated as follows:

K c o = 7.2 10 × 100 = 72 %

The results obtained show that Globalink Logistics’ operational efficiency can be assessed as above average, reaching 72% of the maximum possible benchmark. This suggests that, while the company’s logistics system is stable and functional, it has not yet been fully optimized. The internal evaluation revealed that document accuracy is the weakest element, suggesting that administrative procedures and information-processing routines are less efficient than transport execution and customer-oriented service functions. This finding is consistent with the survey results, which also highlighted the need for stronger automation and improved coordination of internal processes.

To identify the main risks affecting the company’s logistics operations, the Failure Mode and Effects Analysis (FMEA) method was applied. This method allows operational risks to be evaluated through three parameters: severity of consequences, occurrence probability, and detection difficulty. In the present case study, the analysis focuses on four typical logistics risks: delivery delays, document errors, cargo damage, and delays in information exchange (Table 10).

TABLE 10. FMEA-based risk assessment of key operational risks in Globalink Logistics.

Scores were assigned by the authors within the FMEA-based risk assessment. Severity (S) reflects the potential operational consequences of each risk, occurrence (O) reflects its perceived relevance based on the case-study interpretation and questionnaire findings, and detection difficulty (D) reflects how difficult the problem is to identify before it affects logistics operations. The scores are used as structured expert judgement for diagnostic purposes, not as statistically estimated probabilities.

Each risk is evaluated according to the following parameters.

• S = severity of consequences (1–10)
• O = occurrence probability (1–10)
• D = detection difficulty (1–10)

The Risk Priority Number is calculated as:

R P N = S × O × D

The highest-risk failure mode is delivery delay, with an RPN of 240.

The theoretical maximum possible value is:

R P N max = 10 × 10 × 10 = 1000

A simplified process reliability index may therefore be expressed as:

K r e l = 1 − 240 1000 = 0.76

Thus, the estimated process reliability level is 76%.

The FMEA results suggest that delivery delays are the most significant risk to the company’s logistics system, with delays in information exchange being the next most significant risk. These two risks are particularly important because they directly impact the reliability of multimodal coordination and the predictability of freight movement. Although cargo damage has a lower overall RPN value, its severity remains high, meaning that even infrequent incidents could generate significant operational losses. Document errors also pose a significant risk, particularly in relation to customs procedures, cargo tracking and interdepartmental communication.

Analytically, the FMEA results confirm that Globalink Logistics’ operational vulnerabilities are connected not only with physical cargo movement, but also with process synchronization and information management. This supports the view that improving multimodal logistics performance requires technical modernization and stronger internal coordination mechanisms.

The case study of Globalink Logistics is supported by a more empirical basis, including the questionnaire-based assessment, respondent insights, the operational efficiency coefficient and FMEA-based risk assessment. The results indicate that the company has a stable operational structure, positive employee evaluations of logistics efficiency and a strong perceived link between service quality and customer satisfaction. However, the evidence also reveals important constraints relating to warehouse–transport coordination, document accuracy, and information exchange. These findings show that the case study is not limited to a descriptive company profile, but provides performance-based evidence through the combined use of questionnaire results, operational efficiency assessment and FMEA-based risk assessment.

4.4 Discussion

The empirical findings of this study are interpreted through an integrated analytical framework that connects four levels of multimodal logistics performance. The LPI-based comparison identifies Kazakhstan’s macro-level position within the Eurasian logistics system and reveals performance gaps in infrastructure, customs efficiency, logistics competence, tracking and timeliness. The route modelling results explain corridor-level efficiency through transport mode configuration, logistics nodes, cost and time parameters. The company-level questionnaire diagnostics identify internal operational bottlenecks related to coordination, digitalization, document flow and service quality. The FMEA-based risk assessment complements these results by identifying the operational risks that may reduce the reliability of multimodal logistics processes.

The main contribution of the study lies in linking LPI analysis, route modelling, company-level diagnostics and FMEA-based risk assessment within a single research framework. This differs from studies that examine logistics performance, transport corridors, digitalization or operational risks as separate research objects. The integrated interpretation shows that multimodal corridor effectiveness is shaped not only by national logistics indicators and physical infrastructure, but also by company-level coordination, information exchange and risk management.

Kazakhstan occupies an intermediate position in the Eurasian logistics system. Its performance is relatively stronger than that of several Central Asian countries. At the same time, the comparison with China and Turkey reveals a substantial gap in infrastructure quality, logistics competence, tracking systems and customs efficiency. This supports the argument of Augan and Alipbayev (2023) that the competitiveness of Kazakhstan’s transport and logistics system depends not only on geographical location, but also on the degree of integration into international transport corridors. In the same vein, the present results extend the conclusions of Tashenova et al. (2022) and Zhiltsov (2024), showing that the growth of transit importance does not automatically imply the formation of a fully competitive logistics system.

The temporal comparison further highlights an important structural difference between Kazakhstan and the stronger benchmark cases. While China demonstrates relatively stable and consistent progress across logistics indicators, Kazakhstan shows a more uneven trajectory, with visible fluctuations in several components over time. This pattern is partly consistent with Sharawi et al. (2025), who identifies infrastructure and logistics competence as among the most influential dimensions of logistics performance. In Kazakhstan’s case, the challenge is not limited to the absolute level of individual indicators, but also concerns the instability of their development. In particular, volatility in customs efficiency and timeliness points to persistent institutional and operational bottlenecks that continue to constrain corridor effectiveness.

The practical dimension of these macro-level differences is illustrated by the case of Globalink Logistics. The case study shows that corridor performance depends not only on the existence of international routes, but also on the coordinated interaction of rail, road, sea, and air transport within a single logistics chain. This is consistent with Saha and Khalil (2025), who emphasize the role of dry ports, intermodal systems, and coordinated logistics hubs, as well as with Khussanov et al. (2025), who underline the importance of node interaction in multimodal transport processes. The route modelling results indicate that efficiency is shaped by the balance of modal structure, node connectivity, and time parameters rather than by the dominance of any single transport mode.

This interpretation is further supported by recent published evidence on Globalink and logistics modernization in Kazakhstan. Lambekova et al. (2025) identify Globalink as a major Kazakhstan-based 3PL provider and note its investment in an integrated digital platform combining ERP, fleet management, HR, finance and CRM systems. Seitmyrza (2024) describes Globalink Transportation and Logistics Worldwide LLP as an international freight-forwarding company operating since 1994 and providing air, sea, rail and road transport, customs brokerage, warehousing, refrigerated transport and project logistics services. Abugaliyeva (2023) also emphasizes route optimization, cleaner transport choices, warehouse-resource efficiency, WMS, IoT and digital platforms as important directions for sustainable logistics development. These studies support the relevance of using Globalink as an illustrative case for analyzing multimodal service integration, digital coordination and sustainability-oriented logistics practices.

The company-level questionnaire findings add an important organizational dimension to this interpretation. The survey results show that logistics procedures, managerial support, and customer-oriented service quality are assessed relatively positively, whereas coordination between warehousing and transportation remains comparatively weaker than the other evaluated dimensions. The open-ended responses make this picture more specific. Respondents most frequently pointed to the need for stronger digitalization, automation of logistics procedures, improvement of technical resources, clearer internal performance control, and closer coordination between warehousing, transportation, and related operational units. In addition, some responses emphasized the importance of expanding partnerships, improving market activity, and strengthening staff motivation. These answers indicate that the respondents do not see multimodal efficiency only as a matter of route geography, but also as a result of internal process quality, information exchange, and organizational coordination.

These company-level findings are consistent with recent studies on digital logistics and logistics modernization in Kazakhstan and Central Asia. Imasheva et al. (2026) emphasize that digital platforms, IoT, RFID, intelligent transport systems, and unmanned technologies can improve transparency and reduce operational costs in Kazakhstan’s transport logistics. Similarly, Tazhiyev et al. (2024) show that digital transport and logistics ecosystems, including integrated platforms, real-time tracking, electronic data exchange, and digital corridors, are essential for improving freight management. Liu et al. (2025) also argue that the modernization of logistics systems in Central Asia depends on infrastructure coordination, digital transformation, customs harmonization, and regional policy alignment. Therefore, the survey results of the present study support the broader literature by showing that digitalization, information exchange, and organizational coordination are not only strategic policy issues, but also practical operational problems at the company level.

The FMEA-based risk assessment supports this interpretation. The highest-risk failure mode was identified as delivery delay, followed by delays in information exchange. This is consistent with the respondent insights, since many of the proposed improvements were directly related to better coordination, stronger digital systems, and more efficient process control. In other words, the risks revealed through FMEA are not isolated technical problems, but reflect broader weaknesses in synchronization between departments, document flow, and operational communication. The additional identification of document error as a meaningful risk further reinforces the importance of administrative accuracy and customs-related process reliability.

The operational efficiency coefficient also complements these findings. Although the company demonstrates an above-average efficiency level, the internal diagnostics show that it has not yet reached a fully optimized state. The comparatively weaker result for document accuracy, together with the FMEA results for delivery delay and information exchange, suggests that the main constraints lie not in the absence of transport routes as such, but in the quality of coordination, process standardization, and internal control mechanisms. Therefore, the combined evidence from the questionnaire, respondent recommendations, and FMEA-based risk assessment shows that internal organizational capabilities are a decisive component of multimodal logistics performance at the company level.

When viewed as a whole, the results indicate that Kazakhstan has strengthened its standing within Central Asia, but has not yet reached the level of the leading Eurasian logistics systems. Further progress depends not only on infrastructure expansion, but also on improving service reliability, implementing stronger digital tracking and information systems, modernizing customs procedures, and coordinating logistics hubs and transport modes more effectively. Multimodal corridors should therefore be understood as integrated functional systems whose competitiveness is shaped by the combined effect of infrastructure, institutions, network organization, and logistics operators.

Based on these findings, the practical and policy implications of the study can be structured according to implementation level, expected outcome and measurable indicator (Table 11).

TABLE 11. Implementation-oriented recommendations based on the study findings.

The recommendations in Table 11 specify how the study findings can be translated into practical measures, indicating not only the required actions but also the implementation level, expected outcome and measurable indicators for monitoring progress.

The findings can be read as a multi-level explanation of multimodal logistics performance in Kazakhstan. The LPI analysis identifies macro-level constraints related to infrastructure, customs procedures, tracking and timeliness. The route modelling results show how these constraints influence corridor-level efficiency under different cost and time conditions. The questionnaire findings reveal company-level operational bottlenecks, especially in warehouse–transport coordination, digitalization and document flow. The FMEA-based risk assessment further specifies which operational risks may have the greatest impact on service reliability. This integrated evidence indicates that improving multimodal logistics performance requires not only corridor and infrastructure development, but also stronger digital coordination, risk control and internal process integration at the company level.

5 Limitations of the study

This study has several limitations that should be acknowledged. First, the analysis relies partly on the Logistics Performance Index, which is a perception-based indicator and may not fully reflect the operational performance of national logistics systems. Although the LPI is useful for international comparison, its results depend on expert assessments and may be influenced by subjective perceptions, time lags and differences in data coverage across countries.

Second, the questionnaire data have limited representativeness. The survey was conducted among respondents connected with logistics operations, but the sample does not cover all categories of stakeholders in Kazakhstan’s transport and logistics sector. Therefore, the survey findings should be interpreted as diagnostic evidence of selected operational problems rather than as a fully representative assessment of the entire national logistics market.

Third, the case study is limited to Globalink Logistics. This company provides useful practical insight into multimodal logistics operations, coordination issues and route management, but a single-case design does not allow direct generalization to all logistics companies operating in Kazakhstan. Future studies could expand the empirical base by including several logistics operators, freight forwarders, terminal managers and public-sector institutions.

Finally, the route modelling and FMEA-based risk assessment involve certain simplifications. The model focuses on selected routes, costs, delivery time, risks and operational factors, while not all external conditions can be fully captured. Seasonal fluctuations, sudden geopolitical disruptions, changes in tariffs, border delays, infrastructure failures and market volatility may affect real logistics performance. Despite these limitations, the integrated use of LPI analysis, survey data, case study evidence, route modelling and risk assessment provides a broader understanding of the factors influencing multimodal logistics systems in Kazakhstan.

6 Conclusion

This study shows that the development of multimodal logistics systems in Eurasia is characterized by substantial structural asymmetries, both among Central Asian countries and between the region and stronger logistics economies such as China and Turkey. A comparative analysis based on the Logistics Performance Index confirms that Kazakhstan occupies an intermediate position. It performs more strongly than several neighboring Central Asian states, yet continues to lag behind leading benchmark countries in terms of infrastructure quality, logistics competence, tracking and tracing, and customs efficiency. A temporal comparison with China reveals that China demonstrates relatively stable and systematic progress across logistics indicators, whereas Kazakhstan’s trajectory remains more uneven, indicating the continuing influence of institutional and operational bottlenecks.

The findings suggest that the effectiveness of multimodal logistics systems cannot be explained by geographical location or transit potential alone. Instead, corridor competitiveness depends on infrastructure quality, service reliability, node connectivity and coordination between transport modes. In this respect, Globalink Logistics is a practical example of how multimodal systems function beyond abstract corridor models. Spanning Europe, Asia, and the Middle East, its network structure shows that Kazakhstan’s role in Eurasian logistics is not only transit-based, but also functional. This is because it involves coordinating rail, road, sea, and air services within integrated supply chains.

The modelling results reinforce this conclusion. The analysis shows that the most efficient logistics solution is not necessarily the shortest route or one based on a single dominant transport mode. Greater efficiency is achieved through a balanced combination of route configuration, node interaction, modal shares and transportation time parameters. The identified railway–waterway route confirms that multimodal optimization should be approached as the coordination of an integrated system, rather than prioritizing one transport infrastructure element in isolation. In this sense, the study is valuable because it links macro-level LPI comparison with operator-level evidence and model-based route evaluation within a unified analytical framework.

The company-level questionnaire findings and operational diagnostics provide further support for this conclusion. These findings show that the performance of multimodal logistics systems depends not only on external corridor conditions, but also on internal organizational factors such as digitalization, coordination between operational units, service quality and process reliability. Together with the operational efficiency assessment and FMEA-based risk assessment, the respondent recommendations indicate that delivery delays, problems with information exchange, document accuracy, and interdepartmental coordination remain critical issues for improving logistics performance at company level.

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Footnotes

Edited by: Behzad Hezarkhani, Brunel University London, United Kingdom

Reviewed by: Volodymyr Meshcheriakov, State Biotechnological University, Ukraine

Hossam Sharawi, Jeddah International College, Saudi Arabia