A Study of Port Digital Transformation in Vietnam Using The Analytic Hierarchy Process (AHP)

1. Literature review

Operationally, ports are coordination engines under uncertainty, where information fidelity and timeliness set performance. Digital twins, Just in Time arrivals, and integrated planning across berths, cranes, yards, and gates reduce variance and stabilize vessel rotations, truck dispatching, and rail allocation. Small cuts in port call delay cascade into time and fuel savings on approach. International pilots under the IMO industry Global Industry Alliance (GIA) and the EU Sea Traffic Management (STM) Validation Project have documented significant improvements in predictability and fuel savings; simulations and pilots show large reductions in waiting time and approach phase fuel burn when JIT is adopted at scale (IMO GIA JIT, 2020–2022; STM Validation, 2018). Zhao(2010) found that ports with high levels of data interoperability experienced significant improvements in operational efficiency, including reduced dwell times, increased cargo velocity, and lower transportation costs. Landside, structured appointments, pre-arrival advice, automated gates, yard optimization, and algorithmic stowage and crane scheduling compress truck turn times, reduce rehandling, and sustain higher crane productivity, while predictive exception management preserves throughput. A study by Su(2026) revealed that ports with advanced IT infrastructure experienced substantial improvements in operational efficiency, including significant reductions in vessel turnaround times and a notable increase in overall cargo throughput

Safety, sustainability, and compliance gains reinforce these effects. Condition-based maintenance, reliability analytics, Maritime Single Window processes, and Port Community Systems reduce unplanned downtime, enable risk-based inspections, and smooth pre-arrival clearance, while shore power, voyage speed optimization, and verifiable emissions accounting through Monitoring, Reporting and Verification cut pollutants and fuel use. Research conducted by Munim and Dutta(2020) unequivocally underscores the critical role of a robust ICT infrastructure in facilitating the faster adoption of artificial intelligence (AI) and smart management systems within port operations. Delivery depends on interoperable data, rigorous governance, resilient cybersecurity, and workforce upskilling, guided by standards from the International Association of Marine Aids to Navigation and Lighthouse Authorities, the Digital Container Shipping Association, and the United Nations Centre for Trade Facilitation and Electronic Business.

Digital transformation in port logistics is increasingly seen as a core element for improving operational efficiency and competitiveness, with integrated management and digital systems playing a central role in optimizing port logistics performance (Sirajuddin, 2025). Studies also indicate that process improvements, particularly through "smart gates" models, help improve operational efficiency and reduce cargo handling time at ports (Basulo-Ribeiro, 2024). At the industry level, digital transformation in maritime transport is driving structural changes, from automation to big data analytics, to improve the supply chain (Zeeshan, 2023). Simultaneously, logistics capacity is significantly improved as ports adopt digital technologies appropriate to their local context (Iman, 2022). Key success factors include technological infrastructure, institutional framework, and logistics operational efficiency (Yang and Hsieh, 2024; Alavi-Borazjani, 2025).

2. Methodology: AHP

The Analytic Hierarchy Process (AHP) is one of the original multi-criteria decision-making methods developed by Professor Thomas L. Saaty. The Analytic Hierarchy Process (AHP) is a multi-criteria decision-making method that involves the basic structural criteria of multiple options into a hierarchy, including the relative values of all criteria, comparing the choices for each specific criterion and determining the average importance of alternatives. That way, an establishment is created to make appropriate decisions. This method is used to solve different situations in which the goal is to make decisions. Areas such as government management, economics, health, education (Ivan Pogarcic and Vlatka Davidovic, 2008)

In AHP, priorities between alternatives are determined by pair comparison. In a paired comparison, decision makers examine two alternatives by considering a criterion and indicating a preference. These comparisons are made using the priority scale, assigning numerical values to different priority levels (Taha, 2003).

According to Rezaei, ae al., 2013, while the decision-makers need to determine the goal and alternative solution to attain it, experts have a duty to evaluate the alternative solutions based on criteria. At the end of this step, components of each criterion are structured. After this section, the weights for different criteria are found by pairwise comparison matrices. Based on the level of supporting to the goal, from the first level to lowest level of contribution, criteria in the same level will be compared.

In the survey collection

• a is the sum of values for comparison pairs,
• a1 is the single evaluation of comparable couples,
• n is the number of evaluations

The number of comparisons depends on the number of items being evaluated in <Table 1>.

<Table 1>

Number of comparisons depends on number of things

Elements in the matrix such as aij represent the importance of Criteria 1 relative to criterion 2 whereas they satisfy the following constraints: aij * aji =1

• Comparison matrix

• In the case that
• aij > 1, Criterion I is considered more important than J
• aij < 1, Criterion j is considered more important than i
• aij = 1, Criterion i and j have similar importance

The Decision maker needs to evaluate the criteria in terms of their relative importance. According to Saaty T.L (1977), the relative importance between two criteria is from 1 to 9 as the <Table 2> below.

<Table 2>

The relative importance between two criteria

• - Calculate Priority vector (x)
• - Calculate adjusted weight
• - Calculate Consistency index (CI)

• CI = (λmax - n) / (n−1)
• 　　　λmax​ is Eigenvalue
• 　　　n is the number of criteria
• - Calculate Consistency Ratio (CR)
• CR= Consistency index (CI)/ Random Index (RI)
• 　　Consistency Ratio = CR ≤ 0.1 is considered reasonable.

For the Consistency Ratio (CR= CI/RI)), with the Random Index (RI) corresponding to the matrix size is also required in <Table 3>.

<Table 3>

Random Consistency Index

3. Main and sub factors

Port digital transformation is propelled by a combination of technological, organizational, and environmental factors; specifically, recent systematic reviews identify core drivers in technological infrastructure, organizational capabilities, and policy–environmental pressures (Belmoukari, 2023; Yaya, 2024).

Technologically, robust digital infrastructures such as the Port Community System (PCS), Internet of Things IoT, cloud computing, and shared data platforms together with interoperability standards and cybersecurity, forms the foundation for secure stakeholder data sharing and operational optimization (Yau, 2020; Belmoukari, 2023).

On the environmental–policy front, sustainability requirements and green governance frameworks steer investments toward clean technologies, automation, and emissions management (Yaya, 2024)

Factors affecting the digital transformation of seaports are divided into three main groups of factors: People & Process, External Environment, Technology & data, and sub-factors based on popular theories in seaport digital transformation research, previously published research results, combined with professional comments collected from experts in the field of seaports and the current situation of seaports in Vietnam. The People & Process group is separated because no matter how advanced technology is, the results of digital transformation still depend on leadership vision, strategy, organizational structure, internal processes and human resources capacity - factors that determine the ability to deploy and operate effectively. Meanwhile, the External Environment group reflects objective pressures and conditions such as state policies, customer requirements and market competition, environmental regulations and green port trends; these are factors beyond the control of the port but play an important role in orienting the level and speed of digital transformation. The Technology group is indispensable because the digital transformation of the port depends heavily on technical capabilities such as the level of system modernity, compatibility, information security level and the level of readiness to apply new technology.

The allocation of sub-factors into each group helps clarify the source of impact from technological capabilities, internal organizational strength to external pressure creating a logical, comprehensive and practical analysis structure for seaport operations. With this structure in [Fig. 1], the port can accurately assess the current situation, identify risks, prioritize resources and build a digital transformation strategy in a synchronous and effective manner.

[Fig. 1]

Main and sub factors to evaluate digital transformation

4. Ports In Vietnam

The selection of the five ports—Cai Mep–Thi Vai, Cat Lai, Hai Phong, Da Nang, and Quy Nhon—was based on their strategic importance within Vietnam’s maritime and logistics network, as well as their differing levels of digital transformation readiness and operational characteristics. Cai Mep–Thi Vai and Cat Lai were selected because they are among Vietnam’s largest and most internationally connected container ports, possessing advanced technological infrastructure and high cargo throughput.

Hai Phong was included due to its role as the primary maritime gateway in Northern Vietnam and its ongoing smart port development initiatives. Meanwhile, Da Nang and Quy Nhon represent emerging central-region ports with growing strategic importance in regional logistics and trade connectivity. The inclusion of ports from Northern, Central, and Southern Vietnam ensures geographical representation and enables comparative analysis across different development conditions, technological capacities, and management approaches. This diversity enhances the reliability and comprehensiveness of the study’s assessment framework

Basic information about the 5 selected ports is shown in <Table 4>.

<Table 4>

Ports in Vietnam

1. Main factors

Based on the aggregation of expert judgments collected from specialists, managers, and practitioners in the maritime and logistics sectors, a pairwise comparison matrix titled “Evaluate the Importance of Three Main Factors” was developed. The matrix was used to assess the relative importance of the three primary factors influencing digital transformation readiness in Vietnamese seaports: Human & Process (C1), Technology & Data (C2), and External Environment (C3), which can be present in <Table 5>.

<Table 5>

Using AHP to evaluate the importance of 3 Main Factors

In Table 5, each pair of criteria was compared with one another using the pairwise comparison method of the Analytic Hierarchy Process (AHP). specifically, the numerical values ​​in the matrix reflect the relative importance between two criteria based on survey participants.Through this pairwise comparison process, all criteria were evaluated step by step against each other to determine their relative priorities and final weights. The resulting weights represent the normalized importance of each criterion in the overall assessment framework. Furthermore, the consistency of these comparisons was verified using the Consistency Index (CI) and Consistency Ratio (CR), where CR values below 0.1 indicate acceptable consistency and reliability

First, the 42% weighting for factor C1 - People and Processes confirms that the human factor is the most important foundation. In the context of digital transformation, the human factor and processes play a key role. Training human resources with digital skills, building an organizational culture ready to innovate and standardizing business processes are the decisive factors for the success of the digital transformation process. Without a high-quality workforce and an efficient working process, the application of advanced technology will not bring the expected results. Vietnam has the advantage of a young population, with about 70% of the total 98 million people under 35 years old. However, this advantage in terms of labor resources cannot be converted into digital capacity without proper investment in skills and process development guidance. According to recent national statistics, approximately 60% of workers lack basic digital skills, and only 15% have advanced digital capabilities - the core element to apply technology in the context of Industry 4.0. In the context of Vietnam, the paradox is that the port workforce, despite being both insufficient in number and lacking in skills, has a decisive influence on the digital transformation process. The high weighting (0.40) indicates that this is a key factor, while the low competency level reflects a shortage of skills and high-quality personnel, especially in technical and operational positions. This weakness makes human resources a "bottleneck," slowing down the entire transformation process. Therefore, prioritizing human resource training, standardizing processes and improving organizational capacity becomes a prerequisite in every digital transformation strategy of seaports.

Next, the 31% weight for C3 - Technology and Data shows that technology plays an important role but is not the leading decisive factor as many people mistakenly believe. Vietnam has invested heavily in digital infrastructure including fiber optic networks, data centers, 5G deployment as well as cloud computing service development. However, reality shows that many technology projects are ineffective due to the lack of high-quality human resources and appropriate operating processes. According to Theotokas’s (2024) study on human resource management in the digitalization of the maritime industry, many IT projects at seaports and maritime enterprises fail or fall short of expectations due to a lack of digitally skilled personnel, lack of training programs, and inappropriate internal implementation processes.

Finally, the 27% weight given to C2 - External Environment reflects the role of contextual factors such as policies, economic integration, international technology trends, or requirements from the global supply chain. Although these factors create important dynamics and set mandatory requirements for Vietnamese enterprises, they are not decisive in terms of competitiveness between organizations. All enterprises are similarly affected by the external environment; therefore, the ability to differentiate lies in the internal capabilities of each enterprise - including the quality of human resources, the level of process standardization, and the ability to effectively deploy technology. Therefore, environmental factors are an indirect group of impacts, shaping the changing context but not creating sustainable competitive advantages.

In general, the C1 - C3 - C2 weighing confirms that sustainable digital transformation does not start with technology, but with people, processes, then technology and the external environment.

2. Evaluation Sub-Criteria

Based on the Analytical Hierarchy Process (AHP), a quantitative tool used for evaluation, sub-criteria were developed and applied similarly to the process described above. Specifically, each sub-criterion corresponding to the five ports mentioned was subjected to pairwise comparison, weighting, and score aggregation to reflect the importance and performance of each port in each evaluation aspect. The results obtained from this process are presented in detail below in <Table 6>, showing the clear differences between the ports according to each criterion.

<Table 6>

People & Process (C1) Sub-criteria Weights

<Table 6> presents the relative importance of each sub-criterion within the People & Process dimension (including Workforce Digital Skills & Capabilities (C11), Organizational Culture & Innovation Readiness (C12), Financial Resources (C13) , as determined through the AHP analysis.

The high weight of C11 (0.40) indicates that human resources and digital skills are the most important factors influencing digital transformation at Vietnamese seaports. Investment in technologies such as port operating systems, IoT monitoring platforms, automation systems, data analytics, and e-customs can only be effective when workers can operate, adapting, and innovating with these technologies. As Vietnamese ports transition from traditional manual operations to a digital environment, the shortage of digitally skilled workers has become a major challenge. Many workers have extensive operational experience but are still unfamiliar with digital systems, increasing the risk of inefficiency, cybersecurity vulnerabilities, and delays in cargo handling.

C12 (0.25) emphasizes the supportive role of organizational culture in driving digital transformation. Although weighted lower than C11, organizational culture strongly influences the speed and effectiveness of innovation adoption. Many Vietnamese ports still operate under traditional and hierarchical management structures, prioritizing stability and strict adherence to procedures. Such conservatism can reduce organizational flexibility and hinder experimentation with new technologies. Employees and managers are often hesitant to digitalize due to concerns related to changes in workflows and job responsibilities.

For C13 – Financial Resources (0.34), the results show that financial capacity remains a significant factor facilitating digital transformation, although less influential than human resources. In the context of Vietnamese seaports, the main obstacles often relate not only to limited investment capital but also to governance capacity, technology standardization, and implementation efficiency. Ports need sustainable investment strategies that support cybersecurity protection, network segmentation for operational technology systems, standardized APIs, and improved data management across the port community.

With regard to the next dimension, <Table 7> illustrates the relative significance of each sub-criterion within the External Environment dimension including: Policies & Regulations (C21), Market Factors (C22), Sustainability Environmental Requirements (C23), based on the results of the AHP analysis.

<Table 7>

External Environment(C2)

The research results indicate that the external environment plays a crucial role in shaping the operational efficiency and digital transformation readiness of Vietnamese seaports. Among the three sub-criteria, Policies and Regulations (0.48) received the highest weight, showing that institutional support and management efficiency are the most influential external factors. A transparent legal framework, efficient licensing procedures, stable tariff policies, and clear regulations on foreign investment are essential to minimize uncertainty and encourage long-term infrastructure investment.

Market Factor (0.35) represents the second most important external factor, reflecting the increasing competition among Vietnamese seaports and regional maritime gateways. Ports that can offer lower logistics costs, higher productivity, reliable inland connections, and consistent service quality are more likely to attract carriers and cargo volumes. Competitive pressure encourages ports to continuously improve operational performance, enhance logistics integration, and adopt digital technology instead of relying on one-time infrastructure upgrades. This finding also reflects the increasing importance of supply chain efficiency and customer-oriented port services in the regional logistics market.

Sustainability (0.17) received the lowest weight among the external environmental factors, indicating that Vietnam's port industry still prioritizes growth and operational efficiency over environmental goals. However, sustainability is becoming increasingly important due to global pressure for green logistics, stricter environmental regulations, and international decarbonization initiatives. Environmental assessments, emissions regulations, green port standards, and energy-saving practices are gradually shaping long-term competitiveness and access to international cargo flows. While sustainable investment remains challenging due to financial and technological constraints, the development of green logistics and environmentally friendly port systems is expected to become a more influential factor in the near future.

Finally, <Table 8> presents the relative importance of each sub-criterion within the Technology and Data dimension including IT & Telecommunications Infrastructure (C31), Presence of Advanced Tech + Foresight & Agile Adoption (C32), Data Standardization & Interoperability (C33)

<Table 8>

Technology and data (C3) Sub-criteria Weights

With an overwhelming weight of C31 (0.43), infrastructure is the first and most important battleground. In port operations, IT infrastructure means flexible dual-path fiber, SD-WAN across all sites, and robust wireless across gates, yards, and wharfs, where device movement and latency must be predictable. Many terminals in Vietnam already have good core connectivity and basic redundancy but still suffer from “dead zones” in yards, blind spots, single points of failure in access circuits, and limited disaster recovery.

Data Standardization & Interoperability (C33) ranks second in importance, with a weight of 0.33. Data Standardization and Interoperability in the port system will ensure that different systems can “talk” to each other and share information quickly and easily, especially in difficult situations. This is important in the logistics industry, where information must be exchanged regularly and continuously between various stakeholders, including ports, shipping lines, logistics companies and customs authorities. The application of international and regional standards helps increase transparency and efficiency of the supply chain. Without standardization, system integration and data mining will be difficult and expensive.

C32: Presence of Advanced Technology + Agile Vision & Adoption has the lowest weight (0.24), but this does not mean that this factor is not important, but only because it depends on the infrastructure as well as the ability to standardize data interactions. The application of advanced technologies such as artificial intelligence (AI), Internet of Things (IoT) and blockchain can help improve operational efficiency and reduce costs for seaports. For example, AI systems can accurately predict the time and destination of cargo ships, helping ports handle cargo more efficiently. However, for these advanced technologies to work effectively, a solid IT infrastructure and standardized data are essential.

3. Port Ranking

Based on the Analytical Hierarchy Process (AHP), a quantitative tool used for evaluation, sub-criteria were developed and applied similarly to the process described above. Specifically, each sub-criterion corresponding to the five ports mentioned was subjected to pairwise comparison, weighting, and score aggregation to reflect the importance and performance of each port in each evaluation aspect. The results obtained from this process are presented in detail below (<Table 9>), showing the clear differences between the ports according to each criterion.

<Table 9>

Port Scores by Criterion and Overall

Based on the results obtained from the AHP method, the five ports were ranked from 1 to 5 as shown in <Table 10>.

<Table 10>

Port Ranking (Overall)

According to the table, Cai Mep-Thi Vai Port (A4) has the highest weight (0.309) and ranked 1st, followed by Cat Lai Port (A1) (0.279) in 2nd. Hai Phong Port (A3) is ranked 3rd with a weight of 0.196 and the remaining two ports Da Nang (A5) and Quy Nhon (A2) have low weights with 0.110 and 0.106 respectively. This result reflects a clear difference in capacity and readiness for digital transformation.

C1. People & Processes. Cai Mep-Thi Vai has the highest score (0.3108), followed by Cat Lai with 0.306. These two ports have modern management teams, apply international standard operating systems and train specialized digital human resources. In addition, Cai Mep-Thi Vai is oriented to become a digital super port, requiring streamlined operations and multimodal connections. In contrast, Quy Nhon (0.097) and Da Nang (0.10) are still limited in process synchronization and lack of specialized IT human resources.

C2. External environment. This is a factor reflecting the connection with the global supply chain, connecting infrastructure, and support policies. Cat Lai leads (0.32) with the location near the center of Ho Chi Minh City, international trade gateway and extensive logistics service network. Meanwhile, Cai Mep-Thi Vai (0.24) stands out for its regional strategy and trans-Pacific shipping routes (22/25 routes to the US depart from here). Hai Phong also scores 0.25 because of its direct connection to international shipping routes and strategic location in the North.

C3. Technology & Data. Cai Mep–Thi Vai scored 0.37, excelling in its application of Big Data, IoT, and automated port management system. Cat Lai (0.21) has deployed container management software and the E-Port system, but its scale and integration are not as good as Cai Mep–Thi Vai. Hai Phong (0.16) is still in the process of perfecting digital solutions and is affected by process and infrastructure barriers, so it is not rated too highly. Da Nang and Quy Nhon (0.13) currently still lack centralized data systems and online analytics capabilities.

Cai Mep-Thi Vai ranked first and scored highly in all three criteria, especially C1 and C3, along with a favorable external environment. Cat Lai followed closely in second place with advantages in international trade and cooperation with many large shipping lines but was weaker than Cai Mep-Thi Vai in terms of technology. Hai Phong ranked third, strong in location and external environment, but technology was not yet synchronized. Da Nang and Quy Nhon ranked last in all three criteria, mainly due to technology infrastructure and human resources not meeting modern digital transformation standards.