Implementing Industry 4.0 in Green Digital Shipping Corridors (GDSC): Human Factor’s Operational Readiness

1. Introduction

The maritime industry is navigating a profound structural reform catalyzed by converging the dual imperatives of decarbonization and digitalization. This dual transformation is a respond to the multilateral regulatory frameworks notably the International Maritime Organization’s (IMO) revised greenhouse gas reduction strategy and growing industry commitments to sustainability. It is expected that the energy efficiency measures, zero-emission technologies, and alternative fuels have the potential to cut GHG emissions by 20–30% by 2030 and 70–80% by 2040 (MEPC, 2023).

Central to this transition are Green Digital Shipping Corridors (GDSCs) that represent a solution that unifies efforts in decarbonization, digitalization, and workforce transformation. The GDSCs are designated maritime routes between two or more ports where zero-emission shipping solutions are demonstrated and reported (Bengue et al., 2024). These corridors integrate low- and zero-emission fuels, advanced digital technologies, and coordinated stakeholder actions to achieve scalable, decarbonized, and smart shipping solutions. The GDSCs serve as testbeds for technological, commercial, and regulatory initiatives aimed at reducing maritime emissions (Khabir et al., 2025).

The maritime sector’s adoption of Industry 4.0 principles – conceptualized as “Shipping 4.0” – represents a paradigm shift in ocean transportation systems. The Shipping 4.0 characterized by Industry 4.0 technologies in shipping. The convergence of technologies such as Internet of Things (IoT), Cyber–Physical Systems (CPS), Digital Twins, Edge AI, and advanced communication networks is offering unprecedented opportunities for real-time data exchange, predictive diagnostics, and semi-autonomous navigation (Aiello et al., 2020; Emad et al., 2020a, 2025). While the technical potential is widely recognized, successful implementation requires understanding how human factors, including seafarer competencies, human–machine interaction, and organizational readiness, mediate the effectiveness of these systems (Emad & Shahbakhsh, 2022a; Han et al., 2021).

Current research on GDSCs predominantly emphasizes technological architectures (Diaz et al., 2023; Dolatabadi et al., 2025; M. Editors, 2023; S. Editors, 2023a; Song et al., 2023; Zhang & Feng, 2024) and policy frameworks (S. Editors, 2023b; Slotvik et al., 2022), while comparatively less attention is given to the operational readiness of seafarers and shore-based personnel who must engage with increasingly complex digital ecosystems. Diverging hypotheses exist arguing automation will reduce reliance on human expertise(Emad et al., 2022; Shahbakhsh et al., 2022), while others highlight that human oversight becomes even more critical in autonomous or remote operations(Emad et al., 2020a; Jokioinen, 2016; Luchenko et al., 2023; Rodseth & Burmeister, 2015; Theotokatos et al., 2023a, 2023b). This lack of clarity underscores the need for a structured framework that integrates technological and human dimensions of Industry 4.0 implementation.

The purpose of this study is to address this gap by:

• Proposing a five-layer Industry 4.0 architecture tailored to GDSCs.
• Developing a conceptual-comparative framework contrasting traditional navigation and monitoring systems with Industry 4.0-enabled systems across 14 performance dimensions.
• Introducing a human operational readiness model to support the transition from manual operations to data-driven supervisory roles; and
• Presenting a phased implementation roadmap that aligns technology deployment with workforce adaptation.

By synthesizing these elements, this study argues that technological advancement without parallel investment in human factors may result in three critical failures of cognitive overload, resistance to adoption, and operational inefficiencies. Conversely, incorporating human element in system design can accelerate the realization of safe, efficient, and resilient green maritime corridors.

Methodology

This study employs a layered and iterative research methodology designed to integrate theoretical insights with pragmatic industry perspectives. The process begins with a clear definition of the problem and a research design that establishes the study’s objectives, scope, and guiding questions. Data collection follows a dual approach, combining a systematic literature review with industry insights. The literature review, conducted through comprehensive searches in Scopus, Web of Science, and IEEE Xplore databases, synthesizes relevant concepts, theories, and research gaps. Concurrently, empirical data from technical reports, white papers, and expert interviews enrich the analysis with current and applied knowledge from the field. These combined data sources feed into strategic analyses using tools like McKinsey’s 12 Elements of a Dynamic Operating Model (Krivkovich et al., 2025)and Upgraded TRL-HRL analysis (Browne et al., 2024; Handley et al., 2024; Salazar & Russi-Vigoya, 2021). This allows evaluating internal and external factors influencing the domain, alongside interface mapping to understand the relationships among regulatory, technological, and organizational elements.

Building on these insights, the study develops a conceptual framework through an iterative process involving expert validation. This framework incorporates a multi-layered model to organize interrelated system components and a comparative table that benchmark international practices. Alignment with international standards further enhances the framework’s credibility. Finally, the evaluated framework informs the development of a strategic roadmap, outlining phased implementation steps, critical success factors, and policy recommendations aligned with global objectives such as the Sustainable Development Goals (USDGs) and industry decarbonization targets. This roadmap is tailored to facilitate practical adoption and scalability across diverse stakeholder contexts, thereby bridging academic research with real-world impact. Figure 2 demonstrates these steps.

Figure 1. Research Methodology.

Figure 1. Research Methodology.

2. Industry 4.0 Framework for GDSCs

The integration of Industry 4.0 technologies into maritime transport—Shipping 4.0—forms the technological backbone of Green Digital Shipping Corridors (GDSCs). These corridors rely on advanced systems to enable real-time monitoring, predictive maintenance, and semi-autonomous operations, to achive zero-emission shipping. This section outlines a multi-layered Industry 4.0 framework tailored to the unique operational and regulatory demands of GDSCs, highlighting how these technologies reshape maritime logistics, vessel operations, and human-machine interaction.

2.1. Shipping 4.0 Components and Applications

Shipping 4.0 is transforming traditional shipping into a smart, connected, and efficient ecosystem. Central to this transformation is the adoption of technologies such as IoT that enables real-time tracking of vessel conditions, cargo status, and port equipment through embedded sensors, while cloud computing facilitates remote fleet management and operational control. Digital twin technology allows for real-time simulation and diagnostics of ship systems, enhancing maintenance and performance planning. Meanwhile, automation and robotics are revolutionizing both onboard operations and port logistics through autonomous cranes, guided vehicles, and unmanned ships, supported by reliable high-speed communication technologies like 5G and satellite networks. Blockchain further strengthens transparency and trust in global supply chains by securing documentation and transactions such as bills of lading.

Among these technologies, Artificial Intelligence (AI) plays a pivotal role in this digital maritime ecosystem(Ceyhun, 2019; Joshva et al., 2024). In route optimization, AI algorithms analyze weather, currents, and traffic data to propose fuel-efficient paths, reducing both costs and emissions. machine learning models monitor engine performance and predict maintenance by processing historical and real-time operational data to forecast failures and suggest timely interventions. AI is being used to streamline cargo handling, berth scheduling, and customs processing, enhancing overall turnaround time in ports. Emissions monitoring systems powered by AI track the pollutant levels and support compliance with international environmental regulations, while guiding the transition to alternative fuels. Most notably, AI enables Maritime Autonomous Surface Ships (MASS) by integrating navigation, situational awareness, and decision-making systems (Rajapakse & Emad, 2019) pushing the industry toward safer, more sustainable, and unmanned operations (Ceyhun, 2019; Emad et al., 2024; Güner, 2022).

Figure 2. Shipping 4.0/ Industry 4.0 Components (Aiello et al., 2020; Baum-Talmor & Kitada, 2022; Emad et al., 2020a; Varelas et al., 2023).

Figure 2. Shipping 4.0/ Industry 4.0 Components (Aiello et al., 2020; Baum-Talmor & Kitada, 2022; Emad et al., 2020a; Varelas et al., 2023).

2.2. Green Digital Shipping Corridors (GDSC)

Green Digital Shipping Corridors (GDSCs) are advanced maritime routes that integrate zero-emission fuels, cutting-edge digital technologies, and collaborative frameworks to accelerate the decarbonization of global shipping(Khabir et al., 2025). At their core, GDSCs rely on sustainable energy sources such as hydrogen, ammonia, methanol, and biofuels, supported by port-based fuel infrastructure and shore-side electrification systems (“Alternative Fuels Data Center: Hydrogen Basics,” n.d.; Editorial Team, 2024; Emad et al., 2020b; Khabir et al., 2020; Khan et al., 2021a, 2021b). These green fuels are complemented by energy-efficient operations and smart logistics systems powered by Industry 4.0 technologies.

Operationally, GDSCs boost fuel efficiency, reduce emissions, and enhance port coordination through smart scheduling, emissions monitoring, and energy optimization tools. These corridors are strategically designed through public-private partnerships that align regulatory, technological, and financial frameworks across borders. Unified efforts among governments, shipping companies, ports, and technology providers enable scalable models, like the Oslo–Rotterdam and Singapore–Los Angeles corridors, that can be replicated globally. GDSCs not only address the shipping sector’s environmental impact but also deliver broader socio-economic benefits, including improved air quality, reduced health risks for port communities, and the stimulation of green economic growth. Their successful implementation depends on deliberate planning, cross-sector collaboration, and strong policy support, making them a transformative approach to sustainable maritime transport. Figure 3 summarizes the core components of green digital shipping corridors (GDSC).

2.3. Comparative Analysis: Traditional vs Industry 4.0 Systems

As maritime transport enters the era of digital transformation, the contrast between traditional navigation and monitoring systems and those aligned with Industry 4.0, especially within the context of Green Digital Shipping Corridors (GDSCs), has become increasingly pronounced. Table 1 provides a structured comparison between these two paradigms, highlighting the key technological, operational, and organizational shifts driving this evolution. By mapping 14 critical dimensions of navigation and monitoring systems, the table outlines how emerging technologies are reshaping maritime operations to enable greater automation, intelligence, and resilience.

Table 1 compares traditional maritime navigation and monitoring systems with those enabled by Industry 4.0 technologies within GDSCs. Traditional systems are typically standalone, hardware-driven, and heavily reliant on manual inputs and crew experience. Their architecture is characterized by fragmented subsystems, limited integration, and static feedback mechanisms. Decision-making and situational awareness depend primarily on human cognition, with low automation and high cognitive load imposed on operators.

Table 1 illustrates that in contrast to the convectional shipping, Industry 4.0 and GDSC systems integrate real-time data processing, predictive analytics, and self-learning capabilities through a unified ecosystem of ship, port, and corridors. Key enhancements include:

• Automation and optimization through AI and CPS,
• Digital supervisory roles for crew members,
• Modular scalability and software-driven adaptability,
• Advanced communication via 5G, satellite, and blockchain,
• Integrated cybersecurity and automated compliance protocols.

Notably, human operators’ role shift from direct control to supervisory and strategic oversight within “human-on-the-loop” decision frameworks, enhancing safety and efficiency (Emad, 2015). The table thus highlights the transformative potential of GDSCs, not only in terms of technology deployment but also in redefining operational paradigms and regulatory approaches in the maritime sector.

2.4. Shipping 4.0 Architecture in GDSC

Shipping 4.0 (Industry 4.0) has precipitated a significant transformation in operations processes, which must be executed through the integration of comprehensive industrial systems (Emad, 2020). Although shipping 4.0 continues to pose considerable challenges for numerous logistics enterprises, reference architectures have seen increased adoption across various domains to assist engineers in defining system interoperability and structural design. Organizations have gained diverse experiences with these reference architectures for shipping 4.0. Nevertheless, the suitability of specific reference architecture varies depending on the distinct use cases it aims to address, potentially affecting its efficacy in supporting organizational transformation(Nakagawa et al., 2021; Narayanan et al., 2023). Furthermore, a complete understanding of existing representative architecture remains elusive. Having a Shipping 4.0 architecture embedded within GDSC is necessary because it provides the digital backbone that connects real-time operational data from ships and ports to high-level decision-making, enabling sustainability, efficiency, and resilience. As illustrated in Figure 4 a pyramid of Shipping 4.0 architecture layered in GDSC ranging from field devices to strategy and enterprise level can be developed. Consequently, each layer is described as follows:

2.4.1. Perception & Control Layer (IoT, CPS)

This foundational layer represents the physical-digital interface of the shipping corridor. It consists of sensors and actuators embedded in ships, cargo systems, and port infrastructure to collect real-time data and initiate automated responses. Internet of Things (IoT) sensors capture critical environmental and operational parameters, such as CO₂, NOₓ, and SOₓ emissions, cargo conditions, hull integrity, and vessel location, while cyber-physical systems (CPS) like smart propulsion, dynamic positioning systems (DPS), and robotic cranes translate digital commands into physical actions. Local control units such as programmable logic controllers (PLCs) and supervisory control and data acquisition (SCADA) systems manage real-time machine behavior, ensuring precision and responsiveness in propulsion, energy use, and port handling operations(Gerrero-Molina et al., 2024; Iqbal et al., 2025). This layer is critical for data acquisition, real-world interaction, and enabling automation at the operational edge.

2.4.2. Communication & Connectivity Layer (IoT, CPS, Edge)

This layer serves as the neural network of the GDSC ecosystem, enabling seamless, secure, and real-time communication between ships, ports, cloud platforms, and edge systems. Advanced maritime communication networks—such as 5G, VDES (VHF Data Exchange System), and satellite routers—ensure constant connectivity even in remote ocean regions(Iqbal et al., 2025; Yang et al., 2022). These technologies support high-bandwidth, low-latency data exchange, which is required for real-time navigation, cargo tracking, and port coordination. In parallel, cybersecurity modules embedded in devices and networks provide robust protection through encryption, intrusion detection, and anomaly diagnostic tools. Blockchain nodes may also be deployed within this layer to secure documentation (e.g., bills of lading), track compliance, and validate transactions across stakeholders, reinforcing trust and transparency throughout the digital corridor(Balador et al., 2018; Nasaruddin & Emad, 2019).

2.4.3. Data Processing & Integration Layer (Edge AI, CPS, Cloud)

This middle layer manages the flow, organization, and contextualization of data captured from the physical environment. It incorporates edge computing systems and localized AI modules to filter, analyze, and act on data close to the source, minimizing latency and reducing dependency on centralized processing. Edge gateways and onboard servers preprocess massive volumes of sensor data, enabling local decision-making in scenarios such as collision avoidance, real-time emissions control, or equipment diagnostics. At the same time, middleware and API gateways ensure seamless interoperability between diverse maritime platforms, ship systems, and port technologies. Digital twin interfaces—virtual representations of physical assets, are also established in this layer to enable synchronized simulation, diagnostics, and forecasting, making it the digital backbone that ensures operational continuity and intelligent integration(Avgeridis et al., 2023; Eom et al., 2023).

2.4.4. Application & Intelligence Layer (Edge AI, Cloud)

This layer transforms processed data into actionable intelligence, driving performance optimization, compliance, and predictive operations. Cloud-based and edge-based applications power advanced analytics tools for route optimization, fuel efficiency, and maintenance forecasting. AI models trained on historical and real-time data enable dynamic voyage planning, cargo sequencing, and emissions reduction strategies. Compliance with international maritime regulations—such as IMO 2020/2050 or MARPOL—can be automated through dedicated tools that log, audit, and report emission levels, waste discharge, and energy consumption(IMO, 2020a, 2020b, 2020c). This layer also supports AI-enhanced decision-making for fleet managers and operators, offering real-time insights into vessel health, environmental impact, and logistical performance. As the brain of the system, it enables adaptive, resilient, and efficient corridor management.

2.4.5. Strategic & Enterprise Layer (Cloud)

This layer, at the top of architecture, encompasses strategic planning, enterprise integration, and sustainability governance. It includes cloud-based Enterprise Resource Planning (ERP) systems(Rub, 2025), Supply Chain Management (SCM) platforms(Lee et al., 2025), and high-level dashboards for tracking emissions, ESG (Environmental, Social, Governance) performance, and compliance metrics(Wang, 2025). Data from all lower layers is aggregated here to support decisions related to long-term fleet planning, investment in green infrastructure, and inter-port collaboration strategies. Digital twin simulations and AI-powered platforms support scenario planning, capacity forecasting, and strategic resource allocation(Carayannis et al., 2025; Spaniol & Rowland, 2023). This layer empowers stakeholders, from logistics executives to sustainability officers, with a comprehensive view of the corridor’s environmental and operational performance, aligning shipping operations with global climate and digital transformation goals.

3. Human Factor’s Operational Readiness

3.1. Necessity and Context

The maritime industry is undergoing a fundamental shift in crew roles as automation, artificial intelligence (AI), and advanced analytics reshaping shipboard operations. Modern seafarers increasingly function as supervisors of AI-driven systems, requiring them to interpret AI-generated insights, validate automated decisions, and intervene in ambiguous or high-stakes scenarios. This evolution demands:

• Digital literacy for navigation of complex systems and robust cybersecurity awareness,
• AI supervision skills to understand machine learning limitations and decision-making boundaries, and
• Cognitive resilience to sustain performance under operational stress.

Without a unified competency framework, training risks becoming reactive and fragmented, unable to meet the integrated demands of human–machine collaboration, predictive operations, and sustainability compliance (Emad & Oxford, 2008).

3.2. Innovative Perspective

This framework addresses four key innovations:

• Bridging Traditional and Future Skills – Each competency maintains a link to its STCW origin while extending into AI-enabled, digitalized workflows.
• Human–Technology Synergy – Competencies are defined to position the human operator as a strategic decision-maker in human-in-the-loop (HITL) systems, ensuring oversight for AI recommendations and ethical considerations.
• Integrated Safety and Cybersecurity Readiness – The framework embeds multi-layered assurance measures, including HITL protocols for safety-critical actions, escalation pathways for low-confidence AI outputs, zero-trust cybersecurity architectures, penetration testing, and explainable AI outputs for transparency.
• Sustainability and Green Competence – Environmental stewardship is integrated into technical and operational competencies, aligning with decarbonization targets and green digital shipping corridor initiatives.

3.3. Human-Machine Interface (HMI) and Operational Design Considerations

Effective application of these competencies requires HMI solutions tailored to maritime environments, where fatigue, noise, and high stress are common. The table supports training for:

• Adaptive HMI designs that adjust to operator skill levels and situational contexts,
• Multimodal feedback systems (visual, auditory, haptic) to enhance situational awareness, and
• Unified decision dashboards integrating AI predictions with real-time sensor inputs, ensuring transparency and rapid escalation options for human intervention.

This focus ensures that automation enhances, rather than replaces, human decision-making authority, particularly in navigation, collision avoidance, and emergency response.

3.4. Continuous Improvement and Adaptation

The competency framework is designed as a living structure, supporting continuous evolution through feedback mechanisms such as:

• Reinforcement Learning from Human Feedback (RLHF) to refine AI performance using operational inputs,
• Combined automated and human evaluation protocols to assess both objective metrics and contextual decision quality, and
• Adaptive regulatory alignment to ensure that safety and performance standards evolve alongside technological capabilities.

3.5. Research Contribution

From an academic standpoint, the competency mapping table contributes by:

• Establishing a taxonomy of Shipping 4.0 competencies that is internationally aligned and operationally actionable.
• Offering a training and assessment blueprint that bridges human factors research with AI, HMI design, and maritime safety science.
• Providing a foundation for Key Performance Indicator (KPI)-linked evaluation models, enabling empirical study of competency impacts on operational efficiency, safety performance, and environmental compliance.

In essence, this framework functions as both a diagnostic tool, identifying competency gaps in AI-augmented operations, and a strategic blueprint for training, policy-making, and ongoing human–machine performance optimization.

The competency mapping in Table 2 provides a structured, multidimensional framework for defining, assessing, and evolving human-factor capabilities in the context of Shipping 4.0. The table organizes competencies into eight key operational domains—Technical & Digital, Cognitive & Situational Awareness, Decision-Making & Problem-Solving, Communication & Collaboration, Leadership & Change, Safety, Sustainability & Green, and Psychological & Human-Centric—and systematically links them to the relevant Standards of Training, Certification and Watchkeeping (STCW) Table/Regulation. Each competency is mapped from its traditional maritime equivalent to its expanded Shipping 4.0 form, concluding with Industry 4.0 implications that highlight operational capabilities such as AI-assisted navigation, predictive maintenance, cyber resilience, and environmental compliance.

4. Strategic Analysis

The successful integration of Industry 4.0 technologies into Green Digital Shipping Corridors (GDSCs) requires a dual focus: transforming organizational structures and ensuring synchronized readiness between advanced systems and their human operators. This section presents two complementary strategic analysis frameworks tailored to the maritime context.

First, the McKinsey 12 Elements of a Dynamic Operating Model is applied to assess how leadership, structure, processes, culture, and talent must be reconfigured to enable safe, efficient, and sustainable human–technology collaboration. Second, an Upgraded Technology Readiness Level–Human Readiness Level (TRL–HRL) Matrix evaluates the maturity of critical Industry 4.0 components alongside the preparedness of seafarers and shore-based personnel to operate, supervise, and adapt to them. Together, these analyses provide an integrated roadmap for aligning organizational transformation with phased technology deployment in GDSCs.

4.1. Dynamic Operating Analysis

As maritime operations evolve toward cyber-physical and AI-augmented systems within Green Digital Shipping Corridors (GDSCs), a coherent organizational transformation strategy is essential. This section adopts the McKinsey 12 Elements of a Dynamic Operating Model—also known as the “Organize to Value” framework—to assess and guide the integration of human factors in this transition. The framework evaluates how organizations align purpose, processes, technology, and people to create value under conditions of systemic change.

Figure 5. McKinsey’s 12 elements of the ‘Organize to Value’ system.

Figure 5. McKinsey’s 12 elements of the ‘Organize to Value’ system.

1. Purpose

The core purpose of GDSC-enabled shipping organizations must extend beyond logistics efficiency to encompass sustainable, intelligent, and human-centric maritime operations. Clearly articulating this purpose helps crew members and stakeholders understand the “why” behind digital transformation, reducing resistance and anchoring the workforce in a shared mission.

2. Value Agenda

Human-AI collaboration becomes a primary source of value in Shipping 4.0. By shifting human roles from manual execution to digital oversight, companies unlock new efficiencies and safety outcomes. Value is created not just through technology adoption, but through effective training, cognitive resilience, and ethical decision-making.

3. Structure

Organizational design must shift from siloed maritime roles to mission-oriented teams combining AI developers, maritime operators, safety engineers, and trainers. Hybrid roles such as “AI Supervisor” or “Digital Twin Analyst” should be institutionalized within new operational hierarchies.

4. Ecosystem

No single organization can enable GDSCs alone. Port authorities, technology vendors, regulators, and maritime training institutions must collaborate. These partnerships create shared platforms for digital compliance, simulator-based training, and real-time data exchange, generating value beyond individual firm boundaries.

5. Leadership

Leadership must actively champion the human dimensions of AI transformation, not just the technological rollout. Captains, fleet managers, and port executives should model digital adoption, sponsor training initiatives, and serve as agents of cultural change across the organization.

6. Governance

Effective governance involves defining clear decision rights in human–machine interfaces (e.g., human-on-the-loop protocols), managing algorithmic transparency, and allocating resources to both technical and human readiness. Policy and audit frameworks should ensure traceability, override control, and compliance in AI operations.

7. Processes

Maritime workflows must be redesigned for shared control between humans and AI. New processes should include structured feedback loops, cognitive load assessments, ethical escalation paths, and AI validation checkpoints—particularly in autonomous or semi-autonomous contexts.

8. Technology

Technologies like digital twins, IoT sensors, and edge AI are enablers—but only when deployed with intuitive human-machine interfaces (HMIs). Crew members must be trained to interpret system outputs, flag anomalies, and collaborate with AI systems as intelligent partners, not passive users.

9. Behaviors

Culture must shift toward continuous learning, digital trust, and open adaptation. Behavioral norms should encourage seafarers to question AI decisions, provide feedback to developers, and actively engage with data systems—while upholding safety and ethical standards.

10. Rewards

Incentive structures should evolve to recognize non-traditional contributions—such as successful intervention in AI operations, feedback quality, and training progression. Recognition systems must reinforce the value of human judgment in AI-supported environments.

11. Footprint

Crew deployment models must be reevaluated in light of GDSCs. AI-augmented ships may require fewer personnel onboard but more distributed roles, such as remote AI monitors, data analysts, and simulation trainers. Location strategies must align digital capabilities with operational zones.

12. Talent

Organizations must attract, develop, and retain talent with hybrid capabilities: maritime knowledge combined with digital fluency. Training institutions should redesign curricula to cover AI supervision, ethical reasoning in automation, and cybersecurity resilience, ensuring the right capabilities are in place to meet value creation goals.

Conclusion

The McKinsey 12-element operating model provides a comprehensive lens to align technological ambition with human readiness in GDSC implementation. By addressing all facets, from leadership and technology to behaviors and rewards, shipping organizations can establish an integrated, future-proof framework that supports safe, sustainable, and resilient digital transformation at scale.

Building on these insights, the Upgraded Technology Readiness Level–Human Readiness Level (TRL–HRL) Matrix evaluates the maturity of critical Industry 4.0 components alongside the preparedness of seafarers and shore-based personnel to operate, supervise, and adapt to them. Together, these analyses provide an integrated roadmap for aligning organizational transformation with phased technology deployment in GDSCs.

4.2. Upgraded TRL–HRL Analysis

The effective implementation of Industry 4.0 technologies in Green Digital Shipping Corridors (GDSCs) depends not only on the maturity of technological components but equally on the readiness of human operators to engage, supervise, and adapt to these systems. The traditional Technology Readiness Level (TRL) and Human Readiness Level (HRL) frameworks provide a foundation for assessing these dimensions; however, the complexity of AI-augmented maritime operations demands an enhanced version of this matrix. Table 4 proposes an Upgraded TRL–HRL Matrix tailored for GDSCs, incorporating additional dimensions of trust, explainability, cognitive load, and team coordination, which are essential for safe and resilient human–machine integration.

The upgraded matrix introduces five new dimensions beyond standard TRL and HRL assessments (Table 3):

These dimensions directly influence HRL scoring and guide human-centered AI deployment strategies.

Table 4. Upgraded TRL–HRL Matrix Application in GDSC Context.

Shipping 4.0 Layer	Technology (TRL)	Human (HRL)	Trust & Explainability	Cognitive Load	Human-AI Integration Readiness
Perception & Control	TRL 8–9: Mature IoT/CPS	HRL 5–6: Operational proficiency	Moderate (sensor fusion is interpretable)	Medium (multiple alert systems)	Suitable for deployment with monitoring
Communication & Connectivity	TRL 7–8: VDES, 5G, blockchain	HRL 3–4: Limited training in protocols	Low to Medium (blockchain is abstract)	Medium	Needs simulation and comms drills
Data Processing & Integration	TRL 6–7: Edge AI, middleware	HRL 2–3: Early familiarity	Low (AI decisions are opaque)	High	Not ready; needs transparency tools and training
Application & Intelligence	TRL 5–6: Predictive routing, emissions AI	HRL 2–3: Basic dashboard use	Low (black-box AI)	High	High risk; phased rollout with strong oversight
Strategic & Enterprise Layer	TRL 4–5: Digital twin planning, ESG dashboards	HRL 1–2: Minimal exposure	Very Low	Variable	Focus on training planners and officers

Table 4. Upgraded TRL–HRL Matrix Application in GDSC Context.

Shipping 4.0 Layer	Technology (TRL)	Human (HRL)	Trust & Explainability	Cognitive Load	Human-AI Integration Readiness
Perception & Control	TRL 8–9: Mature IoT/CPS	HRL 5–6: Operational proficiency	Moderate (sensor fusion is interpretable)	Medium (multiple alert systems)	Suitable for deployment with monitoring
Communication & Connectivity	TRL 7–8: VDES, 5G, blockchain	HRL 3–4: Limited training in protocols	Low to Medium (blockchain is abstract)	Medium	Needs simulation and comms drills
Data Processing & Integration	TRL 6–7: Edge AI, middleware	HRL 2–3: Early familiarity	Low (AI decisions are opaque)	High	Not ready; needs transparency tools and training
Application & Intelligence	TRL 5–6: Predictive routing, emissions AI	HRL 2–3: Basic dashboard use	Low (black-box AI)	High	High risk; phased rollout with strong oversight
Strategic & Enterprise Layer	TRL 4–5: Digital twin planning, ESG dashboards	HRL 1–2: Minimal exposure	Very Low	Variable	Focus on training planners and officers

Table 5 presents the standardized nine-level scales for Technology Readiness Level (TRL) and Human Readiness Level (HRL), providing a common benchmark for assessing the maturity of Industry 4.0 systems and the corresponding preparedness of their human operators.

5. Implementation Roadmap for GDSCs’ Human Factor

5.1. Implementation Phases

The effective integration of Artificial Intelligence (AI) systems within maritime operations requires a structured, phased approach that addresses both technological reliability and human readiness. A successful roadmap should align system maturity with crew competency, while ensuring regulatory compliance and long-term adaptability.

Phase 1: Controlled Pilot Testing (3–6 months)

Initial deployment should occur in controlled environments such as ship simulators or low-risk operational routes with predictable conditions. This phase focuses on validating the core functionalities of AI systems, identifying potential edge cases, and establishing baseline performance metrics. Concurrently, it provides a safe context for crew members to familiarize themselves with AI-supported operations and interface dynamics.

Phase 2: Workflow Integration (6–12 months)

Upon successful pilot completion, human-AI collaboration protocols must be developed and institutionalized. This includes the formalization of decision-making hierarchies, override protocols, and emergency roles. Specific procedures should be established for communication between bridge teams and AI systems, including standardized response models and user interfaces. The resulting documentation serves as a foundation for subsequent training and certification.

Phase 3: Competency Development (Ongoing)

A multi-tiered training framework is essential to develop the cognitive, technical, and supervisory skills required for effective human-AI interaction. Training should begin with foundational digital literacy and progress toward advanced AI oversight. Simulation-based exercises, grounded in real operational data, should be designed to reflect increasing complexity. Assessment protocols should evaluate not only technical proficiency but also situational judgment and ethical decision-making in AI-assisted contexts.

Phase 4: Regulatory Compliance (Parallel Process)

Engagement with regulatory bodies, including classification societies and flag states, must occur in parallel with system deployment. This includes co-developing certification standards for AI components, establishing algorithmic audit trails, and creating change management protocols for system updates. Compliance must remain adaptive, ensuring AI implementations align with evolving maritime safety and operational regulations.

Phase 5: Operational Refinement (Continuous)

The final phase establishes continuous improvement mechanisms informed by real-world performance data. This includes structured feedback loops with crews, analysis of near-miss and anomalous events, and iterative software updates. Key performance indicators should track both system-level efficiency gains and human-centric metrics, such as reduced cognitive load, improved situational awareness, and enhanced decision accuracy. Organizations must retain the flexibility to revisit earlier phases when scaling to new vessel types or integrating system upgrades.

Across all phases, clearly defined success criteria and transition gates are essential to ensure that technological implementation proceeds in lockstep with crew readiness. A complete fleet-wide rollout typically spans 18–24 months, though continuous adaptation and refinement remain integral throughout the system lifecycle.

5.2. Key Performance Indicators (KPIs)

Table 6 prioritizes and weights critical key performance indicators (KPIs) for human-AI integration in shipping, adding technically essential metrics like model drift and multi-agent coordination. It provides clear benchmarks, scoring scales, and phase alignment to guide decision-making and risk mitigation.

In maturity level, there are some other KPIs to evaluate the competency of the human element, presented in Table 7. The effective operationalization of the proposed competency framework requires a quantitative and qualitative evaluation structure that allows for consistent performance tracking, benchmarking, and continuous improvement. In the context of AI-augmented maritime operations, Key Performance Indicators (KPIs) must address both traditional maritime skill sets and emerging Shipping 4.0 capabilities, capturing dimensions such as technical proficiency, decision-making quality, safety assurance, sustainability compliance, and human–AI collaboration.

This KPI Evaluation Matrix is designed as a dual-purpose tool:

• Framework Function – A structured alignment of competencies to measurable outcomes, enabling comparative analysis across operators, vessels, and organizational units.
• Measurement Package – A standardized scoring and benchmarking system that integrates objective data streams (e.g., system logs, operational performance metrics) and subjective assessments (e.g., peer review, expert evaluation).

KPI Matrix Design Principles

The evaluation system follows five design principles:

• Competency–KPI Alignment – Each KPI directly maps to a defined competency domain in the competency table.
• Mixed-Method Measurement – Integration of quantitative metrics (time-to-decision, system error rate, environmental compliance percentage) with qualitative indicators (leadership adaptability, decision justification quality).
• Benchmark-Driven Scoring – All KPIs are measured against established industry benchmarks (e.g., IMO guidelines, company safety standards, STCW requirements).
• Context-Aware Normalization – KPI results are adjusted for operational context variables such as voyage type, weather severity, and crew experience.
• Continuous Feedback Integration – The evaluation feeds into a reinforcement loop that updates training priorities and AI system design.

Scoring Scale:

• Critical Failure: Does not meet minimum safety or operational standards
• Below Standard: Significant gaps; requires corrective action before phase transition
• Meets Minimum Standard: Acceptable performance; can progress with close monitoring
• Exceeds Standard: Demonstrates strong performance with minor refinements needed
• Best-in-Class: Optimal performance; serves as a benchmark for fleet-wide scaling

6. Conclusions and Future Work

6.1. Key Findings on Technology-Human Alignment

This study has proposed an integrated human–technology readiness framework for implementing Industry 4.0 technologies within Green Digital Shipping Corridors (GDSCs). By combining a five-layer Shipping 4.0 architecture, a comparative analysis of traditional and digitalized maritime systems, and a structured human competency model aligned with STCW standards, the research addresses a critical gap in current GDSC discourse—operational readiness of human actors. Strategic analyses using McKinsey’s 12 Elements of a Dynamic Operating Model and an upgraded TRL–HRL matrix revealed that while technological maturity in areas such as IoT, CPS, and AI is advancing rapidly, human readiness, particularly in trust calibration, AI explainability, cognitive load management, and multi-agent coordination, lags behind.

The proposed competency framework and KPI evaluation matrix provide practical tools for bridging this gap, ensuring that crew members evolve from manual operators to supervisory decision-makers in AI-augmented environments (Emad & Shahbakhsh, 2022b). The phased implementation roadmap emphasizes the need for parallel development of system capability and human proficiency, underpinned by continuous training, regulatory alignment, and performance monitoring.

6.2. Strategic Implications

For Shipping Companies: Adopting AI-augmented operations requires more than system procurement; it demands workforce restructuring, targeted training, and cultural change. Companies should institutionalize “human-on-the-loop” oversight protocols, integrate KPI-based competency monitoring, and phase technology rollouts to match crew proficiency levels. Continuous feedback loops between operators, developers, and management will ensure system adaptability and sustained operational performance.

For Regulators and Policy Makers: Policy frameworks must evolve to certify not only technology but also human readiness. This includes establishing standards for AI transparency, override authority, cyber resilience, and ethical decision-making. Regulators should promote interoperability across corridors and incentivize training programs that integrate sustainability goals with digital competency development.

For Maritime Training Institutions: Curricula should extend beyond conventional STCW competencies to incorporate AI supervision, digital twin interaction, cybersecurity, and alternative fuel handling. Simulation-based, scenario-driven learning environments should reflect real GDSC operational contexts, enabling seafarers to practice both technical execution and ethical judgment in AI-assisted environments.

6.3. Future Research

Future studies should focus on longitudinal validation of the upgraded TRL–HRL framework through real-world GDSC deployments, capturing data on human–machine performance over extended operational periods. Research is also needed on adaptive HMI designs that dynamically adjust to operator expertise and situational demands, as well as on integrating human feedback into AI model retraining pipelines. Comparative analyses of different corridor implementations could yield best practices for global scalability, while socio-economic studies could evaluate the broader impacts of AI-augmented GDSCs on maritime employment patterns and coastal communities.