Autonomy in Dry Docking: Engineering Pathways, Operational Realities, and Strategic Imperatives
The MOC
By
David Von Schmidt
May 21, 2026
Introduction
What is autonomy in dry docking? Until recently, the question itself would have seemed misplaced in most shipyards. Dry docking has traditionally been viewed as one of the most human-dependent evolutions in the maritime industry: an operation built upon judgment, experience, timing, and coordination under constantly changing conditions. Unlike highly repetitive industrial processes, dry docking involves variables that are often dynamic, imperfectly known, and operationally interdependent.
For decades, the prevailing industry assumption was that dry docking was simply too complex to automate in any meaningful way. Shipyard executives and naval leadership frequently sought methods to improve efficiency, reduce delays, and increase throughput, particularly as maintenance demands expanded. Yet the response was often the same: the operation was too variable, too dependent upon human interpretation, and too unforgiving of error to permit substantial autonomous integration.
That assumption is now being challenged.
Autonomy in dry docking is not emerging as a wholesale replacement for dockmasters, engineers, riggers, or shipyard personnel. Rather, it is developing incrementally through specialized systems capable of automating discrete operational functions with increasing precision and reliability. Automated ballast control systems, synchronized shiplift mechanisms, AI-assisted planning tools, and digitally integrated transfer systems are already altering the operational landscape of modern shipyards.
At the same time, the adoption of autonomous technologies raises legitimate engineering and operational concerns. Questions surrounding system redundancy, cybersecurity, data integrity, overreliance on automation, workforce competency degradation, and operational resiliency remain unresolved in many areas. The issue is whether autonomy is sufficiently mature to improve operations safely and where human judgment must continue to remain dominant.
This paper evaluates the current state of autonomy in dry docking, distinguishing between technologies that are operationally mature and those that remain developmental. It further examines the strategic pressures driving adoption, the risks associated with technological stagnation, and the criteria necessary for responsible implementation within the shipyard and dry docking sector.
Defining Autonomy in Dry Docking
A critical distinction must be made between automation and autonomy. Many systems currently described within the shipyard sector as “autonomous” are more accurately categorized as advanced automation. Automated systems execute pre-programmed functions within defined operational parameters. Autonomous systems, by contrast, interpret changing conditions, adapt to uncertainty, and make conditional operational decisions with reduced human intervention. For example, in a floating dry dock operation, an automated ballast control system may simply flood and dewater tanks according to a pre-set docking sequence, whereas an autonomous docking system could detect an unexpected vessel list, changing tide conditions, or uneven block loading and independently adjust ballast distribution and docking rates in real time to maintain vessel stability and dock safety.
Most existing dry docking technologies remain firmly within the realm of automation rather than full autonomy. This distinction is important because dry docking operations frequently involve complex, nonlinear conditions that resist deterministic programming. Variations in vessel condition, loading irregularities, weather effects, degraded infrastructure, sensor faults, or inaccurate hydrostatic assumptions can rapidly alter an evolution beyond programmed expectations.
As a result, truly autonomous dry docking operations remain limited by the industry’s inability to fully digitize and predict real-world operational variability. Human operators continue to serve as the primary mechanism for contextual interpretation and risk management when conditions deviate from expected parameters.
Autonomy in dry docking should not be misconstrued as the wholesale removal of human operators. Rather, it is best understood as the integration of automated and semi-autonomous control systems that reduce manual intervention, enhance precision, and provide decision-support capabilities.
From an engineering standpoint, autonomy exists along a spectrum:
Assisted Operations: Human-controlled systems augmented with automation (e.g., digital ballast monitoring). These are simple systems with few elements, variables and states. Checklists and stepwise procedures can be applied by anyone including automation to solve problems.
Supervised Autonomy: Automated subsystems executing tasks under human oversight (e.g., programmable ballast sequences). These are complicated systems with a large number of elements, variables and states, but like simple systems have been designed and assembled.
Conditional Autonomy: Systems capable of executing complex operations with limited human input, intervening only when required. These are complex systems with any variables that are tightly inter related. Small changes can have outsize and unpredictable impacts.
Current dry docking technologies largely occupy Assisted Operations and Supervised Autonomy, with emerging capabilities approaching Conditional Autonomy in specific domains.
Existing Autonomous Technologies in Dry Docking
Automated Ballast Control Systems
Modern floating dry docks (FDDs) are increasingly equipped with advanced automated ballast control systems. These systems enable precise control over ballast tank filling and discharge, allowing for:
Real-time trim and heel adjustments
Pre-programmed docking sequences
Integrated sensor feedback loops
Despite their advantages, automated ballast systems remain heavily dependent upon sensor reliability, calibration accuracy, and operator validation. Faulty tank level indications, corrupted inputs, valve failures, or inaccurate vessel loading assumptions can create cascading operational risks if operators become overly reliant on automated outputs. Consequently, mature ballast automation systems are most effective when implemented as decision-support and execution tools operating under experienced human supervision rather than as fully independent control systems.
Courtesy: IDAC West (2023) Automated Docking and Ballast Control Systems (ADECS) Product Documentation.
This reflects an important operational reality: automation can reduce human workload, but it does not eliminate responsibility or accountability from the docking authority.
From an engineering perspective, these automated ballast control systems reduce the risk of asymmetric loading and human error during critical phases of the docking evolution. By continuously monitoring tank levels, pressures, and structural responses, the system can execute corrections far faster than manual operations.
The result is a measurable improvement in docking cycle time, repeatability, and safety margins.
Vertical Shiplift Systems and Synchronized Hoist Mechanisms
Vertical ship-lifts represent one of the most mature implementations of autonomy in dry docking infrastructure. Systems developed and deployed by ship-lift manufacturers utilize synchronized chain-jack mechanisms to lift vessels with exceptional precision.
Key characteristics include:
Distributed load management across multiple lifting points
Courtesy: Bardex Corporation (2024) Shiplift and Transfer Systems Technical Overview. OmniLift®: Shiplift by Bardex – Bardex CorporationCourtesy: Bardex Corporation (2024) Shiplift and Transfer Systems Technical Overview. OmniLift®: Shiplift by Bardex – Bardex Corporation
These systems effectively eliminate the need for manual load balancing during lifting operations. Engineering tolerances are maintained through digital control systems that continuously adjust each lifting station in real time.
From a risk standpoint, this significantly reduces the likelihood of structural overstress or uneven support conditions, which are historically among the most critical hazards in dry docking.
Automated Transfer Systems
The movement of vessels into and out of dry docks—whether via rail transfer systems, self-propelled modular transporters (SPMTs), or winch-driven translation systems—has also seen increasing automation.
Modern transfer systems incorporate:
Programmable logic controllers (PLCs)
Laser alignment and positioning systems
Automated winch tensioning and line management
These technologies enable precise vessel positioning with minimal manual intervention. In high-throughput facilities, automated transfer systems reduce bottlenecks and improve overall yard productivity.
Importantly, they also enhance safety by minimizing personnel exposure to high-risk zones during vessel movement.
AI-Driven Load Monitoring and Predictive Analytics
Emerging applications of artificial intelligence are beginning to address one of the most complex aspects of dry docking: the management of liquid and dry loads aboard the vessel.
AI-based systems are being developed to:
Model weight distribution in real time
Predict structural responses to loading conditions
Identify anomalies or unsafe configurations
Modern tech firms are advancing digital twin technologies that integrate vessel data, dock geometry, and operational constraints into unified planning platforms.
However, AI-driven load monitoring remains an emerging capability rather than a fully mature operational standard. Machine-learning systems are inherently dependent upon the quality, completeness, and accuracy of historical and real-time data inputs. Many shipyards continue to operate with incomplete vessel data, inconsistent digitization standards, and aging infrastructure that limit the reliability of predictive models.
Additionally, dry docking incidents are relatively infrequent but often highly consequential events, meaning available failure datasets are limited. This constrains the ability of AI systems to reliably predict low-frequency, high-impact operational anomalies. As such, AI currently functions most effectively as a supplemental analytical tool rather than a replacement for engineering review and dockmaster judgment.
Courtesy: Space Grid AI (2025) Digital Twin Applications in Shipyard Planning and Operations. Technology — Space Grid AI
3D Block Planning and Digital Layout Systems
The adoption of 3D block planning tools marks a significant advancement in pre-docking preparation. These systems allow engineers to:
Design optimized blocking arrangements
Simulate vessel landing conditions
Validate structural load paths
By digitizing the planning phase, shipyards can reduce uncertainty and improve execution accuracy. This is particularly valuable for complex vessels with non-uniform hull geometries.
Integration with broader digital ecosystems further enhances coordination between engineering, operations, and management teams.
Underwater Optical Systems and Laser-Assisted Positioning
Another emerging technology in the dry docking sector involves the use of underwater optical systems, laser positioning equipment, and integrated visual monitoring networks to assist dockmasters during vessel positioning and landing operations. These systems utilize subsea cameras, low-light imaging, laser reference tools, and real-time monitoring displays to improve underwater situational awareness and enhance positioning accuracy during critical phases of the docking evolution.
While still relatively new to the dry docking industry, these technologies are actively being implemented and tested in the maritime sector. Environmental continue to present operational challenges, and the systems currently function primarily as decision-support tools rather than autonomous control systems. Even so, this technology shows considerable promise in improving operational awareness, reducing uncertainty, and enhancing communication during docking evolutions.
If ongoing testing and operational validation continue to prove successful, underwater optical and laser-assisted positioning systems will likely become increasingly common throughout the industry and may eventually replace portions of more conventional positioning and verification methods presently relied upon during dry docking operations.
The move toward autonomy in dry docking is not occurring in a vacuum. It is being accelerated by two converging pressures:
Expanding Fleet Requirements:
The U.S. Navy and commercial sectors are both experiencing increased demand for shipbuilding and maintenance capacity.
Workforce Limitations:
The industry faces a well-documented shortage of skilled labor, compounded by a gap in formalized training programs for dry docking professionals.
Autonomous systems offer a means of bridging this gap by amplifying the dry dock crew’s effectiveness. An experienced dockmaster and crew, supported by advanced control systems, can oversee operations that would have previously required a significantly larger team.
Human Capital and Competency Risks
One of the more complex tradeoffs associated with autonomy is the potential erosion of operational competency within the workforce itself. Historically, dry docking expertise has been developed through direct operational exposure, mentorship, and repetition under experienced personnel. Excessive reliance on automated systems risks reducing opportunities for developing foundational judgment and problem-solving skills among younger generations of dockmasters and engineers.
This concern is particularly significant in casualty scenarios or degraded operating conditions where autonomous systems may fail, provide conflicting outputs, or encounter circumstances outside programmed expectations. In such situations, shipyards remain dependent upon personnel capable of reverting to first-principles decision making under pressure.
Accordingly, autonomous systems should not be viewed solely as labor-reduction tools. Their implementation must also support competency development by improving training environments, simulation capability, operational visibility, and knowledge transfer within the workforce.
Risk Analysis: The Cost of Inaction
Failure to adopt autonomous technologies in dry docking and shipyard operations carries significant operational, commercial, and strategic risks. Operational inefficiency remains one of the most immediate consequences, as manual processes inherently limit throughput, constrain scalability, and reduce a shipyard’s ability to consistently meet increasingly demanding production and maintenance schedules. In practice, this often results in longer docking evolutions, inefficient labor utilization, and reduced overall facility capacity when compared to more technologically advanced competitors.
Safety exposure also remains a critical concern, as human error continues to be a leading contributing factor in dry docking incidents involving ballast control, vessel positioning, communications, and sequencing during complex evolutions. While experienced personnel remain indispensable, autonomous support systems have the potential to reduce risk by continuously monitoring conditions, identifying anomalies, and assisting decision-making in real time during high-consequence operations.
Competitive disadvantage is another growing risk, particularly as international shipyards continue making substantial investments in automation, robotics, digital twins, and AI-assisted operational systems designed to improve efficiency, precision, and cost control. Shipyards that fail to modernize may find themselves increasingly unable to compete for commercial or government work where schedule reliability, production speed, and technological capability are becoming decisive factors in contract awards.
Strategic vulnerability must also be considered, especially within the context of national defense and maritime industrial base readiness. Insufficient shipyard capacity, outdated infrastructure, and inefficient docking operations directly affect the ability to repair, modernize, and return naval and commercial vessels to service in a timely manner, ultimately impacting fleet readiness and national resiliency during periods of crisis or sustained operational demand.
From an engineering management perspective, the question is no longer whether autonomy should be adopted, but rather how rapidly and effectively these technologies can be integrated into shipyard operations while maintaining the rigorous safety, reliability, and operational standards required in the dry docking sector.
Criteria for Responsible Adoption
The implementation of autonomous technologies in dry docking must be governed by clear, objective criteria:
Safety Enhancement:
Systems must demonstrably reduce risk to personnel, infrastructure, and vessels.
Operational Efficiency:
Technologies should improve cycle times, reduce variability, and increase throughput.
Reliability and Redundancy:
Autonomous systems must include fail-safes and manual override capabilities.
Training Integration:
Adoption should contribute to the development of the next generation of dock masters and engineers, not erode core competencies.
Proven Performance:
Technologies must be thoroughly tested and validated under real-world conditions before widespread deployment.
A cautious but proactive approach that balances innovation with the inherent risks of complex maritime operations is essential.
Conclusion
Autonomy in dry docking is neither a speculative concept nor a fully realized operational state. Certain technologies—such as synchronized shiplift controls, automated ballast sequencing, and digitally integrated transfer systems—have reached a level of operational maturity capable of delivering measurable improvements in safety, repeatability, and efficiency. Other technologies, particularly AI-driven predictive systems and higher-order autonomous decision-making tools, remain developmental and should continue to be adopted with a measured approach.
The industry faces a dual obligation. It must continue modernizing its infrastructure and operational practices to support the growing demands of naval and commercial fleet maintenance, while simultaneously ensuring that technological adoption does not outpace operational understanding, workforce competency, or risk management capability.
Failure to evolve presents its own strategic risk. Increasing maintenance demands, workforce shortages, infrastructure constraints, and rising operational complexity require shipyards to improve throughput and precision wherever responsibly possible. At the same time, overreliance on poorly validated autonomous systems introduces vulnerabilities that may undermine the very efficiencies being pursued.
The most effective path forward is neither resistance to technological change nor uncritical acceptance of it. Rather, it is the disciplined integration of technologies that demonstrably enhance safety, improve operational effectiveness, strengthen workforce capability, and preserve human authority over complex maritime operations.
The dry docking profession has historically adapted to every major technological transition within the maritime industry. The present transition toward increasingly autonomous systems is likely to be no different. If implemented responsibly, these technologies possess the potential to strengthen shipyard resiliency, improve fleet readiness, and better position the maritime industry to support the operational demands of the decades ahead.The maritime industry is entering an era in which advanced engineering tools, digital systems, and operational experience can be combined in ways previously unavailable. The long-term success of autonomy in dry docking will depend not upon replacing human expertise, but upon using technology to reinforce and extend it.
David Von Schmidt is the Founder and Managing Director of VSM Associates.
The views expressed in this piece are the sole opinions of the author and do not necessarily reflect those of the Center for Maritime Strategy or other institutions listed.
References
IDAC West (2023) Automated Docking and Ballast Control Systems (ADECS) Product Documentation.
Space Grid AI (2025) Digital Twin Applications in Shipyard Planning and Operations.
United States Navy (2022) Shipyard Infrastructure Optimization Program (SIOP) Report.
National Shipbuilding Research Program (NSRP) (2021) Best Practices in Dry Dock Operations.
International Maritime Organization (IMO) (2020) Guidelines on Maritime Autonomous Surface Ships (MASS).
Bardex Corporation (2024) Shiplift and Transfer Systems Technical Overview.
Being Human in Safety Critical Organizations by Dik Gregory and Paul Shanahan
Center for Maritime Strategy
By David Von Schmidt
Introduction
What is autonomy in dry docking? Until recently, the question itself would have seemed misplaced in most shipyards. Dry docking has traditionally been viewed as one of the most human-dependent evolutions in the maritime industry: an operation built upon judgment, experience, timing, and coordination under constantly changing conditions. Unlike highly repetitive industrial processes, dry docking involves variables that are often dynamic, imperfectly known, and operationally interdependent.
For decades, the prevailing industry assumption was that dry docking was simply too complex to automate in any meaningful way. Shipyard executives and naval leadership frequently sought methods to improve efficiency, reduce delays, and increase throughput, particularly as maintenance demands expanded. Yet the response was often the same: the operation was too variable, too dependent upon human interpretation, and too unforgiving of error to permit substantial autonomous integration.
That assumption is now being challenged.
Autonomy in dry docking is not emerging as a wholesale replacement for dockmasters, engineers, riggers, or shipyard personnel. Rather, it is developing incrementally through specialized systems capable of automating discrete operational functions with increasing precision and reliability. Automated ballast control systems, synchronized shiplift mechanisms, AI-assisted planning tools, and digitally integrated transfer systems are already altering the operational landscape of modern shipyards.
At the same time, the adoption of autonomous technologies raises legitimate engineering and operational concerns. Questions surrounding system redundancy, cybersecurity, data integrity, overreliance on automation, workforce competency degradation, and operational resiliency remain unresolved in many areas. The issue is whether autonomy is sufficiently mature to improve operations safely and where human judgment must continue to remain dominant.
This paper evaluates the current state of autonomy in dry docking, distinguishing between technologies that are operationally mature and those that remain developmental. It further examines the strategic pressures driving adoption, the risks associated with technological stagnation, and the criteria necessary for responsible implementation within the shipyard and dry docking sector.
Defining Autonomy in Dry Docking
A critical distinction must be made between automation and autonomy. Many systems currently described within the shipyard sector as “autonomous” are more accurately categorized as advanced automation. Automated systems execute pre-programmed functions within defined operational parameters. Autonomous systems, by contrast, interpret changing conditions, adapt to uncertainty, and make conditional operational decisions with reduced human intervention. For example, in a floating dry dock operation, an automated ballast control system may simply flood and dewater tanks according to a pre-set docking sequence, whereas an autonomous docking system could detect an unexpected vessel list, changing tide conditions, or uneven block loading and independently adjust ballast distribution and docking rates in real time to maintain vessel stability and dock safety.
Most existing dry docking technologies remain firmly within the realm of automation rather than full autonomy. This distinction is important because dry docking operations frequently involve complex, nonlinear conditions that resist deterministic programming. Variations in vessel condition, loading irregularities, weather effects, degraded infrastructure, sensor faults, or inaccurate hydrostatic assumptions can rapidly alter an evolution beyond programmed expectations.
As a result, truly autonomous dry docking operations remain limited by the industry’s inability to fully digitize and predict real-world operational variability. Human operators continue to serve as the primary mechanism for contextual interpretation and risk management when conditions deviate from expected parameters.
Autonomy in dry docking should not be misconstrued as the wholesale removal of human operators. Rather, it is best understood as the integration of automated and semi-autonomous control systems that reduce manual intervention, enhance precision, and provide decision-support capabilities.
From an engineering standpoint, autonomy exists along a spectrum:
Current dry docking technologies largely occupy Assisted Operations and Supervised Autonomy, with emerging capabilities approaching Conditional Autonomy in specific domains.
Existing Autonomous Technologies in Dry Docking
Modern floating dry docks (FDDs) are increasingly equipped with advanced automated ballast control systems. These systems enable precise control over ballast tank filling and discharge, allowing for:
Despite their advantages, automated ballast systems remain heavily dependent upon sensor reliability, calibration accuracy, and operator validation. Faulty tank level indications, corrupted inputs, valve failures, or inaccurate vessel loading assumptions can create cascading operational risks if operators become overly reliant on automated outputs. Consequently, mature ballast automation systems are most effective when implemented as decision-support and execution tools operating under experienced human supervision rather than as fully independent control systems.
This reflects an important operational reality: automation can reduce human workload, but it does not eliminate responsibility or accountability from the docking authority.
From an engineering perspective, these automated ballast control systems reduce the risk of asymmetric loading and human error during critical phases of the docking evolution. By continuously monitoring tank levels, pressures, and structural responses, the system can execute corrections far faster than manual operations.
The result is a measurable improvement in docking cycle time, repeatability, and safety margins.
Vertical ship-lifts represent one of the most mature implementations of autonomy in dry docking infrastructure. Systems developed and deployed by ship-lift manufacturers utilize synchronized chain-jack mechanisms to lift vessels with exceptional precision.
Key characteristics include:
These systems effectively eliminate the need for manual load balancing during lifting operations. Engineering tolerances are maintained through digital control systems that continuously adjust each lifting station in real time.
From a risk standpoint, this significantly reduces the likelihood of structural overstress or uneven support conditions, which are historically among the most critical hazards in dry docking.
The movement of vessels into and out of dry docks—whether via rail transfer systems, self-propelled modular transporters (SPMTs), or winch-driven translation systems—has also seen increasing automation.
Modern transfer systems incorporate:
These technologies enable precise vessel positioning with minimal manual intervention. In high-throughput facilities, automated transfer systems reduce bottlenecks and improve overall yard productivity.
Importantly, they also enhance safety by minimizing personnel exposure to high-risk zones during vessel movement.
Courtesy: Transfer Systems – Bardex Corporation
Emerging applications of artificial intelligence are beginning to address one of the most complex aspects of dry docking: the management of liquid and dry loads aboard the vessel.
AI-based systems are being developed to:
Modern tech firms are advancing digital twin technologies that integrate vessel data, dock geometry, and operational constraints into unified planning platforms.
However, AI-driven load monitoring remains an emerging capability rather than a fully mature operational standard. Machine-learning systems are inherently dependent upon the quality, completeness, and accuracy of historical and real-time data inputs. Many shipyards continue to operate with incomplete vessel data, inconsistent digitization standards, and aging infrastructure that limit the reliability of predictive models.
Additionally, dry docking incidents are relatively infrequent but often highly consequential events, meaning available failure datasets are limited. This constrains the ability of AI systems to reliably predict low-frequency, high-impact operational anomalies. As such, AI currently functions most effectively as a supplemental analytical tool rather than a replacement for engineering review and dockmaster judgment.
The adoption of 3D block planning tools marks a significant advancement in pre-docking preparation. These systems allow engineers to:
By digitizing the planning phase, shipyards can reduce uncertainty and improve execution accuracy. This is particularly valuable for complex vessels with non-uniform hull geometries.
Integration with broader digital ecosystems further enhances coordination between engineering, operations, and management teams.
Courtesy: Digital Twin Marine What We Do — Digital Twin Marine
Another emerging technology in the dry docking sector involves the use of underwater optical systems, laser positioning equipment, and integrated visual monitoring networks to assist dockmasters during vessel positioning and landing operations. These systems utilize subsea cameras, low-light imaging, laser reference tools, and real-time monitoring displays to improve underwater situational awareness and enhance positioning accuracy during critical phases of the docking evolution.
While still relatively new to the dry docking industry, these technologies are actively being implemented and tested in the maritime sector. Environmental continue to present operational challenges, and the systems currently function primarily as decision-support tools rather than autonomous control systems. Even so, this technology shows considerable promise in improving operational awareness, reducing uncertainty, and enhancing communication during docking evolutions.
If ongoing testing and operational validation continue to prove successful, underwater optical and laser-assisted positioning systems will likely become increasingly common throughout the industry and may eventually replace portions of more conventional positioning and verification methods presently relied upon during dry docking operations.
Courtesy: SIDUS Solutions (2025) Subsea Cameras and Lasers Subsea Products | Sidus Solutions
Efficiency Imperatives and Industry Constraints
The move toward autonomy in dry docking is not occurring in a vacuum. It is being accelerated by two converging pressures:
The U.S. Navy and commercial sectors are both experiencing increased demand for shipbuilding and maintenance capacity.
The industry faces a well-documented shortage of skilled labor, compounded by a gap in formalized training programs for dry docking professionals.
Autonomous systems offer a means of bridging this gap by amplifying the dry dock crew’s effectiveness. An experienced dockmaster and crew, supported by advanced control systems, can oversee operations that would have previously required a significantly larger team.
Human Capital and Competency Risks
One of the more complex tradeoffs associated with autonomy is the potential erosion of operational competency within the workforce itself. Historically, dry docking expertise has been developed through direct operational exposure, mentorship, and repetition under experienced personnel. Excessive reliance on automated systems risks reducing opportunities for developing foundational judgment and problem-solving skills among younger generations of dockmasters and engineers.
This concern is particularly significant in casualty scenarios or degraded operating conditions where autonomous systems may fail, provide conflicting outputs, or encounter circumstances outside programmed expectations. In such situations, shipyards remain dependent upon personnel capable of reverting to first-principles decision making under pressure.
Accordingly, autonomous systems should not be viewed solely as labor-reduction tools. Their implementation must also support competency development by improving training environments, simulation capability, operational visibility, and knowledge transfer within the workforce.
Risk Analysis: The Cost of Inaction
Failure to adopt autonomous technologies in dry docking and shipyard operations carries significant operational, commercial, and strategic risks. Operational inefficiency remains one of the most immediate consequences, as manual processes inherently limit throughput, constrain scalability, and reduce a shipyard’s ability to consistently meet increasingly demanding production and maintenance schedules. In practice, this often results in longer docking evolutions, inefficient labor utilization, and reduced overall facility capacity when compared to more technologically advanced competitors.
Safety exposure also remains a critical concern, as human error continues to be a leading contributing factor in dry docking incidents involving ballast control, vessel positioning, communications, and sequencing during complex evolutions. While experienced personnel remain indispensable, autonomous support systems have the potential to reduce risk by continuously monitoring conditions, identifying anomalies, and assisting decision-making in real time during high-consequence operations.
Competitive disadvantage is another growing risk, particularly as international shipyards continue making substantial investments in automation, robotics, digital twins, and AI-assisted operational systems designed to improve efficiency, precision, and cost control. Shipyards that fail to modernize may find themselves increasingly unable to compete for commercial or government work where schedule reliability, production speed, and technological capability are becoming decisive factors in contract awards.
Strategic vulnerability must also be considered, especially within the context of national defense and maritime industrial base readiness. Insufficient shipyard capacity, outdated infrastructure, and inefficient docking operations directly affect the ability to repair, modernize, and return naval and commercial vessels to service in a timely manner, ultimately impacting fleet readiness and national resiliency during periods of crisis or sustained operational demand.
From an engineering management perspective, the question is no longer whether autonomy should be adopted, but rather how rapidly and effectively these technologies can be integrated into shipyard operations while maintaining the rigorous safety, reliability, and operational standards required in the dry docking sector.
Criteria for Responsible Adoption
The implementation of autonomous technologies in dry docking must be governed by clear, objective criteria:
Systems must demonstrably reduce risk to personnel, infrastructure, and vessels.
Technologies should improve cycle times, reduce variability, and increase throughput.
Autonomous systems must include fail-safes and manual override capabilities.
Adoption should contribute to the development of the next generation of dock masters and engineers, not erode core competencies.
Technologies must be thoroughly tested and validated under real-world conditions before widespread deployment.
A cautious but proactive approach that balances innovation with the inherent risks of complex maritime operations is essential.
Conclusion
Autonomy in dry docking is neither a speculative concept nor a fully realized operational state. Certain technologies—such as synchronized shiplift controls, automated ballast sequencing, and digitally integrated transfer systems—have reached a level of operational maturity capable of delivering measurable improvements in safety, repeatability, and efficiency. Other technologies, particularly AI-driven predictive systems and higher-order autonomous decision-making tools, remain developmental and should continue to be adopted with a measured approach.
The industry faces a dual obligation. It must continue modernizing its infrastructure and operational practices to support the growing demands of naval and commercial fleet maintenance, while simultaneously ensuring that technological adoption does not outpace operational understanding, workforce competency, or risk management capability.
Failure to evolve presents its own strategic risk. Increasing maintenance demands, workforce shortages, infrastructure constraints, and rising operational complexity require shipyards to improve throughput and precision wherever responsibly possible. At the same time, overreliance on poorly validated autonomous systems introduces vulnerabilities that may undermine the very efficiencies being pursued.
The most effective path forward is neither resistance to technological change nor uncritical acceptance of it. Rather, it is the disciplined integration of technologies that demonstrably enhance safety, improve operational effectiveness, strengthen workforce capability, and preserve human authority over complex maritime operations.
The dry docking profession has historically adapted to every major technological transition within the maritime industry. The present transition toward increasingly autonomous systems is likely to be no different. If implemented responsibly, these technologies possess the potential to strengthen shipyard resiliency, improve fleet readiness, and better position the maritime industry to support the operational demands of the decades ahead.The maritime industry is entering an era in which advanced engineering tools, digital systems, and operational experience can be combined in ways previously unavailable. The long-term success of autonomy in dry docking will depend not upon replacing human expertise, but upon using technology to reinforce and extend it.
David Von Schmidt is the Founder and Managing Director of VSM Associates.
The views expressed in this piece are the sole opinions of the author and do not necessarily reflect those of the Center for Maritime Strategy or other institutions listed.
References
IDAC West (2023) Automated Docking and Ballast Control Systems (ADECS) Product Documentation.
Space Grid AI (2025) Digital Twin Applications in Shipyard Planning and Operations.
United States Navy (2022) Shipyard Infrastructure Optimization Program (SIOP) Report.
National Shipbuilding Research Program (NSRP) (2021) Best Practices in Dry Dock Operations.
International Maritime Organization (IMO) (2020) Guidelines on Maritime Autonomous Surface Ships (MASS).
Bardex Corporation (2024) Shiplift and Transfer Systems Technical Overview.
Being Human in Safety Critical Organizations by Dik Gregory and Paul Shanahan
SIDUS Solutions (2025) Subsea Cameras and Lasers
Digital Twin Marine What We Do — Digital Twin Marine