Peer Review of the United Kingdom Shipbuilding Industry

2. Informational capabilities

Abstract

This chapter reviews the availability and quality of shipbuilding data from both domestic and international perspectives, highlighting key gaps that constrain effective policy design and workforce planning. It explores challenges related to industry classification, data production and knowledge exchange, and emphasises the role of reliable skills data in improving understanding of current and emerging workforce needs.

Data availability is a key issue which impacts knowledge, decision-making, and co-ordination, on international, national, and regional levels. Challenges exist along three domains: industry classification, data production and availability, and knowledge exchange. Addressing these data issues will have considerable benefits for industry and government action.

For the investigation of data capabilities across countries and within the UK, three domains were identified as key:

• Industry classification – ‘what is the definition of the issue I have information on?’ i.e. the definitional scope of the shipbuilding industry, how firms and economic activities are classified, and the approach towards emerging technologies.

• Data production and availability – ‘what information do I have?’ i.e. what information is produced in a systematic way by firms, government bodies, or other organisations, and published in a non-discriminatory fashion?

• Knowledge exchange – ‘who do I share my information with?’ i.e. what and how information is exchanged and organised through various systematic or ad-hoc mechanisms, across industry and government.

A. The cross-country lens

To better understand informational capabilities comparatively, formal industry classifications and accessible data for effective analysis were mapped across Australia, Canada, the People’s Republic of China (hereafter China), Spain, the European Union, the UK, Italy, Japan, South Korea, the Netherlands, Norway, and the United States.

Industry classification. Inconsistent industry definitions limit international comparability. A review of classification systems, as well as of the International Standard Industrial Classification (SIC) system, reveals significant differences in what is considered part of the shipbuilding sector. While ship construction and repair are consistently included, other elements, such as military shipbuilding, marine equipment, design, and port infrastructure, vary widely. These differences are often rooted in national industrial structures and historical context, but they hamper cross-country analysis and risk underrepresenting innovation and emerging technologies in policy assessments.

Data production and availability. Key industry indicators are inconsistently reported, making direct comparisons difficult. Shipbuilding performance was assessed across four indicator categories: input, output, productivity, and performance. Employment indicators vary considerably across countries, and output metrics are reported in different units even by major shipbuilding nations. Clear reporting on productivity is limited, and detailed performance indicators, including innovation and intellectual property measures, are only partially available. International organisations and commercial data providers fill some gaps, but more systematic reporting of indicators can support robust, comparable analysis.

B. The domestic lens

Strong infrastructure exists, but data collection is fragmented and inconsistently used. Despite having well-developed structures for data collection, the UK shipbuilding sector contains systemic areas for improvement. Firm-level classification errors, inconsistencies in the reporting of economic activities, and unco-ordinated efforts between government departments, regional clusters, and industry bodies result in gaps and duplication. While detailed data are collected at regional and sectoral levels, they are not always organised or accessible. Industry associations have become key points of knowledge exchange, but few have made structured, standardised data collection a priority.

Classification of firms and economic activities. Various solutions have been developed to account for challenges regarding firm categorisation, economic activity classification, and data availability. This includes the Data City’s RSIC and RTIC AI-driven adaptations from the SIC system, the Scottish Industry Directories, and taxonomies developed by individual industry associations.

While these initiatives resolve challenges in sub-sector classification, especially in regard to emerging submarkets and technologies, a key issue is diversity which compromises comparability. When different systems of classifications or taxonomies are used, even empirically robust reports may produce analytical findings that differ significantly. In this way, solutions to improve information can also result in conflicting findings. That the choice in taxonomy is one which is often hidden deep within (unread) documentation of methodology can further obfuscate analysis across multiple sources.

Data production and availability. Diversity in data production capabilities and choice of indicators among firms and industry associations impact how well data sourced from across the industry can be harmonised or compared.

Data production capabilities by firms differ greatly and are generally correlated to the firms’ degree of digitalisation: larger defence firms and those engaged in high-end technological capabilities typically have the infrastructure to collect continuous or frequent data points for a wide range of indicators related to monitoring vessel production and operation, factory settings and production processes, financing, and other broader social and economic factors. Conversely, SMEs, particularly those outside of defence or advanced technology submarkets, lack the infrastructure for that scale of data collection. As such, the data which could ultimately feed into various cross-firm collection mechanisms is itself created unevenly among different groups of firms. This limits the extent to which improvement of the data collection mechanism can comprehensively ‘gather’ data from firms.

Knowledge exchange and data sharing. Cluster and other industry associations have become a key node in the flow of knowledge between government and industry, as well as among individual firms. Even though clusters are well situated to gather information from members, few have made data a priority, and many rely on organic information rather than standardised systematic mechanisms.

The data collection capabilities of industry associations differ widely. For example, Maritime-UK Southwest has a database of circa 3,500 businesses through their newsletter. Their informational capabilities rely mostly on the organic use of information rather than more organised collection of data. Other cluster organisations rely on and pay for ad-hoc data collection and analysis through external services. Finally, in related industries, the ADS Group expands on official public data through firms’ memberships submissions – firms directly send information on employment and apprenticeships upon membership registration, as well as turnover on an annual basis – and additional ad-hoc surveys.

One challenge highlighted by multiple stakeholders is competition for information between organisations which can result in survey fatigue. On the other hand, one of the characteristics of best practice examples is a combination of general data collection with verification on the ground.

C. Skills in focus

The lack of harmonised and comprehensive information on skills and skills needs is a significant barrier to ensuring the sector has access to the right skills, in the right place, at the right time. This challenge is particularly acute at a moment when interest in UK shipbuilding has intensified, creating a generational opportunity to align industrial ambition with workforce development and regional growth.

Current data systems are insufficient for effective workforce planning in shipbuilding. Existing metrics struggle to capture the sector’s occupational complexity, regional variation and emerging skills requirements. Standard industrial and occupational classifications, such as SIC and SOC, tend to underestimate employment and obscure critical roles within shipbuilding and related activities. Publicly available datasets are often highly aggregated and uneven in coverage, with notable gaps across Scotland, Wales and Northern Ireland, limiting the ability of policymakers and industry to anticipate and respond to localised skills pressures.

Addressing these limitations requires a more structured approach to assessing skills needs. An effective framework should analyse:

• Skills supply: Track vocational and higher education outputs, apprenticeships, and other qualification metrics.

• Skills demand: Monitor current vacancies and employer needs, while also anticipating future trends driven by technological advancements.

• Skills imbalances and mismatches: Compare available workforce skills against evolving job requirements to highlight both shortages and areas of over-qualification.

• Clusters: Examine geographical clusters (i.e. shipbuilding clusters and related industry clusters) to understand current and future skills availability.

• Future skills demand: Conduct skills foresighting and forecasting to understand how evolving technological advances, market trends and policy trends will affect future occupational demand.

The Shipbuilding Skills Needs Framework developed in Report II provides a structured response to these challenges, setting out methodologies that can be applied flexibly by organisations with differing analytical capacities. By combining quantitative indicators with occupational foresight and data gap analysis, the framework enables more granular, evidence-based workforce planning and clarifies both existing data coverage and remaining information gaps.

Sector collaboration and career visibility must improve. Improved data alone, however, is not sufficient. Stronger co-ordination across government, industry and education remains essential, including through existing mechanisms such as the Shipbuilding Skills Development Group. Greater clarity around career pathways, simplified qualification frameworks and more accessible training funding can help increase engagement, particularly among SMEs. At the same time, strengthening the visibility of shipbuilding as a technologically advanced, digital and decarbonising industry is critical to attracting younger and more diverse talent.

A resilient, future-ready shipbuilding workforce will not emerge by chance. It requires strategic planning, improved data, and targeted interventions that respond to both present and future needs. This report supports the National Shipbuilding Office and its partners in moving from reactive responses towards a more proactive, co-ordinated workforce strategy, helping to sustain the competitiveness, innovation and resilience of the UK shipbuilding sector over the coming decades.

D. Impact on analysis of opportunities and challenges

Significant gaps in data availability have impacted both the comparative analysis across peer competitors and the domestic analysis. This is a challenge covered further in the two companion reports on data and skills.

Because of the above identified challenges in data, there are certain limitations to the findings in the next sections. For the comparative analysis of opportunities, data availability has most strongly impacted findings for smaller vessels and for emerging subspecialisations. When combining data from two different providers – one with more comprehensive data on vessels above 100 GT, the other on vessels below 100 GT – because of differences in vessel and sub-sector categorisation, some judgment calls had to be made. Additionally, for aspects like ship design, autonomous vessels, and shipbuilding innovation, no official data sources provide sufficient information for quantitative trend or comparative analysis. For identification of market and government failures the analysis was primarily qualitative; the availability of empirical evidence to support presence or absence of challenges, not least to identify and quantify impacts of certain variables on the market, was relatively limited.