The impact of digitalization on shipbuilding as measured by Artificial Intelligence (AI) maturity models: a systematic review
Volume 10, Issue 3, Page No 15-20, 2025
Author’s Name: Dharmender Salian 1 *, Geeta Sandeep Nadella 1, Gasan Elkhodari 2, Rabih Neouchi 2, Steven Brown 1, Eduard Babulak 3, Raed Sbeit 1
View Affiliations
1 University of the Cumberlands, Williamsburg, KY 40769, USA
2 Naveen Jindal School of Management Business School, The University of Texas at Dallas, Richardson, TX 75080, USA
3 National Science Foundation, Alexandria, VA 22314, USA
a)whom correspondence should be addressed. E-mail: htaguchi68@gmail.com
Adv. Sci. Technol. Eng. Syst. J. 10(3), 15-20 (2025); DOI: 10.25046/aj100303
Keywords: Digitalization, Shipping, Transformation, Technologies, Maturity
Export Citations
Artificial Intelligence (AI) is reshaping the global shipbuilding sector, yet existing maturity models fail to capture the domain-specific complexities of this capital-intensive industry. This study reviews over 50 AI maturity models and introduces a specialized framework tailored for shipbuilding. The proposed model outlines four progressive stages—Beginner, Innovation, Integration, and Expert—across eight key dimensions: culture, resilience, sustainability, strategy, customer focus, organizational integration, connectivity, and production efficiency. A hybrid benchmarking approach involving comparative analysis of major shipbuilders such as China State Shipbuilding Corporation(CSSC), General Dynamics National Steel and Shipbuilding Company(NASSCO), and Hyundai Heavy Industries(HHI), as well as synthesis from literature, was used to validate the relevance and coverage of each dimension. The framework provides a roadmap for operational modernization and links digital maturity to measurable outcomes such as delivery timelines, production scalability, and environmental performance. Policy recommendations highlight the need for targeted investments, workforce reskilling, and public-private collaboration to enable sustainable and AI-enabled growth in the U.S. shipbuilding sector.
Received: 02 February 2025 Revised: 17 April 2025 Accepted: 18 April 2025 Online: 22 May 2025
- Y.-G. Lee, C.-H. Lee, Y.-H. Jeon, and J.-H. Bae, “Transformative Impact of the EU AI Act on Maritime Autonomous Surface Ships,” Laws, vol. 13, no. 5, p. 61, Sep. 2024, doi: 10.3390/laws13050061.
- P. C. Hong, Y. S. Park, D. W. Hwang, and M. J. Sepehr, “A growth theory perspective on the competitive landscape of shipbuilding: a comparative study of Japan, Korea, and China,” Maritime Economics & Logistics, vol. 26, no. 3, pp. 462–489, Sep. 2024, doi: 10.1057/s41278-023-00279-5.
- B. F. Socoliuc, A. A. Suciu, M. E. Popescu, D. A. Plesea, and F. Nicolae, “Shipyard Manpower Digital Recruitment: A Data-Driven Approach for Norwegian Stakeholders,” Economies, vol. 13, no. 1, p. 16, Jan. 2025, doi: 10.3390/economies13010016.
- “The Big Data bandwagon,” Strategic Direction, vol. 36, no. 10, pp. 13–14, Sep. 2020, doi: 10.1108/SD-08-2020-0144.
- A. Cakir, Ö. Akın, H. F. Deniz, and A. Yılmaz, “Enabling real time big data solutions for manufacturing at scale,” J Big Data, vol. 9, no. 1, p. 118, Dec. 2022, doi: 10.1186/s40537-022-00672-6.
- G. Cappellesso and K. M. Thomé, “Technological innovation in food supply chains: systematic literature review,” British Food Journal, vol. ahead-of-print, no. ahead-of-print, Aug. 2019, doi: 10.1108/BFJ-03-2019-0160.
- L. Jiang and S. P. Strandenes, “Assessing the cost competitiveness of China’s shipbuilding industry,” Maritime Economics & Logistics, vol. 14, no. 4, pp. 480–497, Dec. 2012, doi: 10.1057/mel.2012.17.
- World Bank, “Skilled Labor Force and Industrial Transformation,” Global Competitiveness Report.
- D. Nadolny and M. Block, “Labor cost structures and competitiveness in U.S. shipbuilding,” International Journal of Maritime Engineering, vol. 165, no. A2, pp. 123–134, 2021.
- F. K, “Legacy Systems and Interoperability in Large-Scale Engineering Projects: A Case Study of U.S. Shipyards,” System Engineering, vol. 24, no. 3, pp. 210–225, 2022.
- A. Martins, “Dynamic capabilities and SME performance in the COVID-19 era: the moderating effect of digitalization,” Asia-Pacific Journal of Business Administration, vol. 15, no. 2, pp. 188–202, Feb. 2023, doi: 10.1108/APJBA-08-2021-0370.
- E. Omol, P. Abuonji, and L. Mburu, “SMEs’ digital maturity: analyzing influencing factors and the mediating role of environmental factors,” Journal of Innovative Digital Transformation, vol. 2, no. 1, pp. 19–36, Mar. 2025, doi: 10.1108/JIDT-01-2024-0002.
- M. Khraiwesh, “Measures of Organizational Training in the Capability Maturity Model Integration (CMMI),” International Journal of Advanced Computer Science and Applications, vol. 11, no. 2, 2020, doi: 10.14569/IJACSA.2020.0110274.
- S. MOUHIB, H. ANOUN, M. RIDOUANI, and L. HASSOUNI, “Towards a Global Big Data Maturity Model,” in 2020 Fourth International Conference On Intelligent Computing in Data Sciences (ICDS), IEEE, Oct. 2020, pp. 1–5. doi: 10.1109/ICDS50568.2020.9268720.
- J. Hu and S. Gao, “Research and Application of Capability Maturity Model for Chinese Intelligent Manufacturing,” Procedia CIRP, vol. 83, pp. 794–799, 2019, doi: 10.1016/j.procir.2019.05.013.
- N. B. Yams, V. Richardson, G. E. Shubina, S. Albrecht, and D. Gillblad, “Integrated AI and Innovation Management: The Beginning of a Beautiful Friendship,” Technology Innovation Management Review, vol. 10, no. 11, pp. 5–18, Dec. 2020, doi: 10.22215/timreview/1399.
- W. Chen, C. Liu, F. Xing, G. Peng, and X. Yang, “Establishment of a maturity model to assess the development of industrial AI in smart manufacturing,” Journal of Enterprise Information Management, vol. 35, no. 3, pp. 701–728, Mar. 2022, doi: 10.1108/JEIM-10-2020-0397.
- T. Paschou, M. Rapaccini, C. Peters, F. Adrodegari, and N. Saccani, “Developing a Maturity Model for Digital Servitization in Manufacturing Firms,” 2020, pp. 413–425. doi: 10.1007/978-3-030-43616-2_44.
- “Digital Maturity Model: Achieving digital maturity to drive growth,” Deloitte.
- L. Canetta, A. Barni, and E. Montini, “Development of a Digitalization Maturity Model for the Manufacturing Sector,” in 2018 IEEE International Conference on Engineering, Technology and Innovation (ICE/ITMC), IEEE, Jun. 2018, pp. 1–7. doi: 10.1109/ICE.2018.8436292.
- G. Valdés, M. Solar, H. Astudillo, M. Iribarren, G. Concha, and M. Visconti, “Conception, development and implementation of an e-Government maturity model in public agencies,” Gov Inf Q, vol. 28, no. 2, pp. 176–187, Apr. 2011, doi: 10.1016/j.giq.2010.04.007.
- F. Blatz, R. Bulander, and M. Dietel, “Maturity Model of Digitization for SMEs,” in 2018 IEEE International Conference on Engineering, Technology and Innovation (ICE/ITMC), IEEE, Jun. 2018, pp. 1–9. doi: 10.1109/ICE.2018.8436251.
- M. Kırmızı and B. Kocaoglu, “Digital transformation maturity model development framework based on design science: case studies in manufacturing industry,” Journal of Manufacturing Technology Management, vol. 33, no. 7, pp. 1319–1346, Sep. 2022, doi: 10.1108/JMTM-11-2021-0476.
- P. NovaKovic, “General Dynamics: Annual Report 2023,” Mar. 2024.
- J. Saballa, “General Dynamics to Build More US Navy Replenishment Ships in $6.7B Deal,” TheDeffensePost.
- S. Wang, “China State Shipbuilding Corporation’s Role in Smart Shipbuilding,” Marine Technology Reports, vol. 58, no. 2, pp. 21–26, 2023.
- HD. Hyundai, “Annual Report 2023,” Hyundai Heavy Industries Group, 2024.
- Mitsubishi Heavy Industries Ltd., “Mitsubishi Heavy Industries Shipbuilding News,” Report.
No. of Downloads Per Month
No. of Downloads Per Country