Knowledge Management Strategy

Knowledge Management Strategy

Pengcheng Ni, Jussi Kantola

Exploring the factors influencing knowledge management strategy in the European shipbuilding industry: A pilot study.

AI generated illustration of a ship and a brain​​​

Knowledge is an important asset in shipbuilding. This paper aims to explore the key factors of knowledge management (KM) in European Shipbuilding to improve its strategic performance. This pilot study can form an important reference for knowledge management strategy implement in European shipbuilding. Data collected from project partners are used to test seven hypotheses by means of linear regression analysis, to identify the key factors of knowledge management strategy in European shipbuilding. This paper provides ideas for shipyards to consider when conducting knowledge management as part of the Smart European Shipbuilding (SEUS) project. A full article is available at doi: https://doi.org/10.62477/jkmp.v25i6.595

Generative Algorithms

Generative Algorithms

Diego De León, Herbert Koelman
 

Generative Algorithms in Early Ship Design: An Exploration of Hull Subdivision Generation 

An Scenarion for Data Driven Early Ship Design​​​ This paper explores the potential for a data-driven tool to aid in the early ship design process, through the generation of subdivisions for the general layout via a proof-of-concept prototype which leverages a GAN to create plausible layout alternatives. The software implementation integrates a BSP tree structure for parametrisation, and a CAD geometry implementation. To work within the intrinsic limitations of generative algorithms, the decision-making is made by a naval architect, targeting facilitating the evaluation of multiple concepts and broadening the design possibilities. The paper describes the functioning of the proof-of-concept prototype, considerations on its creation and applicability. The full article is available at doi: https://doi.org/10.5281/zenodo.17534156

AI Naval Architecture

AI Naval Architecture

Karolina Bierkowska, Henrique Gaspar, Thomasz Hinz

Navigating AI in Naval Architecture: A Comparative Effectiveness Study of Machine Learning Models for Ship Stability 

AI generated image - ship in rough sea​​​

This study comparatively analyses diverse AI/ML models on an established dataset of hull variants and Second-Generation Intact Stability Criteria metrics, a time-consuming task in the early stages of ship design. This selection encompasses diverse AI techniques, each recognised for its unique strengths, featuring Artificial Neural Networks, Decision Trees, Probabilistic Models, and Large Language Models. In this article, we focus primarily on one of the failure modes: excessive acceleration. This research guides naval architects in selecting suitable emergent AI tools to enhance design space exploration, ultimately contributing to filter the current AI "hype" into useful NA practices in the industry. A full article is available at doi: https://doi.org/10.5281/zenodo.17493840