Lean manufacturing in shipbuilding and ship repair: application of the analytical hierarchy process to the issue of reducing the time required to make management decisions

This study is devoted to the development of a comprehensive methodology for implementing lean manufacturing in the shipbuilding and ship repair industries with the integration of Industry 4.0 digital technologies and the Analytical Hierarchy Process (AHP). The relevance of the research is determined by the need to improve the operational efficiency of enterprises under conditions of global competition, tightening IMO environmental requirements, and increasing quality standards for shipbuilding products. The paper analyzes current trends in the digital transformation of lean manufacturing, including the application of BIM technologies for value stream mapping, IoT sensors for predictive analytics, and cloud platforms for automating Andon systems. Particular attention is given to adapting the Analytical Hierarchy Process to solving management problems in shipbuilding. A multi-level decision-making model has been developed to structure complex tasks related to the selection of technological solutions, supplier evaluation, and resource allocation. The results of practical testing of the proposed methodology at industry enterprises are presented, demonstrating a 40–60% reduction in management decision-making time and a 35% decrease in the number of erroneous decisions. The study identifies and systematizes key barriers to digital transformation in shipbuilding, including personnel resistance to change, a shortage of qualified specialists, and fragmentation of digital solutions. Practical mechanisms for overcoming these barriers are proposed through the development of Lean 4.0 industry standards and the creation of hybrid implementation models suitable for enterprises of different scales. The scientific novelty of the study lies in the development of a holistic Lean 4.0 concept for shipbuilding that combines the digitalization of traditional lean manufacturing tools with a multi-criteria decision-making methodology. The practical significance of the research is confirmed by calculated implementation effects, including a 15–20% reduction in construction time, a 10–15% reduction in production costs, and a 25–30% increase in the transparency of management decisions. The results of the study can be applied in the development of corporate lean manufacturing standards, the creation of decision support systems, and the planning of digital transformation strategies for shipbuilding enterprises. Prospects for further research include the development of industry-specific solution templates for typical management tasks and the integration of AHP with artificial intelligence technologies to create adaptive control systems.