The continuous development of information and communication technologies has resulted in an exponential increase in data. Consequently, technologies related to data analysis are growing in importance. The shipbuilding industry has high production uncertainty and variability, which has created an urgent need for data analysis techniques, such as machine learning. In particular, the industry cannot effectively respond to changes in the production-related standard time information systems, such as the basic cycle time and lead time. Improvement measures are necessary to enable the industry to respond swiftly to changes in the production environment. In this study, the lead times for fabrication, assembly of ship block, spool fabrication and painting were predicted using machine learning technology to propose a new management method for the process lead time using a master data system for the time element in the production data. Data preprocessing was performed in various ways using R and Python, which are open source programming languages, and process variables were selected considering their relationships with the lead time through correlation analysis and analysis of variables. Various machine learning, deep learning, and ensemble learning algorithms were applied to create the lead time prediction models. In addition, the applicability of the proposed machine learning methodology to standard work hour prediction was verified by evaluating the prediction models using the evaluation criteria, such as the Mean Absolute Percentage Error (MAPE) and Root Mean Squared Logarithmic Error (RMSLE).

信息和通信技术的不断发展导致数据呈指数级增长。因此，与数据分析有关的技术越来越重要。造船业具有很高的生产不确定性和可变性，因此迫切需要数据分析技术，例如机器学习。尤其是，该行业不能有效地响应与生产相关的标准时间信息系统的变化，例如基本周期时间和提前期。必须采取改进措施，以使该行业对生产环境的变化做出快速反应。在这项研究中，制造，组装船体，使用机器学习技术预测线轴的制造和喷漆，以使用生产数据中时间要素的主数据系统为工艺提前期提出一种新的管理方法。数据预处理是使用R和Python（这是开源编程语言）以各种方式执行的，并且通过相关性分析和变量分析考虑了过程变量与提前期之间的关系来选择过程变量。应用了各种机器学习，深度学习和集成学习算法来创建提前期预测模型。此外，通过使用评估标准评估预测模型，验证了所提出的机器学习方法对标准工作时间预测的适用性，