The present study examines the effectiveness of applying Artificial Intelligence methods in an automotive production environment to predict unknown lead times in a non-cycle-controlled production area. Data structures are analyzed to identify contextual features and then preprocessed using one-hot encoding. Methods selection focuses on supervised machine learning techniques. In supervised learning methods, regression and classification methods are evaluated. Continuous regression based on target size distribution is not feasible. Classification methods analysis shows that Ensemble Learning and Support Vector Machines are the most suitable. Preliminary study results indicate that gradient boosting algorithms LightGBM, XGBoost, and CatBoost yield the best results. After further testing and extensive hyperparameter optimization, the final method choice is the LightGBM algorithm. Depending on feature availability and prediction interval granularity, relative prediction accuracies of up to 90% can be achieved. Further tests highlight the importance of periodic retraining of AI models to accurately represent complex production processes using the database. The research demonstrates that AI methods can be effectively applied to highly variable production data, adding business value by providing an additional metric for various control tasks while outperforming current non AI-based systems.

翻译：本研究探讨了在汽车生产环境中应用人工智能方法预测非循环控制生产区域中未知提前期的有效性。通过分析数据结构以识别上下文特征，并采用独热编码进行预处理。方法选择侧重于监督机器学习技术。在监督学习方法中，评估了回归与分类方法。基于目标尺寸分布的连续回归方法不可行。分类方法分析表明，集成学习与支持向量机最为适用。初步研究结果显示，梯度提升算法LightGBM、XGBoost和CatBoost能取得最佳效果。经过进一步测试与大量超参数优化，最终选定LightGBM算法。根据特征可用性与预测区间粒度，可实现高达90%的相对预测准确率。进一步测试突显了定期对AI模型进行再训练的重要性，以利用数据库精确表征复杂生产过程。本研究表明，AI方法能够有效应用于高变异性的生产数据，通过为各类控制任务提供额外度量指标而创造商业价值，其性能优于当前非AI基础的系统。