Evaluation of Artificial Intelligence Methods for Lead Time Prediction in Non-Cycled Areas of Automotive Production

2025-01-13

AI Summary

To address the challenge of accurately predicting delivery lead times in automotive flexible manufacturing zones—characterized by non-paced production and highly variable work orders—this paper proposes a context-aware supervised classification framework, circumventing the limitations of conventional continuous regression models when applied to discrete, long-tailed delivery time distributions. Methodologically, it introduces, for the first time in non-cyclic production lines, a systematic evaluation of lightweight gradient-boosting models (specifically LightGBM), augmented by a periodic dynamic retraining mechanism to accommodate line evolution. Feature engineering employs one-hot encoding, while model selection and hyperparameter optimization involve comparative evaluation across LightGBM, XGBoost, CatBoost, and SVM. Experimental results demonstrate that the optimal LightGBM-based classifier achieves a relative prediction accuracy of 90%, substantially outperforming existing non-AI systems. This provides a reliable, production-deployable metric for scheduling decisions and delivery commitment management.

Technology Category

• Planning, Routing, and Scheduling: Planning with Language Models
• Machine Learning: Hardware-aware ML
• Search and Optimization: Mixed Discrete/Continuous Search

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

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.

Problem

• Automotive Manufacturing
• Non-repetitive Areas
• Time Prediction

Innovation

• Integrated Learning
• Support Vector Machines
• AI Model Updating