An Explainable Deep Learning Framework for Predicting Production Line Downtime Risks in New Vehicle Launch Projects: A Case Study of the Iranian Automotive Industry

Author

Fereshteh larti


Abstract

 New vehicle launch projects are continuously exposed to various risks, including production line downtime, due to their technical complexity, extensive dependence on supply chains, and uncertainties during the transition phase to mass production. Although traditional risk management methods are effective in identifying critical factors, they have limitations in analyzing the complex, nonlinear, and multidimensional relationships embedded in manufacturing data. Therefore, this study aims to propose a conceptual framework based on explainable deep learning for predicting production line downtime risks in new vehicle launch projects, with a specific focus on the conditions of the Iranian automotive industry.This research is a systematic literature review and conceptual framework development study. Initially, relevant studies related to automotive project risk management, smart manufacturing, machine learning, deep learning, and explainable artificial intelligence were reviewed and analyzed. Subsequently, based on the research gaps identified in the literature, a proposed architecture consisting of four main layers was developed: the data acquisition and integration layer, the data processing and feature extraction layer, the predictive deep learning model layer, and the explainable artificial intelligence layer.The review of previous studies indicates that deep learning models, particularly LSTM and Transformer architectures, have significant capabilities in analyzing time-series and multi-source manufacturing data and predicting critical events. However, the main limitation of these models is the lack of transparency in their decision-making processes. Incorporating explainable artificial intelligence methods, such as SHAP and LIME, can maintain predictive accuracy while revealing the factors contributing to increased production line downtime risks for industrial managers. By integrating equipment, quality, production, and supply chain data, the proposed framework enables the development of an early warning system for production risk management.The proposed framework can serve as a novel approach for transitioning from reactive risk management toward proactive risk management in the Iranian automotive industry. The application of explainable deep learning not only improves production downtime prediction capabilities but also enhances the reliability of intelligent systems and supports managerial decision-making in new product launch projects.



Keywords

Deep Learning; Explainable Artificial Intelligence; Production Line Downtime Prediction; Production Risk Management; Iranian Automotive Industry.



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