AI-Based Failure Management: Value Chain Approach in Commercial Vehicle Industry
This paper describes an artificial intelligence (AI) based failure management approach across the value chain for the commercial vehicle industry by integrating and utilizing lifecycle data for product and production optimization. The amount of available data throughout a product lifecycle has increased significantly in previous years, primarily driven by the development and deployment of cyber-physical systems. While data from a single entity in the value chain already enables failure management-related analysis and services, including AI-based methods such as predictive maintenance, there remains a lack of systematic approaches to utilize data across the entire value chain. This paper proposes an AI-based failure management approach, which relies on integrating a variety of diverse data sources along the value chain. At first, three so-called application areas were defined: process and product optimization, availability optimization, and performance optimization. Consequently, practice-relevant use cases are identified for each area, for which it is shown how failures in the value chain can be proactively eliminated with the support of AI. Methods for predictive analytics are adapted for cross-value chain failure management to derive correlations between different stages of the production process and product usage. Based on these results and human expert knowledge, proactive measures are recommended by a decision support system (DSS) to resolve failures before arising. The commercial vehicle industry serves as an overarching validation case study for the practice-relevant verification of the targeted applications. The paper gives an outlook on the envisaged research work for the realization of holistic failure management.