AI-FTA for Automotive Quality Management

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last updated：Oct 22, 2026

Support for identifying the causes of failures in automotive quality management.

In the automotive industry's quality control, it is important to quickly identify abnormalities and defects in the manufacturing process and implement measures to prevent recurrence. Particularly in increasingly complex manufacturing lines and a wide variety of parts, the time taken to identify the causes of failures can lead to decreased production efficiency and customer complaints. AI-FTA integrates on-site data such as failure reports and inspection histories, transforming complex causal relationships into easily understandable structural information, thereby accelerating cause identification and supporting problem-solving in quality management. 【Usage Scenarios】 - Quality control departments in automotive manufacturing lines - Quality assurance departments in parts suppliers - Situations requiring failure analysis 【Effects of Implementation】 - Reduction in time to identify failure causes - Elimination of reliance on specific individuals for abnormal response - Assetization of trouble response history

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【Features】 - Integration of on-site data → Structured decision-making - Utilization of past failure cases and causal relationships - Acceleration of primary cause estimation - Reduction of reliance on veterans and support for horizontal deployment - Utilization of existing equipment and existing data (no modifications needed) 【Our Strengths】 Our company is a specialized store focused on LED lighting and custom solutions. With expertise in AI-FTA and flexible responses tailored to customer needs, we provide optimal solutions.

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※2-6 months

Applications/Examples of results

■ Performance and Verification Examples Verification conducted in an operational plant management company in Japan (*Customer and facility names are confidential) Verification focused on continuous processing equipment operating for over 10 years Integration of failure reports, work standards, and inspection history Structuring of over 1,200 records Implementation of multiple system classification and cause candidate suggestion functions Confirmation of potential for horizontal deployment across locations *The verification examples are utilized as "effective judgment structure models on-site." ■ Common Concerns (FAQ Style) Q: What is needed before implementation? A: You can start simply by organizing and providing your current maintenance and failure report documents. Q: How much effort is required for implementation? A: It varies depending on the data preparation status of the target systems, but you can start with a Proof of Concept (PoC). Q: In what environments is it effective? A: It is effective in equipment maintenance environments where the causal structure is complex and relies on veteran judgment.