Overview

The process of categorizing and clustering warranty claims has traditionally been a manual and time-consuming task. With AIP, teams can now process claims at scale, aiding quality engineers in the early identification of new and emerging issues. This rapid identification allows for swift investigation and determination of the root causes of these issues by leveraging data from various sources, including warranty claims, configuration data, connected vehicle data such as diagnostic trouble codes (DTCs) and telemetry data, production tests, and more.

Features

• Claim Classification: Clients utilize AIP to sort claims into predefined categories, such as impacted components. As the system captures expert input over time, creating an evolving knowledge base that feeds back into AIP, confidence in AIP's classification capabilities increases. This growing trust enables engineers to opt for automatic categorization, thereby streamlining the process. - Automated Cluster Generation. AIP autonomously groups unassigned claims, presenting engineers with an early warning of potential new issues and visual data to facilitate investigation, including safeguards to ensure transparency and trust in the clustering process.
• Claim, Issue, and Vehicle 360s: The Ontology enriches claims and issues with vehicle and model context, allowing users to proactively find known issues, do rapid hypothesis testing, and accurate root cause analysis. This facilitates quick recall scoping and measure definition across the fleet.
• Feedback to Manufacturing Quality: AIP empowers teams to compile issues from every customer, vehicle, and dealership at the global level. This helps in identifying fleet-wide quality issues and seamlessly feeding this information back into manufacturing processes, manufacturing execution systems (MES), and supply chain management (SCM).