Quality Analytics Products
Overview
Below are list of active Analytics Products for Quality.
Analytics Products
Several tools and access information for Warranty Analytics can be found here :
| Product | Brief Description/Product Purpose | Directly Responsible Individual (DRI) | Science Lead | ASMS |
|---|---|---|---|---|
| QDAP Warranty & DTC Analytics | The Quality Data & Analytics Platform (QDAP) is a collection of processes transforming data required for Vehicle Quality Analytics and Reporting. Insights are continuously build on data included in QDAP include Vehicle Events, Warranty Transaction Claims, Vehicle Diagnostic Trouble Codes (DTCs), Global A Parameter IDentifiers (PIDs) and VIP (Vehicle Intelligence Platform) Data IDentifiers (DIDs) from Electronic Control Units (ECUs). DTC and PID/DID are captured through Vehicle Data Recorders (VDRs), OnStar, Dealer Uploads and Plant Uploads. The DTC data is also combined with Warranty/Claims data and the system provides users with various reports including Warranty, DTC and PID/DID Summary and Detail Reports. | Matt Brozowski and Reesa Mason | Jason Grizzle | 189376 |
| Prognosis on Demand | This is a warranty prediction model that leverages warranty data using regressive machine learning model, predict potential warranty cost, optimize incident per thousand vehicle, Cost Per Vehicle, and improve vehicle reliability. | Jason Grizzle | Pablo Macias | |
| Quality - Failure Mode Effect Analysis (FMEA)* | Tool with Models (LLMs) tuned to FMEA datasets to accelerate the creation of initial FMEA documents by vehicle sub-system. Will significantly increase engineer productivity by reducing the current roughly 2 week manual process of creating initial FMEA documents to a few minutes.Automated FMEA: Minutes Instead of Weeks. Generate initial FMEA documents in minutes with AI-powered automation, significantly increasing engineering productivity. | Jason Grizzle | Dnyanesh Rajpathak | |
| Quality DTC Analytics (ADEPT)* | Deliver Dashboards to enable Quality teams to identify, prioritize, and analyze vehicle product issues using telemetry data, specifically Diagnostic Trouble Codes (DTCs). Integrate data science models with dashboards to identify and predict DTC trends early DTC: Predictive Diagnostics for Quality Improvement. Leverages dashboards and data science models to identify, analyze, and predict Diagnostic Trouble Codes (DTCs) using telemetry data. | Kelly Link | Ashok Gullapally | |
| Warranty Data Cube | Self Serve Descriptive Analytics on Vehicle Warranty Claims across Vehicles, Dealers, Regions, Suppliers, Parts, Controllers, Expense Categories, QRD Focus Areas, PPECs, SMTs, BOMs, Labor Codes, verbatims etc. Purpose: Enable Teams that serve Leadership to take make informed decisions on QSB; Enable Teams that serve Leadership to have informed conversations in ROC; Enable QRDs, EGLs, CQEs with insights in their areas that they are responsible for; Enable teams that Serves Finance in Warranty Spend Tracking | Jason Grizzle | 237954 | |
| VIN One Stop Shop (VOSS) | The Single/Multi-VIN reports are VIN-level reports that provide details on Part Traceability, DTC, PID, CAC/TAC, Compass, Recall Campaign, and Warranty transactions for multiple VINs. VINs can be uploaded into a selection box from a CSV file. Recommended limit is 500 VINs. Users have the option to select multiple sources for the data desired in the report. | Renita Williams |
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Support Channels
- #qdap-warranty-and-dtc
- #Warranty-data-analysis
- #ask-adept
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Last updated: August 20, 2026 Document version: 1.0