Quality Analytics Products
Overview
Below are list of active Analytics Products for Quality.
Analytics Products
AI/ML products are marked with an asterisk (*)
| 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. | Bindhu Rajen | Selam Tesfamariam | 189376 |
| Vehicle Engineering & Quality Maintenance | This project aims to manage and optimize the Cognos and Power BI platforms to ensure stable, secure, and efficient hosting of analytical products for VEQ organization. Key activities include platform monitoring, user access management, issue resolution, and performance optimization. It will also involve user training, support cloud migration, and guidance on the deployment of new analytics products. Success will be measured by platform uptime, issue resolution speed, user satisfaction, and cost-effectiveness. | Bindhu Rajen | Jaime Gonzales | 188819/188820 |
| COMPASS Advanced Analytics | Develop ML capabilities to process both structured and unstructured responses, detecting underlying themes, sentiments, and trends across multiple survey entries. | Bindhu Rajen | Dnyanesh Rajpathak | |
| Electron Chatbot | Integration for a teams bot in South America. The bot uses a large language model for employees to collection limited information about GM Vehicles. | Bindhu Rajen | Pablo Macias | |
| 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. | Bindhu Rajen | Pablo Macias | |
| Quality - User Interface | User Interface to manage user managed information like uploading umt files, provide access control to suppliers for QDAP based on architecture they support, and also manage the KPIs measured within Quality organization. | Bindhu Rajen | Kacie Bergh | |
| Proactive Incident Alert System | Models to leverage patterns in vehicle telemetry data (specifically Parameter Identifiers (PIDS) and DTCs) to predict potential future issues at a VIN Level and at a Vehicle Population Level. Enable proactive vehicle issue identification and management, and potential resolution | Shelka Sachdeva | Fariborz Mahmoudi | |
| 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. | Shelka Sachdeva | Dnyanesh Rajpathak | |
| Quality DTC Analytics + New (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 earlyDTC: 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 | Anas Ramadan | |
| RESS Replacement Dashboard | Reserve Energy Storage System (RESS) is the battery pack storing energy in EVs. Currently there are numerous issues relating to collecting and managing the data needed to support resolution of RESS related problems. This project will deliver a common integrated dataset along with the dashboards required by the business analysts to track, analyze, and accelerate resolution of RESS issues. | Sucheetha Dayananda | ||
| Warranty Claims Clustering and Analytics + New | Deliver Model to predict emerging DTCs based on its association with other DTCAutomate the processing of Warranty Claims verbatims text using Large Language Models (LLMs) to cluster issues into pre-defined buckets to accelerate analysis of issues. | Shelka Sachdeva | Anas Ramadan | |
| 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 | Shelka Sachdeva | 237954 | |
| DTC Based Emerging Issue Detection - Quality, Service, Safety | Diagnostic trouble code pattern mining technology enables detection and monitoring of service, quality, safety issues by constructing strong DTC signals from the telemetry data. With the help of constructed diagostic trouble code patterns it helps to reduce quality, service, or safety specific issue discovery time. | Shelka Sachdeva |
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Last updated: January 23, 2026 Document version: 1.0