Manufacturing Analytics Products
Overview
Below are list of active Analytics Products for Manufacturing / Vehicle.
Analytics Products.
AI/ML products are marked with an asterisk (*)
| Product | Brief Description/Product Purpose | Directly Responsible Individual (DRI) | Science Lead | ASMS |
|---|---|---|---|---|
| Battery Strategy Optimization Tool (BSOT) | Develop a cross-domain “scenario planning tool” that optimizes battery technology application proposals to entire EV portfolios until 2030 within business and manufacturing constraints to maximize cost reduction. | Sujatha Venugopal | Chunxi Wang | 243698 / 1000767 |
| COTS - COA Legacy | Prescriptive analytics solution that optimizes content, packaging, and pricing decisions based on consumer preferences, while maximizing profitability and increasing market share | Sujatha Venugopal | 57363 | |
| COTS - COA Next Gen | Prescriptive analytics solution that optimizes content, packaging, and pricing decisions based on consumer preferences, while maximizing profitability and increasing market share | Sujatha Venugopal | 57363 | |
| Financial Impact (Project Moonshot) | Incorporate Variable Profit (VP/unit) and GHG impact (g/unit) to enable directional financial assessment of volume scenarios | Peiling Wu-Smith | NEW | |
| GHG Compliance (Project Moonshot) | Incorporate Cost of Compliance to Moonshot ecosystem to assess the U.S. GHG impact for the Long term and Manufacturing forecasting | Peiling Wu-Smith | NEW | |
| IQ (Intelligent Query)* | Chatbot used to extract insights from proprietary GM datasets based on natural language questionsIntelligent Query: Natural Language Access to GM Data. Get instant insights from proprietary GM datasets simply by asking questions. | Sujatha Venugopal | NEW | |
| Manufacturing Footprint Automation (Project Moonshot) | Manufacturing Footprint Automation: An automated process that converts sales to production, allocates production across assembly plants, and identifies capacity surplus/shortfall | Sujatha Venugopal | 1000534 | |
| Mid Cycle Major | Automates the existing manual Life Cycle Scoring process and facilitates business users to effectively identify programs that require Mid Cycle updates. | Sujatha Venugopal | 237954 | |
| Multivariate Demand Simulator (Project Moonshot) | MDS incorporates proprietary conjoint research on the voice of customer, observed historical data, and GM business inputs to provide a wholistic, statistical-based capability that projects GM’s long-term sales demand, supports “what-if” scenario analysis, and enables real-time end-to-end business planning by integrating with MFA, GHG, and Financials. | Peiling Wu-Smith | Peiling Wu-Smith | |
| Product Program 360 | Product Program Identifier allows this 360 to dive into global vehicle development relating to both the vehicle and its powertrain programs for planning, costing and forecasting. | Sujatha Venugopal | 180543 | |
| DLA (Device Level Analytics)* | Develop new capability allowing GM to automatically detect, debug and repair systemic discrepancies in motions, robots, dispense, vision, welding and conveyor controllers in order to improve manufacturing throughput and reduce costs in our Vehicle Assembly plantsDLA: Automate Manufacturing System Diagnostics and Repairs. DLA improves throughput and reduces costs by autonomously detecting and resolving issues in motion, robotics, and other critical systems at Vehicle Assembly plants worldwide. worldwide. | Paras Gandhi | 220372 | |
| EFTQ Score Enhancement | Design and develop data ingestion, curation, and extraction processes of multiple datasets(Vehicle 360, MERS, PRTS, QsStat, and Bauer) to enable Electrical First Time Quality(EFTQ) analysis and predictive modeling | Paras Gandhi | ||
| Ultium Data Analytics* | Integrate battery test, vehicle operational data, and electric vehicle service data to implement AI/ML solution for early detection of anomalies in battery pack during manufacturing process, and to create a holistic 360-degree view of battery components. | Paras Gandhi | 213575 | |
| Busbar Weld Quality Enhancement | Enhance weld quality evaluation by developing an inline defect detection system that combines machine vision and spectrometer data for improved accuracy, while also integrating machine learning models to predict electrical and mechanical performance based on real-time sensor and process data. | Paras Gandhi | Hui Chen | |
| Logistics/Material Flow Optimization | Develop a centralized dashboard that consolidates critical data from multiple sources to deliver real-time insights and analytics, enhance visibility into material flow performance, uncover strategic opportunities through statistical correlations, and ultimately support a scalable enterprise strategy for optimizing material flow across Assembly Plants. | Paras Gandhi | Matt Brown | 248398 |
Need Help? Connect with us
Support Channels
- Primary Channel for Data: #help-data
- Primary Contact for access related questions - Galileo Access Request (per ASMS)
Have an update? Send us your feedback via Slack at: #feedback-data-gm-com
Last updated: January 23, 2026 Document version: 1.0