Beyond scorecards/dashboards, there is limited data collection and analysis of product performance and failures.
Goals and Objectives
Context-driven product/design insights from previous engineering R&D for higher product quality, increased innovation, improved product success rate, and higher customer satisfaction
Hardware: Servers, storage, IoT, smartphones, and tablets
Software: GenAI, CBR, PLM, ERP, CRM, MES, SLM, cognitive/AI, and BDA
Services: IT services
Use Case Summary
Past design approaches and product performance are evaluated by AI to drive engineering actions and outcomes and to evaluate/predict product performance and guide courses of action.