Transform siloed yield data from testing equipment into real-time intelligence with automated burst reports, operator red-screen alerts, AI-generated weekly reports, and a generative self-service interface for Meta.
Real-time yield alerts, burst reports, and historical fault-fix guidance pushed to floor personnel.
Operators, Supervisors, Ops Managers, PM, Engineering, Account Management
Automated weekly reports identifying yield variances, root causes, and recommended actions.
Meta, RXT Engineering, RXT Operations, Account Management
Generative AI interface enabling Meta to query yield, quality, and fault data on demand.
Meta Program Team, Meta Quality, RXT Account Management
Impact: No real-time cross-operation, cross-model, cross-workstation visibility
Root Cause: No aggregation layer or dashboard connecting test equipment data
Impact: Operators cannot access solutions in context; knowledge lost with turnover
Root Cause: No link between fault codes and resolution database
Impact: Yield problems persist for hours before detection; excess rework and scrap
Root Cause: No monitoring agent or threshold engine watching yield in real time
Impact: Inconsistent formats; time-consuming; subjective root-cause analysis
Root Cause: No automated reporting pipeline from test data to formatted output
Impact: Cannot measure fix effectiveness or identify chronic issues
Root Cause: Offline resolution data not connected to fault code system
Impact: Every data request requires RXT effort; delays in Meta decision-making
Root Cause: No query interface or API for Meta to access yield/quality data
Impact: Leadership unaware of intra-shift yield shifts; delayed corrective action
Root Cause: No automated periodic reporting to supervisors/managers
AI agent continuously extracts yield performance and delivers automated burst reports every hour (configurable). Includes yield by workstation/operation/model, top fault codes ranked by frequency and impact, yield trend vs. 7-day and 30-day averages, and historical fault-fix solutions auto-surfaced.
When a workstation's yield drops below configurable thresholds (3 consecutive failures, yield below 85%, or high-severity fault code), the system flashes a red-screen alert on the operator's test station display with the specific fault code, recommended fix, and auto-escalation path.
Static Word documents ingested into a structured, searchable knowledge base linked directly to fault codes. When a fault fires, proven resolutions display automatically. New successful resolutions are captured and fed back. Engineering reviews resolution effectiveness over time.
Automated weekly reports with yield by model/operation/workstation/time period, week-over-week and month-over-month comparisons, variance analysis with correlated fault code data, root-cause identification, and structured action items. Consistent format, auto-distributed.
Generative AI interface for Meta to query yield and quality data directly in natural language. Example: 'What is the current yield for Model X at functional test?' Same real-time data pipeline as burst reports. Access controls protect RXT-internal operational details.
Resolution effectiveness data feeds back into fault-fix recommendations. Models retrain on new response data. Burst report frequency and granularity expand based on Phase 2 learnings. KPIs tracked: yield improvement, mean time to detect, mean time to resolve.
Natural language queries Meta can run against the generative AI interface — drawing from the same real-time data pipeline as burst reports and weekly reports.
Meta defines what variances, formats, and action items they want in weekly reports. Joint working session to align on content requirements.
Joint agreement on what data Meta can query vs. what is RXT-internal. Access controls configured with Meta's approval.
Meta reviews yield improvement data from the Phase 2 pilot (alerts + burst reports). Provides feedback on alert thresholds and report granularity.
During Phase 3 transition, AI-generated reports are validated against manually produced reports. Meta confirms accuracy before switching to automated.
Monthly review of KPIs (yield improvement, MTD, MTR) with Meta. Feedback drives model retraining and report refinement.
Meta tests the generative query interface with real questions. Feedback on accuracy, response quality, and data coverage drives improvements.
Every Dream Team member has explicit, actionable responsibilities for the Yield Analytics project.
Reviews AI-generated weekly reports before Meta distribution; validates root-cause accuracy
Provides Meta with API documentation for the self-service query interface; manages data access controls
Coordinates with Meta on weekly report content requirements and delivery cadence
Primary technical contact for Meta on yield data interpretation, fault code definitions, and quality standards
Primary relationship manager with Meta program team; owns the communication cadence and escalation path
| # | Open Item | Owner | Priority | Blocker |
|---|---|---|---|---|
| 1 | Define yield threshold parameters for red-screen alerts by operation and model | RXT Engineering / Ops | High | Phase 2 blocker |
| 2 | Identify and collect all existing fault-fix Word documents for knowledge base ingestion | RXT Engineering | High | Phase 1 dependency |
| 3 | Determine test equipment data export capabilities and API/integration options | RXT IT / Equipment Vendors | High | Phase 1 blocker |
| 4 | Agree on Meta self-service data access scope and security boundaries | Meta / RXT Joint | Medium | Phase 4 dependency |
| 5 | Define weekly report content requirements with Meta | Meta PM / RXT AM | Medium | Phase 3 input |
| 6 | Determine burst report distribution list and escalation hierarchy | RXT Operations | Medium | Phase 2 input |
| 7 | Evaluate test station display capabilities for red-screen alert rendering | RXT IT / Engineering | High | Phase 2 blocker |