AI-Driven Quality Investigation
Deploys an 11-agent AI system that performs real-time anomaly detection using isolation forests and CUSUM algorithms, identifies drift patterns through time-series analysis, executes Bayesian causal inference for root cause identification, runs Monte Carlo simulations for impact quantification, and generates optimized corrective action plans with full resource allocation and execution tracking..
Problem Statement
The challenge addressed
Solution Architecture
AI orchestration approach
Agentic Investigation System Configuration - Comprehensive setup interface displaying system readiness metrics (2,847 parameters monitored across 19 data sources, 11 AI agents ready, 94.2% model accuracy), investigation scope configuration with facility and production line selection, AUML anomaly detection model settings with configurable confidence thresholds, priority and constraint management, agent network preview showing all 11 specialized agents, real-time sensor monitoring with live feeds tracking critical parameters (welding torque, electrode temp, injection pressure, mold current, spindle vibration, coolant flow), and active anomaly detection alerts identifying quality issues requiring investigation
Multi-Agent Orchestration in Progress - Real-time visualization of the 11-agent collaborative investigation workflow progressing through seven stages (Ingest → Detect → Analyze → Root Cause → Simulate → Decide → Approve). Features interactive agent network diagram showing the Orchestrator Agent coordinating Pattern Analyzer, Anomaly Detector, Root Cause Engine, Decision Optimizer, Impact Simulator, and Action Planner. Includes Agent Reasoning Trace panel displaying live AI thought processes with timestamped observations, impact analysis showing 847 parts at risk with $125,000 exposure, and agent status cards indicating completion progress for each specialized agent
AI-Recommended Actions with Impact Analysis - Comprehensive decision support interface presenting executive summary of identified quality issue (electrode tip wear at 94% of lifecycle with expected ROI 278:1), Monte Carlo simulation results (10,000 runs) comparing no-action scenario (847 parts at risk, $125,000 total exposure, 67% defect probability, 192 min time to impact) versus recommended intervention ($450 implementation cost, $124,550 expected savings, 99.2% success probability, 278:1 ROI). Features ranked recommendations with multi-dimensional scoring (effectiveness 96%, feasibility 98%, cost efficiency 92%, risk reduction 95%), detailed action plan for top recommendation (replace welding electrodes on Robot W12), required parts inventory with stock availability (ME-4436-L and ME-4456-R welding electrode tips, 47 in stock), trade-off analysis comparing downtime and cost across alternatives, and technician assignment with certification verification (Mike Chen, Low risk level, 30-minute duration)
Execution Monitor - Real-time Corrective Action Tracking - Live execution dashboard monitoring approved corrective action implementation with step-by-step progress tracking (create and dispatch work order, technician arrival and parts verification, execute LOTO safety procedure, remove worn electrode tips, install and align new electrode tips, release LOTO and restart line). Displays assigned technician details (Mike Chen, TECH-105, On Site status) with certifications (Welding Equipment, LOTO Certified, Robotic Systems), quality checkpoints validation (Parts Verified - all required parts staged, LOTO Complete - safety lockout confirmed, Installation Complete - new electrodes installed, Verification Pass - first 20 parts within spec), Agent Tool Calls monitoring panel showing real-time AI agent actions and observations during execution, and estimated completion times for each execution step ensuring proper sequencing and safety compliance
AI Agents
Specialized autonomous agents working in coordination
Orchestrator Agent
Complex quality investigations require coordinating multiple analysis streams, managing priorities, and synthesizing findings from different specialized systems into coherent action plans.
Core Logic
Coordinates the entire multi-agent workflow through task distribution, result aggregation, conflict resolution, and priority management. Receives inputs from all agent outputs, user commands, and system events. Produces workflow state updates, agent instructions, and final synthesis reports. Acts as the central intelligence hub that sequences analysis phases and ensures all agents contribute optimally to the investigation.
Anomaly Detector
Manual quality monitoring cannot process thousands of parameters simultaneously, leading to missed early warning signs of process drift or equipment degradation.
Core Logic
Performs real-time statistical anomaly detection using ensemble methods including Isolation Forest, CUSUM, EWMA, and multivariate analysis. Processes sensor streams, process parameters, and quality measurements to generate anomaly alerts with confidence scores (z-score, Mahalanobis distance, isolation score) and feature importance rankings. Identifies contributing factors and severity assessments for each detected anomaly.
Pattern Analyzer
Raw anomaly data lacks context about whether observed deviations represent temporary noise, systematic drift, or impending equipment failure.
Core Logic
Conducts time-series pattern recognition and trend forecasting on historical and real-time data streams. Classifies patterns (linear, exponential, cyclic, step-change), quantifies drift rates with confidence intervals, projects time-to-limit breaches, and matches current observations against a library of known failure signatures. Provides 95% confidence interval projections for specification limit violations.
Root Cause Engine
Identifying why quality issues occur requires deep analysis of equipment logs, maintenance history, material traceability, and process conditions—a complex task prone to human bias and oversight.
Core Logic
Executes causal inference using Bayesian networks and fault tree analysis. Builds probabilistic models from equipment logs, maintenance history, and knowledge bases. Generates ranked hypotheses with posterior probabilities, evidence chains (5-Whys methodology), and historical incident matching. Provides Bayesian summaries showing prior-to-posterior probability updates and likelihood ratios for explainable root cause identification.
Impact Simulator
Without quantified impact assessment, decision-makers cannot objectively compare intervention options or justify investment in corrective actions.
Core Logic
Runs Monte Carlo simulations (10,000+ iterations) to predict outcome distributions for both no-action and intervention scenarios. Quantifies parts at risk, defect probability, scrap/rework costs, customer impact risk, and production delays with P5/P50/P95 confidence bounds. Performs sensitivity analysis to identify which variables most influence outcomes, enabling targeted risk mitigation.
Decision Optimizer
Selecting the best corrective action involves balancing effectiveness, feasibility, cost, risk reduction, and speed—a multi-objective optimization problem too complex for manual analysis.
Core Logic
Applies Pareto optimization and constraint satisfaction algorithms to rank candidate actions across five objectives: effectiveness, feasibility, cost-efficiency, risk reduction, and speed. Generates trade-off matrices showing how each option performs on competing dimensions, sensitivity reports indicating robustness of recommendations, and explicit reasoning for why the top recommendation dominates alternatives.
Action Planner
Converting a recommended action into an executable plan requires resource scheduling, dependency management, safety compliance, and contingency planning.
Core Logic
Creates detailed execution plans with step-by-step instructions, assigned technicians (with certification verification), required parts (with stock availability checks), tool requirements, safety procedures, and LOTO protocols. Generates contingency plans and rollback procedures. Integrates with HR systems for technician availability and CMMS for parts location verification.
Digital Twin Agent
Testing corrective actions on live production equipment risks unintended consequences. Understanding equipment health requires synthesizing multiple sensor streams.
Core Logic
Maintains a synchronized virtual replica of physical equipment with 12ms latency. Runs physics-based what-if simulations to predict intervention outcomes before implementation. Calculates equipment health scores (0-100), remaining useful life predictions, and failure mode projections. Enables virtual testing of parameter changes without production risk.
Sustainability Agent
Quality interventions have environmental implications—energy consumption, carbon emissions, and waste generation—that are typically ignored in traditional decision-making.
Core Logic
Calculates environmental impact of each intervention option including energy consumption (kWh), carbon footprint (kg CO2e by scope), waste metrics, and water usage. Generates ESG scores and tracks sustainability improvements. Quantifies carbon avoided through defect prevention and identifies energy-optimal timing for maintenance activities.
Cross-Facility Agent
Quality patterns at one facility may already be solved at another, but knowledge sharing across global sites is inconsistent and slow.
Core Logic
Analyzes patterns across all 5 global facilities (Korea, Vietnam, India, Mexico, USA) to identify correlations and transfer best practices. Benchmarks current facility performance against global averages and best-in-class metrics. Generates knowledge transfer recommendations with expected improvement percentages and implementation effort estimates.
Predictive Agent
Reactive quality management addresses problems after they occur. Proactive prevention requires forecasting future anomalies and maintenance needs.
Core Logic
Deploys LSTM ensemble models trained on historical data to predict future anomalies, maintenance requirements, and quality trends. Generates predicted anomaly lists with probability scores, maintenance predictions with remaining useful life estimates, and quality forecasts with critical factor identification. Identifies optimal proactive maintenance windows.
Worker Overview
Technical specifications, architecture, and interface preview
System Overview
Technical documentation
Tech Stack
5 technologies
Architecture Diagram
System flow visualization