AI-Powered Production Optimization & OEE Intelligence Digital Worker
## The Solution This digital worker deploys 6 specialized AI agents that continuously monitor OEE metrics, identify optimization opportunities with dollar values, validate improvements through digital twin simulation, predict quality issues before they occur, optimize changeover sequences, and capture expert operator knowledge as reusable playbooks. The agents provide real-time guidance and can display on shop floor monitors for immediate operator action.
Problem Statement
The challenge addressed
Solution Architecture
AI orchestration approach
Multi-agent orchestration system with DAG-based workflow and real-time telemetry monitoring
ML-driven pattern recognition revealing production anomalies and root causes with statistical validation
Digital twin scenario testing with Monte Carlo simulations validating OEE improvement strategies
Optimization recommendations with Pareto-optimal prioritization showing $1.2M potential savings
AI Agents
Specialized autonomous agents working in coordination
Performance Agent - Real-time OEE Monitoring
## Visibility Gap OEE is often calculated retrospectively from shift reports, missing real-time opportunities to address losses as they occur. By the time managers see OEE data, the production shift is over and opportunities are lost.
Core Logic
## Live OEE Intelligence The Performance Agent calculates OEE in real-time (updates every 3 seconds), breaking down Availability × Performance × Quality components with visual gauges. It tracks trends, compares against targets (85% world-class), and immediately surfaces production anomalies (micro-stops, speed reductions, quality dips) with timestamps and root cause indicators.
Optimizer Agent - Digital Twin Simulation & Validation
## Risk of Change Production engineers hesitate to implement optimization changes due to risk. Without validation, speed increases might cause quality issues, and parameter changes might cause equipment damage. Trial-and-error on production lines is costly.
Core Logic
## Simulation-Validated Optimization The Optimizer Agent runs 10,000+ simulations in the digital twin environment to validate proposed changes before implementation. It tests parameter modifications (turret speed, compression force, fill depth), calculates success probability (98%+ required for recommendation), assesses risk on a 0-10 scale, and provides historical validation from sister plant implementations.
Quality Predictor Agent - Proactive Defect Prevention
## Reactive Quality Control Traditional quality control catches defects after they occur, scrapping or reworking product. By the time quality issues are detected, significant production has been affected.
Core Logic
## Predictive Quality Management The Quality Predictor Agent monitors 12+ quality parameters in real-time, detecting drift patterns before they cause defects. It predicts First Pass Yield (FPY) hours in advance, identifies root causes (feeder calibration drift, temperature variance), and recommends preventive actions with cost-benefit analysis.
Changeover Agent - Setup Time Optimization
## Changeover Losses Product changeovers are major sources of OEE loss, often taking hours longer than necessary due to inconsistent procedures, sequential (instead of parallel) tasks, and lack of real-time guidance for operators.
Core Logic
## Optimized Changeover Sequences The Changeover Agent analyzes historical changeover data to generate optimized sequences, identifying opportunities for parallel execution, optimal task ordering, and best-practice techniques. It significantly reduces changeover time, provides step-by-step guidance during changeover, and tracks progress in real-time with milestone notifications.
Learning Agent - Best Practice Capture & Knowledge Transfer
## Expert Knowledge Loss Top operators achieve significantly better OEE than average operators, but their techniques aren't captured or shared. When experts retire or transfer, their knowledge leaves with them.
Core Logic
## AI-Discovered Best Practices The Learning Agent analyzes operator behavior patterns, identifying techniques that correlate with superior performance. It automatically generates Best Practice Playbooks with specific parameter settings, procedures, and conditions. Playbooks can be applied, exported, and shared across shifts and plants, institutionalizing expert knowledge organization-wide.
Coach Agent - Operator Guidance & Real-time Support
## Operator Support Gap Operators face complex decisions during production but may not have immediate access to engineering support, especially on night shifts. Questions go unanswered, leading to suboptimal decisions.
Core Logic
## AI-Powered Operator Assistance The Coach Agent provides real-time guidance through an interactive chat interface, answering operator questions, recommending actions based on current conditions, and providing step-by-step instructions for procedures. It tracks questions answered per hour, learns from interactions to improve responses, and escalates complex issues to human experts when appropriate.
Worker Overview
Technical specifications, architecture, and interface preview
System Overview
Technical documentation
Tech Stack
5 technologies
Architecture Diagram
System flow visualization