OptimaFlow

Modular OpEx Intelligence

Professional
Active
AI Status

All systems operational

DMAIC Six Sigma

DMAIC (Define, Measure, Analyze, Improve, Control) - Data-driven approach to reduce variation and improve quality

Planning - Scheduled

AI Query Interface

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Core Principles

Define: Identify problem and project goals
Measure: Collect data and establish baselines
Analyze: Identify root causes of defects
Improve: Implement and verify solutions
Control: Sustain improvements and monitor

Process Sigma Level

3.8σ
+0.3%
Target: 4.5σ
84%
Predicted: 4.1σ85% confident

Defects Per Million Opportunities

4250DPMO
-12.5%
Target: 1500DPMO
283%
Predicted: 3200DPMO82% confident

First Pass Yield

92.5%
+2.8%
Target: 96%
96%
Predicted: 94.2%87% confident

Real-Time Events & Alerts
1

RISK
high
...

Process Variation Trending Out of Control

Critical dimension on Part X showing increasing variation. Cpk degraded from 1.6 to 1.15 over past week. Approaching spec limit.

Impact: High risk of defects escaping to customer within 2-3 days if trend continues. Potential recall scenario worth $250K+.
Recommended Action: Initiate immediate DMAIC project. Root cause analysis on tooling wear, material variation, and process drift. Consider interim 100% inspection.
Explainability: SPC charts show 7 consecutive points trending toward upper spec limit (Rule 1 violation imminent). Predictive model estimates 92% probability of exceeding USL in next 48 hours.
RL Score: 89%
AI Confidence: 87%

AI-Powered Improvement Opportunities

Total Potential: $468K/year

AI-Powered DMAIC Project Selection

Impact: high
Effort: medium
$185K/yr
3-4 months
#5
93% AI confidence

Use ML to automatically identify and prioritize DMAIC projects based on defect patterns, cost impact, and probability of success.

RL Recommendation: Analyze last 24 months of quality data. Train model on 50 past projects (success/failure features). Start with top 3 AI-recommended projects.

Real-time SPC with Predictive Alerts

Impact: high
Effort: high
$156K/yr
4-5 months
#5
90% AI confidence

Deploy AI-enhanced Statistical Process Control that predicts out-of-control conditions 2-4 hours before they occur, enabling proactive intervention.

RL Recommendation: Start with Line 1 (highest defect rate). Use sensor data + historical patterns. Alert operators when Cpk predicted to drop below 1.33.

Automated Root Cause Analysis

Impact: medium
Effort: medium
$82K/yr
2-3 months
#4
88% AI confidence

Machine learning system that analyzes defect data and automatically identifies top 3 likely root causes with supporting evidence, reducing analysis time by 70%.

RL Recommendation: Train on 500+ past incidents with known root causes. Start with recurring defect patterns. Validate AI suggestions against expert analysis.

Design of Experiments (DOE) Optimizer

Impact: medium
Effort: low
$45K/yr
1-2 months
#3
86% AI confidence

AI assistant that designs optimal experiments for process optimization, reducing experimental runs by 40% while maintaining statistical power.

RL Recommendation: Use for next 3 DMAIC projects. Compare traditional vs AI-optimized DOE designs. Track time and cost savings.