OptimaFlow

Poka-Yoke

Designing processes and equipment to prevent errors before they occur

Piloting - Active

AI Query Interface

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Core Principles

Mistake-proofing at the source

Preventing defects rather than detecting

Simple, low-cost error prevention devices

Immediate feedback on process deviations

Error Prevention Rate

78%

+15.5%

Target: 90%

87%

Predicted: 84%89% confident

Assembly Errors Eliminated

42per month

+22.5%

Target: 60per month

70%

Predicted: 51per month86% confident

Right First Time

94.5%

+4.8%

Target: 98%

96%

Predicted: 96.2%91% confident

Real-Time Events & Alerts
1

INCIDENT

medium

...

Assembly Error Rate Spike

Station 12 assembly errors increased 3x in past hour (from 2% to 6.5%). Wrong-part errors dominating.

Impact: Quality escapes risk. Increased rework costs estimated at $8K per shift if trend continues.

Recommended Action: Check Station 12 part staging. Verify operator training status. Consider emergency deployment of RFID poka-yoke system.

Explainability: Error pattern suggests incorrect part bin placement. Similar issue occurred on Station 9 last month, resolved by visual color-coding ($12K savings).

RL Score: 82%

AI Confidence: 88%

AI-Powered Improvement Opportunities

Total Potential: $409K/year

AI Vision-Based Error Detection

Impact: high

Effort: medium

$156K/yr

3-5 months

93% AI confidence

Deploy computer vision systems at critical assembly stations to detect incorrect parts, missing components, or assembly errors in real-time before the product moves to next stage.

RL Recommendation: Start with Station 7 (highest error rate: 8.5%). Train vision model on 2000+ good/bad examples. Implement with audio/visual alerts. Validate 98% accuracy before auto-rejection. Scale to 12 stations.

Explainability: Vision system catches 95% of assembly errors vs 68% with manual inspection. Prevents $128K in downstream rework and $28K in customer returns annually.

Smart Assembly Fixtures with RFID

Impact: high

Effort: medium

$134K/yr

4-5 months

91% AI confidence

Fixtures with embedded sensors and RFID readers ensure correct parts are placed in correct positions. Lock mechanism prevents progression if error detected.

RL Recommendation: Retrofit Assembly Line 2 fixtures first (15 stations). Tag all parts with RFID. Program sequence logic. Test with intentional errors. Monitor first 2 weeks for false positives (<1%).

Explainability: RFID poka-yoke eliminates wrong-part errors completely (current cost: $89K/year). Sequence enforcement prevents 92% of assembly sequence errors (current cost: $45K/year).

Force-Feedback Torque Tools

Impact: medium

Effort: low

$76K/yr

2-3 months

94% AI confidence

Electric torque tools with integrated sensors prevent over/under-tightening. Record and verify each fastener operation. Reject if torque out of spec.

RL Recommendation: Replace pneumatic tools in critical joints (engine mounts, safety components). Set tight torque windows. Log all operations to MES. Investigate outliers within 1 hour.

Explainability: Torque verification prevents loosening failures ($42K annual warranty cost) and overtightening damage ($34K rework annually). 100% traceability adds audit compliance value.

Color-Coded Component Staging

Impact: medium

Effort: low

$43K/yr

3-4 weeks

87% AI confidence

Use color psychology and visual cues to eliminate picking errors. Components staged in sequence with color matching assembly steps. Reduce cognitive load on operators.

RL Recommendation: Redesign kitting areas for complex assemblies first. Match component bins, instruction sheets, and assembly zones by color. A/B test vs old layout. Track picking error reduction.

Explainability: Visual color coding reduces picking errors from 3.2% to 0.4%, saving $43K annually in rework. Also improves assembly cycle time by 8 seconds per unit.