OptimaFlow

Modular OpEx Intelligence

Professional
Active
AI Status

All systems operational

Lean

Elimination of waste and creation of flow in processes

Piloting - Active

AI Query Interface

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Core Principles

Stable processes
Eliminating waste (Muda, Mura, Muri)
Flow & pull systems
Just-in-Time production

Lead Time Reduction

32%
+6.8%
Target: 40%
80%
Predicted: 37%86% confident

Inventory Turnover

8.5times/year
+5.2%
Target: 12times/year
71%
Predicted: 10.2times/year83% confident

Value-Add Ratio

58%
+4.5%
Target: 70%
83%
Predicted: 64%88% confident

Real-Time Events & Alerts
2

RISK
medium
...

Inventory Buildup Detected

WIP inventory at Station 5 increased 40% in past 2 days. Bottleneck forming. Downstream stations showing idle time.

Impact: Cash tied up in WIP. Lead time extension risk affecting 5 customer orders. Estimated $18K impact on cash flow.
Recommended Action: Balance line rates. Consider temporary redeployment of 2 operators to Station 5. Implement kanban pull signal to prevent overproduction.
Explainability: Flow analysis shows Station 5 processing time increased 8 minutes per unit. Root cause: new operator learning curve. RL suggests short-term labor rebalancing over process changes.
RL Score: 76%
AI Confidence: 83%
OPPORTUNITY
high
...

Waste Elimination Opportunity Identified

Value stream mapping shows 18 minutes of waiting time per unit in Assembly. Motion study reveals excessive material handling.

Impact: Eliminating this waste could improve throughput 12%, equivalent to $145K annual value.
Recommended Action: Redesign Assembly layout. Implement gravity-feed racks for high-runner parts. Move tools closer to point-of-use. Execute in next Kaizen event (2 weeks).
Explainability: Waste analysis confirmed via Gemba observation and time study. RL simulation of proposed layout predicts 18-minute reduction with 92% confidence. Quick ROI (<3 months).
RL Score: 94%
AI Confidence: 92%

AI-Powered Improvement Opportunities

Total Potential: $354K/year

AI-Driven Dynamic Takt Time

Impact: high
Effort: high
$125K/yr
4-6 months
#5
89% AI confidence

Implement real-time takt time optimization using ML models that adjust production pace based on demand forecasts, inventory levels, and line capacity.

RL Recommendation: Deploy sensors on Line 2 first (most stable throughput). Build 3-month historical model. Start with manual validation for 2 weeks before automation.

Predictive Pull System

Impact: high
Effort: medium
$95K/yr
3-4 months
#4
91% AI confidence

Use predictive analytics to anticipate material needs 48 hours in advance, reducing buffer inventory by 35% while maintaining flow.

RL Recommendation: Start with top 10 high-value SKUs. Integrate with ERP demand signals. Use safety stock for first month, then gradually reduce as confidence builds.

Waste Elimination via Computer Vision

Impact: high
Effort: medium
$78K/yr
3-5 months
#4
84% AI confidence

Deploy CV cameras to detect 7 wastes in real-time: overproduction, waiting, transport, over-processing, inventory, motion, defects.

RL Recommendation: Install 5 cameras in Assembly area (highest waste concentration). Train on 1000 labeled examples per waste type. Start with motion and waiting detection.

Automated Value Stream Mapping

Impact: medium
Effort: medium
$56K/yr
2-3 months
#3
86% AI confidence

Continuous VSM updates using IoT data streams. Auto-identify bottlenecks and generate improvement recommendations every 24 hours.

RL Recommendation: Connect to existing SCADA/MES systems. Build baseline VSM, then track deviations. Alert when process times exceed 15% of baseline.