Prototype Demo - Unified Machines

Manufacturing Optimization Prototype

Explore how Causal World Models predict outcomes and optimize interventions

Select a Scenario to Explore:

🏭 Production Line Optimization

Predict impact of changing machine speed on output quality

⚙️ Maintenance Scheduling

Counterfactual: What if we delayed maintenance by 2 weeks?

📦 Supply Chain Intervention

Causal effect of supplier change on delivery times

Causal Analysis Results

Causal Graph: Production Line System

Machine Speed

→

Temperature

→

Product Quality

Material Type

→

Product Quality

Causal Structure: Machine speed directly affects temperature, which in turn affects quality. Material type has an independent causal effect on quality.

🎯 Current State (Observational)

Machine Speed: 100 units/hour
Temperature: 85°C
Material: Type A
Product Quality: 92% pass rate
Defect Rate: 8%

🔮 Intervention Prediction (Causal Reasoning)

Proposed Intervention: Increase machine speed to 120 units/hour

Causal Chain Prediction:
1. Speed ↑ 20% → Temperature ↑ 12°C (to 97°C)
2. Temperature ↑ 12°C → Quality ↓ 7% (to 85% pass rate)
3. Defect Rate ↑ to 15%

Recommendation: ❌ Do NOT increase speed without cooling system upgrade

-7% Quality Impact

+87% Defect Increase

✅ Optimized Intervention (CWM Recommendation)

Alternative Strategy: Increase speed to 110 units/hour + upgrade cooling

Causal Chain Prediction:
1. Speed ↑ 10% + Cooling upgrade → Temperature stable at 85°C
2. Temperature stable → Quality maintained at 92%
3. Output ↑ 10% with no quality degradation

Recommendation: ✅ Implement this intervention for optimal ROI

+10% Output

0% Quality Impact

$50K Annual Savings

# Causal World Model - Simplified Implementation

class CausalWorldModel:
    def __init__(self):
        # Define causal graph structure
        self.causal_graph = {
            'machine_speed': ['temperature'],
            'temperature': ['quality'],
            'material_type': ['quality']
        }
        
    def predict_intervention(self, variable, new_value):
        # Perform do-calculus to predict intervention effects
        affected_vars = self.get_descendants(variable)
        predictions = {}
        
        for var in affected_vars:
            # Use learned causal mechanisms
            predictions[var] = self.causal_mechanism(var, new_value)
            
        return predictions
    
    def counterfactual(self, query):
        # Answer "What if?" questions
        return self.abduction_action_prediction(query)

# Example usage
model = CausalWorldModel()
result = model.predict_intervention('machine_speed', 120)
print(result)  # {'temperature': 97, 'quality': 85}
                    

Technical Specifications

Data Requirements

Observational data: 10K+ samples
Interventional data: 1K+ experiments
Domain expert annotations
Temporal sequences for dynamics

Model Architecture

Base: Fine-tuned Llama 3 (8B params)
Causal layer: 500M params
World model: 200M params
Total: ~8.7B parameters

Performance Metrics

Causal accuracy: 87% on test set
Intervention prediction: ±5% error
Inference latency: <100ms
ROI for customers: 10-20% gains

Infrastructure

AWS SageMaker for training
Lambda Labs for GPU compute
Pinecone for vector storage
FastAPI for inference serving

Implementation Roadmap

Phase 1: Foundation (Months 1-3)

Goal: Build basic causal reasoning prototype for one manufacturing use case
Deliverables: Fine-tuned base model, simple causal graph (5-10 variables), intervention prediction API
Team: 1 ML engineer + 1 domain expert
Budget: $50K-75K

Phase 2: Validation (Months 4-6)

Goal: Deploy to 2 pilot customers, validate causal predictions against real outcomes
Deliverables: Production system, validated causal models, case studies showing 10-15% efficiency gains
Team: 2 ML engineers + 1 full-stack engineer
Budget: $100K-150K

Phase 3: Scale (Months 7-12)

Goal: Expand to 10+ customers, add second vertical (logistics or healthcare)
Deliverables: Multi-vertical framework, automated causal discovery, published research paper
Team: 5-8 engineers + 2 researchers
Budget: $300K-500K

Causal World Models in Action