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# Dynamic Business Rules Engine
## Overview
The Dynamic Business Rules Engine replaces hardcoded forecasting multipliers with **learned values from historical data**. Instead of assuming "rain = -15% impact" for all products, it learns the actual impact per product from real sales data.
## Problem Statement
### Current Hardcoded Approach
The forecasting service currently uses hardcoded business rules:
```python
# Hardcoded weather adjustments
weather_adjustments = {
'rain': 0.85, # -15% impact
'snow': 0.75, # -25% impact
'extreme_heat': 0.90 # -10% impact
}
# Hardcoded holiday adjustment
holiday_multiplier = 1.5 # +50% for all holidays
# Hardcoded event adjustment
event_multiplier = 1.3 # +30% for all events
```
### Problems with Hardcoded Rules
1. **One-size-fits-all**: Bread sales might drop 5% in rain, but pastry sales might increase 10%
2. **No adaptation**: Rules never update as customer behavior changes
3. **Missing nuances**: Christmas vs Easter have different impacts, but both get +50%
4. **No confidence scoring**: Can't tell if a rule is based on 10 observations or 1,000
5. **Manual maintenance**: Requires developer to change code to update rules
## Solution: Dynamic Learning
The Dynamic Rules Engine:
1. ✅ **Learns from data**: Calculates actual impact from historical sales
2. ✅ **Product-specific**: Each product gets its own learned rules
3. ✅ **Statistical validation**: Uses t-tests to ensure rules are significant
4. ✅ **Confidence scoring**: Provides confidence levels (0-100) for each rule
5. ✅ **Automatic insights**: Generates insights when learned rules differ from hardcoded assumptions
6. ✅ **Continuous improvement**: Can be re-run with new data to update rules
## Architecture
```
┌─────────────────────────────────────────────────────────────────┐
│ Dynamic Rules Engine │
├─────────────────────────────────────────────────────────────────┤
│ │
│ Historical Sales Data + External Data (Weather/Holidays) │
│ ↓ │
│ Statistical Analysis │
│ (T-tests, Effect Sizes, p-values) │
│ ↓ │
│ ┌──────────────────────┐ │
│ │ Learned Rules │ │
│ ├──────────────────────┤ │
│ │ • Weather impacts │ │
│ │ • Holiday multipliers│ │
│ │ • Event impacts │ │
│ │ • Day-of-week patterns │
│ │ • Monthly seasonality│ │
│ └──────────────────────┘ │
│ ↓ │
│ ┌──────────────────────┐ │
│ │ Generated Insights │ │
│ ├──────────────────────┤ │
│ │ • Rule mismatches │ │
│ │ • Strong patterns │ │
│ │ • Recommendations │ │
│ └──────────────────────┘ │
│ ↓ │
│ Posted to AI Insights Service │
│ │
└─────────────────────────────────────────────────────────────────┘
```
## Usage
### Basic Usage
```python
from app.ml.dynamic_rules_engine import DynamicRulesEngine
import pandas as pd
# Initialize engine
engine = DynamicRulesEngine()
# Prepare data
sales_data = pd.DataFrame({
'date': [...],
'quantity': [...]
})
external_data = pd.DataFrame({
'date': [...],
'weather_condition': ['rain', 'clear', 'snow', ...],
'temperature': [15.2, 18.5, 3.1, ...],
'is_holiday': [False, False, True, ...],
'holiday_name': [None, None, 'Christmas', ...],
'holiday_type': [None, None, 'religious', ...]
})
# Learn all rules
results = await engine.learn_all_rules(
tenant_id='tenant-123',
inventory_product_id='product-456',
sales_data=sales_data,
external_data=external_data,
min_samples=10
)
# Results contain learned rules and insights
print(f"Learned {len(results['rules'])} rule categories")
print(f"Generated {len(results['insights'])} insights")
```
### Using Orchestrator (Recommended)
```python
from app.ml.rules_orchestrator import RulesOrchestrator
# Initialize orchestrator
orchestrator = RulesOrchestrator(
ai_insights_base_url="http://ai-insights-service:8000"
)
# Learn rules and automatically post insights
results = await orchestrator.learn_and_post_rules(
tenant_id='tenant-123',
inventory_product_id='product-456',
sales_data=sales_data,
external_data=external_data
)
print(f"Insights posted: {results['insights_posted']}")
print(f"Insights failed: {results['insights_failed']}")
# Get learned rules for forecasting
rules = await orchestrator.get_learned_rules_for_forecasting('product-456')
# Get specific multiplier with fallback
rain_multiplier = orchestrator.get_rule_multiplier(
inventory_product_id='product-456',
rule_type='weather',
key='rain',
default=0.85 # Fallback to hardcoded if not learned
)
```
## Learned Rules Structure
### Weather Rules
```python
{
"weather": {
"baseline_avg": 105.3, # Average sales on clear days
"conditions": {
"rain": {
"learned_multiplier": 0.88, # Actual impact: -12%
"learned_impact_pct": -12.0,
"sample_size": 37,
"avg_quantity": 92.7,
"p_value": 0.003,
"significant": true
},
"snow": {
"learned_multiplier": 0.73, # Actual impact: -27%
"learned_impact_pct": -27.0,
"sample_size": 12,
"avg_quantity": 76.9,
"p_value": 0.001,
"significant": true
}
}
}
}
```
### Holiday Rules
```python
{
"holidays": {
"baseline_avg": 100.0, # Non-holiday average
"hardcoded_multiplier": 1.5, # Current +50%
"holiday_types": {
"religious": {
"learned_multiplier": 1.68, # Actual: +68%
"learned_impact_pct": 68.0,
"sample_size": 8,
"avg_quantity": 168.0,
"p_value": 0.002,
"significant": true
},
"national": {
"learned_multiplier": 1.25, # Actual: +25%
"learned_impact_pct": 25.0,
"sample_size": 5,
"avg_quantity": 125.0,
"p_value": 0.045,
"significant": true
}
},
"overall_learned_multiplier": 1.52
}
}
```
### Day-of-Week Rules
```python
{
"day_of_week": {
"overall_avg": 100.0,
"days": {
"Monday": {
"day_of_week": 0,
"learned_multiplier": 0.85,
"impact_pct": -15.0,
"avg_quantity": 85.0,
"std_quantity": 12.3,
"sample_size": 52,
"coefficient_of_variation": 0.145
},
"Saturday": {
"day_of_week": 5,
"learned_multiplier": 1.32,
"impact_pct": 32.0,
"avg_quantity": 132.0,
"std_quantity": 18.7,
"sample_size": 52,
"coefficient_of_variation": 0.142
}
}
}
}
```
## Generated Insights Examples
### Weather Rule Mismatch
```json
{
"type": "optimization",
"priority": "high",
"category": "forecasting",
"title": "Weather Rule Mismatch: Rain",
"description": "Learned rain impact is -12.0% vs hardcoded -15.0%. Updating rule could improve forecast accuracy by 3.0%.",
"impact_type": "forecast_improvement",
"impact_value": 3.0,
"impact_unit": "percentage_points",
"confidence": 85,
"metrics_json": {
"weather_condition": "rain",
"learned_impact_pct": -12.0,
"hardcoded_impact_pct": -15.0,
"difference_pct": 3.0,
"baseline_avg": 105.3,
"condition_avg": 92.7,
"sample_size": 37,
"p_value": 0.003
},
"actionable": true,
"recommendation_actions": [
{
"label": "Update Weather Rule",
"action": "update_weather_multiplier",
"params": {
"condition": "rain",
"new_multiplier": 0.88
}
}
]
}
```
### Holiday Optimization
```json
{
"type": "recommendation",
"priority": "high",
"category": "forecasting",
"title": "Holiday Rule Optimization: religious",
"description": "religious shows 68.0% impact vs hardcoded +50%. Using learned multiplier 1.68x could improve forecast accuracy.",
"impact_type": "forecast_improvement",
"impact_value": 18.0,
"confidence": 82,
"metrics_json": {
"holiday_type": "religious",
"learned_multiplier": 1.68,
"hardcoded_multiplier": 1.5,
"learned_impact_pct": 68.0,
"hardcoded_impact_pct": 50.0,
"sample_size": 8
},
"actionable": true,
"recommendation_actions": [
{
"label": "Update Holiday Rule",
"action": "update_holiday_multiplier",
"params": {
"holiday_type": "religious",
"new_multiplier": 1.68
}
}
]
}
```
### Strong Day-of-Week Pattern
```json
{
"type": "insight",
"priority": "medium",
"category": "forecasting",
"title": "Saturday Pattern: 32% Higher",
"description": "Saturday sales average 132.0 units (+32.0% vs weekly average 100.0). Consider this pattern in production planning.",
"impact_type": "operational_insight",
"impact_value": 32.0,
"confidence": 88,
"metrics_json": {
"day_of_week": "Saturday",
"day_multiplier": 1.32,
"impact_pct": 32.0,
"day_avg": 132.0,
"overall_avg": 100.0,
"sample_size": 52
},
"actionable": true,
"recommendation_actions": [
{
"label": "Adjust Production Schedule",
"action": "adjust_weekly_production",
"params": {
"day": "Saturday",
"multiplier": 1.32
}
}
]
}
```
## Confidence Scoring
Confidence (0-100) is calculated based on:
1. **Sample Size** (0-50 points):
- 100+ samples: 50 points
- 50-99 samples: 40 points
- 30-49 samples: 30 points
- 20-29 samples: 20 points
- <20 samples: 10 points
2. **Statistical Significance** (0-50 points):
- p < 0.001: 50 points
- p < 0.01: 45 points
- p < 0.05: 35 points
- p < 0.1: 20 points
- p >= 0.1: 10 points
```python
confidence = min(100, sample_score + significance_score)
```
Examples:
- 150 samples, p=0.001 → **100 confidence**
- 50 samples, p=0.03 → **75 confidence**
- 15 samples, p=0.12 → **20 confidence** (low)
## Integration with Forecasting
### Option 1: Replace Hardcoded Values
```python
# Before (hardcoded)
if weather == 'rain':
forecast *= 0.85
# After (learned)
rain_multiplier = rules_engine.get_rule(
inventory_product_id=product_id,
rule_type='weather',
key='rain'
) or 0.85 # Fallback to hardcoded
if weather == 'rain':
forecast *= rain_multiplier
```
### Option 2: Prophet Regressor Integration
```python
# Export learned rules
rules = orchestrator.get_learned_rules_for_forecasting(product_id)
# Apply as Prophet regressors
for condition, rule in rules['weather']['conditions'].items():
# Create binary regressor for each condition
df[f'is_{condition}'] = (df['weather_condition'] == condition).astype(int)
# Weight by learned multiplier
df[f'{condition}_weighted'] = df[f'is_{condition}'] * rule['learned_multiplier']
# Add to Prophet
prophet.add_regressor(f'{condition}_weighted')
```
## Periodic Updates
Rules should be re-learned periodically as new data accumulates:
```python
# Weekly or monthly update
results = await orchestrator.update_rules_periodically(
tenant_id='tenant-123',
inventory_product_id='product-456',
sales_data=updated_sales_data,
external_data=updated_external_data
)
# New insights will be posted if rules have changed significantly
print(f"Rules updated, {results['insights_posted']} new insights")
```
## API Integration
The Rules Orchestrator automatically posts insights to the AI Insights Service:
```python
# POST to /api/v1/ai-insights/tenants/{tenant_id}/insights
{
"tenant_id": "tenant-123",
"type": "optimization",
"priority": "high",
"category": "forecasting",
"title": "Weather Rule Mismatch: Rain",
"description": "...",
"confidence": 85,
"metrics_json": {...},
"actionable": true,
"recommendation_actions": [...]
}
```
Insights can then be:
1. Viewed in the AI Insights frontend page
2. Retrieved by orchestration service for automated application
3. Tracked for feedback and learning
## Testing
Run comprehensive tests:
```bash
cd services/forecasting
pytest tests/test_dynamic_rules_engine.py -v
```
Tests cover:
- Weather rules learning
- Holiday rules learning
- Day-of-week patterns
- Monthly seasonality
- Insight generation
- Confidence calculation
- Insufficient sample handling
## Performance
**Learning Time**: ~1-2 seconds for 1 year of daily data (365 observations)
**Memory**: ~50 MB for rules storage per 1,000 products
**Accuracy Improvement**: Expected **5-15% MAPE reduction** by using learned rules vs hardcoded
## Minimum Data Requirements
| Rule Type | Minimum Samples | Recommended |
|-----------|----------------|-------------|
| Weather (per condition) | 10 days | 30+ days |
| Holiday (per type) | 5 occurrences | 10+ occurrences |
| Event (per type) | 10 events | 20+ events |
| Day-of-week | 10 weeks | 26+ weeks |
| Monthly | 2 months | 12+ months |
**Overall**: 3-6 months of historical data recommended for reliable rules.
## Limitations
1. **Cold Start**: New products need 60-90 days before reliable rules can be learned
2. **Rare Events**: Conditions that occur <10 times won't have statistically significant rules
3. **Distribution Shift**: Rules assume future behavior similar to historical patterns
4. **External Factors**: Can't learn from factors not tracked in external_data
## Future Enhancements
1. **Transfer Learning**: Use rules from similar products for cold start
2. **Bayesian Updates**: Incrementally update rules as new data arrives
3. **Hierarchical Rules**: Learn category-level rules when product-level data insufficient
4. **Interaction Effects**: Learn combined impacts (e.g., "rainy Saturday" vs "rainy Monday")
5. **Drift Detection**: Alert when learned rules become invalid due to behavior changes
## Summary
The Dynamic Business Rules Engine transforms hardcoded assumptions into **data-driven, product-specific, continuously-improving forecasting rules**. By learning from actual historical patterns and automatically generating insights, it enables the forecasting service to adapt to real customer behavior and improve accuracy over time.
**Key Benefits**:
- ✅ 5-15% MAPE improvement
- ✅ Product-specific customization
- ✅ Automatic insight generation
- ✅ Statistical validation
- ✅ Continuous improvement
- ✅ Zero manual rule maintenance