refactor: Convert internal services to structured JSON reasoning
Convert pipe-separated reasoning codes to structured JSON format for: - Safety stock calculator (statistical calculations, errors) - Price forecaster (procurement recommendations, volatility) - Order optimization (EOQ, tier pricing) This enables i18n translation of internal calculation reasoning and provides structured data for frontend AI insights display. Benefits: - Consistent with PO/Batch reasoning_data format - Frontend can translate using same i18n infrastructure - Structured parameters enable rich UI visualization - No legacy string parsing needed Changes: - safety_stock_calculator.py: Replace reasoning str with reasoning_data dict - price_forecaster.py: Convert recommendation reasoning to structured format - optimization.py: Update EOQ and tier pricing to use reasoning_data Part of complete i18n implementation for AI insights.
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Claude
@@ -25,7 +25,7 @@ class SafetyStockResult:
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lead_time_days: int
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calculation_method: str
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confidence: str # 'high', 'medium', 'low'
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reasoning: str
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reasoning_data: dict
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@dataclass
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@@ -108,7 +108,13 @@ class SafetyStockCalculator:
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lead_time_days=lead_time_days,
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calculation_method='zero_due_to_invalid_inputs',
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confidence='low',
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reasoning='ERROR:LEAD_TIME_INVALID' # Error code for i18n translation
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reasoning_data={
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'type': 'error_lead_time_invalid',
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'parameters': {
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'lead_time_days': lead_time_days,
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'demand_std_dev': demand_std_dev
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}
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}
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)
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# Safety Stock = Z × σ × √L
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@@ -117,9 +123,6 @@ class SafetyStockCalculator:
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# Determine confidence
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confidence = self._determine_confidence(demand_std_dev, lead_time_days)
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# Use calculation method as reasoning code with parameters
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reasoning = f"CALC:STATISTICAL_Z_SCORE|service_level={service_level*100:.1f}|z_score={z_score:.2f}|demand_std={demand_std_dev:.2f}|lead_time={lead_time_days}"
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return SafetyStockResult(
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safety_stock_quantity=Decimal(str(round(safety_stock, 2))),
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service_level=service_level,
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@@ -128,7 +131,18 @@ class SafetyStockCalculator:
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lead_time_days=lead_time_days,
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calculation_method='statistical_z_score',
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confidence=confidence,
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reasoning=reasoning
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reasoning_data={
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'type': 'statistical_z_score',
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'calculation_method': 'statistical_z_score',
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'parameters': {
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'service_level': round(service_level * 100, 1),
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'z_score': round(z_score, 2),
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'demand_std_dev': round(demand_std_dev, 2),
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'lead_time_days': lead_time_days,
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'safety_stock': round(safety_stock, 2)
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},
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'confidence': confidence
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}
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)
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def calculate_from_demand_history(
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@@ -158,7 +172,13 @@ class SafetyStockCalculator:
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lead_time_days=lead_time_days,
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calculation_method='insufficient_data',
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confidence='low',
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reasoning='ERROR:INSUFFICIENT_DATA' # Error code for i18n translation
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reasoning_data={
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'type': 'error_insufficient_data',
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'parameters': {
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'data_points': len(daily_demands) if daily_demands else 0,
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'min_required': 2
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}
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}
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)
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# Calculate standard deviation
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@@ -209,9 +229,6 @@ class SafetyStockCalculator:
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confidence = 'high' if lead_time_std_dev > 0 else 'medium'
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# Use calculation method as reasoning code with parameters
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reasoning = f"CALC:ADVANCED_VARIABILITY|demand_std={demand_std_dev:.2f}|lead_time_std={lead_time_std_dev:.1f}"
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return SafetyStockResult(
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safety_stock_quantity=Decimal(str(round(safety_stock, 2))),
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service_level=service_level,
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@@ -220,7 +237,20 @@ class SafetyStockCalculator:
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lead_time_days=lead_time_mean,
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calculation_method='statistical_with_lead_time_variability',
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confidence=confidence,
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reasoning=reasoning
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reasoning_data={
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'type': 'advanced_variability',
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'calculation_method': 'statistical_with_lead_time_variability',
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'parameters': {
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'service_level': round(service_level * 100, 1),
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'z_score': round(z_score, 2),
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'demand_mean': round(demand_mean, 2),
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'demand_std_dev': round(demand_std_dev, 2),
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'lead_time_mean': lead_time_mean,
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'lead_time_std_dev': round(lead_time_std_dev, 1),
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'safety_stock': round(safety_stock, 2)
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},
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'confidence': confidence
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}
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)
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def calculate_using_fixed_percentage(
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@@ -245,9 +275,6 @@ class SafetyStockCalculator:
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lead_time_demand = average_demand * lead_time_days
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safety_stock = lead_time_demand * percentage
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# Use calculation method as reasoning code with parameters
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reasoning = f"CALC:FIXED_PERCENTAGE|percentage={percentage*100:.0f}|lead_time_demand={lead_time_demand:.2f}"
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return SafetyStockResult(
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safety_stock_quantity=Decimal(str(round(safety_stock, 2))),
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service_level=0.0, # Not based on service level
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@@ -256,7 +283,18 @@ class SafetyStockCalculator:
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lead_time_days=lead_time_days,
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calculation_method='fixed_percentage',
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confidence='low',
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reasoning=reasoning
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reasoning_data={
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'type': 'fixed_percentage',
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'calculation_method': 'fixed_percentage',
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'parameters': {
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'percentage': round(percentage * 100, 0),
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'average_demand': round(average_demand, 2),
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'lead_time_days': lead_time_days,
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'lead_time_demand': round(lead_time_demand, 2),
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'safety_stock': round(safety_stock, 2)
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},
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'confidence': 'low'
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}
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)
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def calculate_batch_safety_stock(
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@@ -432,5 +470,5 @@ class SafetyStockCalculator:
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'lead_time_days': result.lead_time_days,
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'calculation_method': result.calculation_method,
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'confidence': result.confidence,
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'reasoning': result.reasoning
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'reasoning_data': result.reasoning_data
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}
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