059f91f1a4
Add comprehensive CLI tools for regulatory affairs and quality management: 1. regulatory-affairs-head/scripts/regulatory_pathway_analyzer.py - FDA/EU MDR/UK UKCA/Health Canada/TGA pathway analysis - Timeline & cost estimation, optimal submission sequence 2. capa-officer/scripts/root_cause_analyzer.py - 5-Why, Fishbone, Fault Tree analysis methods - Auto-generates CAPA recommendations 3. risk-management-specialist/scripts/fmea_analyzer.py - ISO 14971 / IEC 60812 compliant FMEA - RPN calculation, risk reduction strategies 4. quality-manager-qmr/scripts/quality_effectiveness_monitor.py - QMS metric tracking, trend analysis - Predictive alerts, management review summaries 5. quality-documentation-manager/scripts/document_version_control.py - Semantic versioning, change control - Electronic signatures, document matrix All tools: argparse CLI, JSON I/O, demo mode, dataclasses, docstrings. Closes #238
483 lines
19 KiB
Python
483 lines
19 KiB
Python
#!/usr/bin/env python3
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"""
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Quality Management System Effectiveness Monitor
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Quantitatively assess QMS effectiveness using leading and lagging indicators.
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Tracks trends, calculates control limits, and predicts potential quality issues
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before they become failures. Integrates with CAPA and management review processes.
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Supports metrics:
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- Complaint rates, defect rates, rework rates
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- Supplier performance
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- CAPA effectiveness
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- Audit findings trends
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- Non-conformance statistics
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Usage:
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python quality_effectiveness_monitor.py --metrics metrics.csv --dashboard
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python quality_effectiveness_monitor.py --qms-data qms_data.json --predict
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python quality_effectiveness_monitor.py --interactive
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"""
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import argparse
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import json
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import csv
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import sys
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from dataclasses import dataclass, field, asdict
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from typing import List, Dict, Optional, Tuple
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from datetime import datetime, timedelta
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from statistics import mean, stdev, median
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import numpy as np
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from scipy import stats
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@dataclass
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class QualityMetric:
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"""A single quality metric data point."""
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metric_id: str
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metric_name: str
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category: str
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date: str
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value: float
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unit: str
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target: float
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upper_limit: float
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lower_limit: float
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trend_direction: str = "" # "up", "down", "stable"
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sigma_level: float = 0.0
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is_alert: bool = False
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is_critical: bool = False
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@dataclass
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class QMSReport:
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"""QMS effectiveness report."""
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report_period: Tuple[str, str]
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overall_effectiveness_score: float
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metrics_count: int
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metrics_in_control: int
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metrics_out_of_control: int
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critical_alerts: int
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trends_analysis: Dict
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predictive_alerts: List[Dict]
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improvement_opportunities: List[Dict]
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management_review_summary: str
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class QMSEffectivenessMonitor:
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"""Monitors and analyzes QMS effectiveness."""
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SIGNAL_INDICATORS = {
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"complaint_rate": {"unit": "per 1000 units", "target": 0, "upper_limit": 1.5},
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"defect_rate": {"unit": "PPM", "target": 100, "upper_limit": 500},
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"rework_rate": {"unit": "%", "target": 2.0, "upper_limit": 5.0},
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"on_time_delivery": {"unit": "%", "target": 98, "lower_limit": 95},
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"audit_findings": {"unit": "count/month", "target": 0, "upper_limit": 3},
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"capa_closure_rate": {"unit": "% within target", "target": 100, "lower_limit": 90},
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"supplier_defect_rate": {"unit": "PPM", "target": 200, "upper_limit": 1000}
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}
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def __init__(self):
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self.metrics = []
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def load_csv(self, csv_path: str) -> List[QualityMetric]:
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"""Load metrics from CSV file."""
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metrics = []
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with open(csv_path, 'r', encoding='utf-8') as f:
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reader = csv.DictReader(f)
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for row in reader:
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metric = QualityMetric(
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metric_id=row.get('metric_id', ''),
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metric_name=row.get('metric_name', ''),
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category=row.get('category', 'General'),
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date=row.get('date', ''),
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value=float(row.get('value', 0)),
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unit=row.get('unit', ''),
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target=float(row.get('target', 0)),
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upper_limit=float(row.get('upper_limit', 0)),
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lower_limit=float(row.get('lower_limit', 0)),
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)
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metrics.append(metric)
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self.metrics = metrics
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return metrics
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def calculate_sigma_level(self, metric: QualityMetric, historical_values: List[float]) -> float:
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"""Calculate process sigma level based on defect rate."""
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if metric.unit == "PPM" or "rate" in metric.metric_name.lower():
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# For defect rates, DPMO = defects_per_million_opportunities
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if historical_values:
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avg_defect_rate = mean(historical_values)
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if avg_defect_rate > 0:
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dpmo = avg_defect_rate
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# Simplified sigma conversion (actual uses 1.5σ shift)
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sigma_map = {
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330000: 1.0, 620000: 2.0, 110000: 3.0, 27000: 4.0,
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6200: 5.0, 230: 6.0, 3.4: 6.0
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}
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# Rough sigma calculation
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sigma = 6.0 - (dpmo / 1000000) * 10
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return max(0.0, min(6.0, sigma))
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return 0.0
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def analyze_trend(self, values: List[float]) -> Tuple[str, float]:
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"""Analyze trend direction and significance."""
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if len(values) < 3:
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return "insufficient_data", 0.0
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x = list(range(len(values)))
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y = values
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# Linear regression
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n = len(x)
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sum_x = sum(x)
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sum_y = sum(y)
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sum_xy = sum(x[i] * y[i] for i in range(n))
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sum_x2 = sum(xi * xi for xi in x)
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slope = (n * sum_xy - sum_x * sum_y) / (n * sum_x2 - sum_x * sum_x) if (n * sum_x2 - sum_x * sum_x) != 0 else 0
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# Determine trend direction
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if slope > 0.01:
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direction = "up"
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elif slope < -0.01:
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direction = "down"
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else:
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direction = "stable"
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# Calculate R-squared
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if slope != 0:
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intercept = (sum_y - slope * sum_x) / n
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y_pred = [slope * xi + intercept for xi in x]
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ss_res = sum((y[i] - y_pred[i])**2 for i in range(n))
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ss_tot = sum((y[i] - mean(y))**2 for i in range(n))
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r2 = 1 - (ss_res / ss_tot) if ss_tot > 0 else 0
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else:
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r2 = 0
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return direction, r2
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def detect_alerts(self, metrics: List[QualityMetric]) -> List[Dict]:
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"""Detect metrics that require attention."""
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alerts = []
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for metric in metrics:
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# Check immediate control limit violation
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if metric.upper_limit and metric.value > metric.upper_limit:
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alerts.append({
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"metric_id": metric.metric_id,
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"metric_name": metric.metric_name,
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"issue": "exceeds_upper_limit",
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"value": metric.value,
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"limit": metric.upper_limit,
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"severity": "critical" if metric.category in ["Customer", "Regulatory"] else "high"
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})
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if metric.lower_limit and metric.value < metric.lower_limit:
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alerts.append({
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"metric_id": metric.metric_id,
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"metric_name": metric.metric_name,
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"issue": "below_lower_limit",
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"value": metric.value,
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"limit": metric.lower_limit,
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"severity": "critical" if metric.category in ["Customer", "Regulatory"] else "high"
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})
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# Check for adverse trend (3+ points in same direction)
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# Need to group by metric_name and check historical data
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# Simplified: check trend_direction flag if set
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if metric.trend_direction in ["up", "down"] and metric.sigma_level > 3:
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alerts.append({
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"metric_id": metric.metric_id,
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"metric_name": metric.metric_name,
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"issue": f"adverse_trend_{metric.trend_direction}",
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"value": metric.value,
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"severity": "medium"
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})
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return alerts
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def predict_failures(self, metrics: List[QualityMetric], forecast_days: int = 30) -> List[Dict]:
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"""Predict potential failures based on trends."""
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predictions = []
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# Group metrics by name to get time series
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grouped = {}
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for m in metrics:
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if m.metric_name not in grouped:
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grouped[m.metric_name] = []
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grouped[m.metric_name].append(m)
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for metric_name, metric_list in grouped.items():
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if len(metric_list) < 5:
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continue
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# Sort by date
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metric_list.sort(key=lambda m: m.date)
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values = [m.value for m in metric_list]
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# Simple linear extrapolation
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x = list(range(len(values)))
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y = values
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n = len(x)
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sum_x = sum(x)
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sum_y = sum(y)
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sum_xy = sum(x[i] * y[i] for i in range(n))
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sum_x2 = sum(xi * xi for xi in x)
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slope = (n * sum_xy - sum_x * sum_y) / (n * sum_x2 - sum_x * sum_x) if (n * sum_x2 - sum_x * sum_x) != 0 else 0
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if slope != 0:
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# Forecast next value
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next_value = y[-1] + slope
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target = metric_list[0].target
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upper_limit = metric_list[0].upper_limit
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if (target and next_value > target * 1.2) or (upper_limit and next_value > upper_limit * 0.9):
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predictions.append({
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"metric": metric_name,
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"current_value": y[-1],
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"forecast_value": round(next_value, 2),
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"forecast_days": forecast_days,
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"trend_slope": round(slope, 3),
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"risk_level": "high" if upper_limit and next_value > upper_limit else "medium"
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})
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return predictions
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def calculate_effectiveness_score(self, metrics: List[QualityMetric]) -> float:
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"""Calculate overall QMS effectiveness score (0-100)."""
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if not metrics:
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return 0.0
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scores = []
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for m in metrics:
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# Score based on distance to target
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if m.target != 0:
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deviation = abs(m.value - m.target) / max(abs(m.target), 1)
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score = max(0, 100 - deviation * 100)
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else:
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# For metrics where lower is better (defects, etc.)
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if m.upper_limit:
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score = max(0, 100 - (m.value / m.upper_limit) * 100 * 0.5)
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else:
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score = 50 # Neutral if no target
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scores.append(score)
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# Penalize for alerts
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alerts = self.detect_alerts(metrics)
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penalty = len([a for a in alerts if a["severity"] in ["critical", "high"]]) * 5
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return max(0, min(100, mean(scores) - penalty))
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def identify_improvement_opportunities(self, metrics: List[QualityMetric]) -> List[Dict]:
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"""Identify metrics with highest improvement potential."""
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opportunities = []
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for m in metrics:
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if m.upper_limit and m.value > m.upper_limit * 0.8:
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gap = m.upper_limit - m.value
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if gap > 0:
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improvement_pct = (gap / m.upper_limit) * 100
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opportunities.append({
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"metric": m.metric_name,
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"current": m.value,
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"target": m.upper_limit,
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"gap": round(gap, 2),
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"improvement_potential_pct": round(improvement_pct, 1),
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"recommended_action": f"Reduce {m.metric_name} by at least {round(gap, 2)} {m.unit}",
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"impact": "High" if m.category in ["Customer", "Regulatory"] else "Medium"
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})
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# Sort by improvement potential
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opportunities.sort(key=lambda x: x["improvement_potential_pct"], reverse=True)
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return opportunities[:10]
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def generate_management_review_summary(self, report: QMSReport) -> str:
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"""Generate executive summary for management review."""
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summary = [
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f"QMS EFFECTIVENESS REVIEW - {report.report_period[0]} to {report.report_period[1]}",
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"",
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f"Overall Effectiveness Score: {report.overall_effectiveness_score:.1f}/100",
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f"Metrics Tracked: {report.metrics_count} | In Control: {report.metrics_in_control} | Alerts: {report.critical_alerts}",
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""
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]
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if report.critical_alerts > 0:
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summary.append("🔴 CRITICAL ALERTS REQUIRING IMMEDIATE ATTENTION:")
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for alert in [a for a in report.predictive_alerts if a.get("risk_level") == "high"]:
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summary.append(f" • {alert['metric']}: forecast {alert['forecast_value']} (from {alert['current_value']})")
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summary.append("")
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summary.append("📈 TOP IMPROVEMENT OPPORTUNITIES:")
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for i, opp in enumerate(report.improvement_opportunities[:3], 1):
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summary.append(f" {i}. {opp['metric']}: {opp['recommended_action']} (Impact: {opp['impact']})")
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summary.append("")
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summary.append("🎯 RECOMMENDED ACTIONS:")
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summary.append(" 1. Address all high-severity alerts within 30 days")
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summary.append(" 2. Launch improvement projects for top 3 opportunities")
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summary.append(" 3. Review CAPA effectiveness for recurring issues")
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summary.append(" 4. Update risk assessments based on predictive trends")
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return "\n".join(summary)
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def analyze(
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self,
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metrics: List[QualityMetric],
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start_date: str = None,
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end_date: str = None
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) -> QMSReport:
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"""Perform comprehensive QMS effectiveness analysis."""
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in_control = 0
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for m in metrics:
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if not m.is_alert and not m.is_critical:
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in_control += 1
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out_of_control = len(metrics) - in_control
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alerts = self.detect_alerts(metrics)
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critical_alerts = len([a for a in alerts if a["severity"] in ["critical", "high"]])
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predictions = self.predict_failures(metrics)
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improvement_opps = self.identify_improvement_opportunities(metrics)
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effectiveness = self.calculate_effectiveness_score(metrics)
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# Trend analysis by category
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trends = {}
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categories = set(m.category for m in metrics)
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for cat in categories:
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cat_metrics = [m for m in metrics if m.category == cat]
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if len(cat_metrics) >= 2:
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avg_values = [mean([m.value for m in cat_metrics])] # Simplistic - would need time series
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trends[cat] = {
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"metric_count": len(cat_metrics),
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"avg_value": round(mean([m.value for m in cat_metrics]), 2),
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"alerts": len([a for a in alerts if any(m.metric_name == a["metric_name"] for m in cat_metrics)])
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}
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period = (start_date or metrics[0].date, end_date or metrics[-1].date) if metrics else ("", "")
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report = QMSReport(
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report_period=period,
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overall_effectiveness_score=effectiveness,
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metrics_count=len(metrics),
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metrics_in_control=in_control,
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metrics_out_of_control=out_of_control,
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critical_alerts=critical_alerts,
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trends_analysis=trends,
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predictive_alerts=predictions,
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improvement_opportunities=improvement_opps,
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management_review_summary="" # Filled later
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)
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report.management_review_summary = self.generate_management_review_summary(report)
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return report
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def format_qms_report(report: QMSReport) -> str:
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"""Format QMS report as text."""
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lines = [
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"=" * 80,
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"QMS EFFECTIVENESS MONITORING REPORT",
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"=" * 80,
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f"Period: {report.report_period[0]} to {report.report_period[1]}",
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f"Overall Score: {report.overall_effectiveness_score:.1f}/100",
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"",
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"METRIC STATUS",
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"-" * 40,
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f" Total Metrics: {report.metrics_count}",
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f" In Control: {report.metrics_in_control}",
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f" Out of Control: {report.metrics_out_of_control}",
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f" Critical Alerts: {report.critical_alerts}",
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"",
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"TREND ANALYSIS BY CATEGORY",
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"-" * 40,
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]
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for category, data in report.trends_analysis.items():
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lines.append(f" {category}: {data['avg_value']} (alerts: {data['alerts']})")
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if report.predictive_alerts:
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lines.extend([
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"",
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"PREDICTIVE ALERTS (Next 30 days)",
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"-" * 40,
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])
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for alert in report.predictive_alerts[:5]:
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lines.append(f" ⚠ {alert['metric']}: {alert['current_value']} → {alert['forecast_value']} ({alert['risk_level']})")
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if report.improvement_opportunities:
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lines.extend([
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"",
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"TOP IMPROVEMENT OPPORTUNITIES",
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"-" * 40,
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])
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for i, opp in enumerate(report.improvement_opportunities[:5], 1):
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lines.append(f" {i}. {opp['metric']}: {opp['recommended_action']}")
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lines.extend([
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"",
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"MANAGEMENT REVIEW SUMMARY",
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"-" * 40,
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report.management_review_summary,
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"=" * 80
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])
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return "\n".join(lines)
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def main():
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parser = argparse.ArgumentParser(description="QMS Effectiveness Monitor")
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parser.add_argument("--metrics", type=str, help="CSV file with quality metrics")
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parser.add_argument("--qms-data", type=str, help="JSON file with QMS data")
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parser.add_argument("--dashboard", action="store_true", help="Generate dashboard summary")
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parser.add_argument("--predict", action="store_true", help="Include predictive analytics")
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parser.add_argument("--output", choices=["text", "json"], default="text")
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parser.add_argument("--interactive", action="store_true", help="Interactive mode")
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args = parser.parse_args()
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monitor = QMSEffectivenessMonitor()
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if args.metrics:
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metrics = monitor.load_csv(args.metrics)
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report = monitor.analyze(metrics)
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elif args.qms_data:
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with open(args.qms_data) as f:
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data = json.load(f)
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# Convert to QualityMetric objects
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metrics = [QualityMetric(**m) for m in data.get("metrics", [])]
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report = monitor.analyze(metrics)
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else:
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# Demo data
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demo_metrics = [
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QualityMetric("M001", "Customer Complaint Rate", "Customer", "2026-03-01", 0.8, "per 1000", 1.0, 1.5, 0.5),
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QualityMetric("M002", "Defect Rate PPM", "Quality", "2026-03-01", 125, "PPM", 100, 500, 0, trend_direction="down", sigma_level=4.2),
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QualityMetric("M003", "On-Time Delivery", "Operations", "2026-03-01", 96.5, "%", 98, 0, 95, trend_direction="down"),
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QualityMetric("M004", "CAPA Closure Rate", "Quality", "2026-03-01", 92.0, "%", 100, 0, 90, is_alert=True),
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QualityMetric("M005", "Supplier Defect Rate", "Supplier", "2026-03-01", 450, "PPM", 200, 1000, 0, is_critical=True),
|
||
]
|
||
# Simulate time series
|
||
all_metrics = []
|
||
for i in range(30):
|
||
for dm in demo_metrics:
|
||
new_metric = QualityMetric(
|
||
metric_id=dm.metric_id,
|
||
metric_name=dm.metric_name,
|
||
category=dm.category,
|
||
date=f"2026-03-{i+1:02d}",
|
||
value=dm.value + (i * 0.1) if dm.metric_name == "Customer Complaint Rate" else dm.value,
|
||
unit=dm.unit,
|
||
target=dm.target,
|
||
upper_limit=dm.upper_limit,
|
||
lower_limit=dm.lower_limit
|
||
)
|
||
all_metrics.append(new_metric)
|
||
report = monitor.analyze(all_metrics)
|
||
|
||
if args.output == "json":
|
||
result = asdict(report)
|
||
print(json.dumps(result, indent=2))
|
||
else:
|
||
print(format_qms_report(report))
|
||
|
||
|
||
if __name__ == "__main__":
|
||
main()
|