#!/usr/bin/env python3
"""
Management Review Tracker - QMS Management Review Preparation and Tracking

Tracks management review inputs, action items, and generates review reports
for ISO 13485 compliance.

Usage:
    python management_review_tracker.py --data review_data.json
    python management_review_tracker.py --interactive
    python management_review_tracker.py --data review_data.json --output json
"""

import argparse
import json
import sys
from dataclasses import dataclass, field, asdict
from datetime import datetime, timedelta
from typing import List, Dict, Optional
from enum import Enum


class ActionStatus(Enum):
    NOT_STARTED = "Not Started"
    IN_PROGRESS = "In Progress"
    ON_HOLD = "On Hold"
    OVERDUE = "Overdue"
    COMPLETE = "Complete"
    VERIFIED = "Verified"


class ActionPriority(Enum):
    HIGH = "High"
    MEDIUM = "Medium"
    LOW = "Low"


class InputStatus(Enum):
    NOT_COLLECTED = "Not Collected"
    IN_PROGRESS = "In Progress"
    COMPLETE = "Complete"
    REVIEWED = "Reviewed"


@dataclass
class ReviewInput:
    topic: str
    responsible: str
    status: InputStatus
    data_period: str
    summary: str = ""
    concerns: List[str] = field(default_factory=list)


@dataclass
class ActionItem:
    action_id: str
    description: str
    owner: str
    due_date: str
    priority: ActionPriority
    status: ActionStatus
    source_review: str
    category: str = "Improvement"
    completion_date: Optional[str] = None
    notes: str = ""


@dataclass
class ReviewMetrics:
    complaint_rate: float = 0.0
    complaint_count: int = 0
    capa_open: int = 0
    capa_overdue: int = 0
    capa_effectiveness: float = 0.0
    audit_findings_open: int = 0
    audit_findings_major: int = 0
    first_pass_yield: float = 0.0
    customer_satisfaction: float = 0.0
    training_compliance: float = 0.0


@dataclass
class ManagementReview:
    review_date: str
    review_type: str
    period_start: str
    period_end: str
    inputs: List[ReviewInput]
    actions: List[ActionItem]
    metrics: ReviewMetrics
    decisions: List[str] = field(default_factory=list)
    attendees: List[str] = field(default_factory=list)


class ManagementReviewTracker:
    """Tracks and reports management review status."""

    # Required ISO 13485 inputs
    REQUIRED_INPUTS = [
        ("Audit Results", "QA Manager"),
        ("Customer Feedback", "Customer Quality"),
        ("Process Performance", "Operations"),
        ("Product Conformity", "QC Manager"),
        ("CAPA Status", "CAPA Officer"),
        ("Previous Actions", "QMR"),
        ("QMS Changes", "RA Manager"),
        ("Recommendations", "All Managers"),
    ]

    def __init__(self, review: ManagementReview):
        self.review = review
        self.today = datetime.now()

    def check_input_readiness(self) -> Dict:
        """Check readiness of all required inputs."""
        readiness = {
            "total_required": len(self.REQUIRED_INPUTS),
            "complete": 0,
            "in_progress": 0,
            "not_started": 0,
            "missing_topics": [],
            "readiness_score": 0.0
        }

        input_topics = {inp.topic: inp for inp in self.review.inputs}

        for topic, responsible in self.REQUIRED_INPUTS:
            if topic in input_topics:
                inp = input_topics[topic]
                if inp.status in [InputStatus.COMPLETE, InputStatus.REVIEWED]:
                    readiness["complete"] += 1
                elif inp.status == InputStatus.IN_PROGRESS:
                    readiness["in_progress"] += 1
                else:
                    readiness["not_started"] += 1
            else:
                readiness["missing_topics"].append(topic)
                readiness["not_started"] += 1

        readiness["readiness_score"] = round(
            (readiness["complete"] / readiness["total_required"]) * 100, 1
        )

        return readiness

    def analyze_actions(self) -> Dict:
        """Analyze action item status."""
        analysis = {
            "total": len(self.review.actions),
            "by_status": {},
            "by_priority": {},
            "overdue": [],
            "due_soon": [],
            "completion_rate": 0.0
        }

        completed = 0
        for action in self.review.actions:
            # Count by status
            status = action.status.value
            analysis["by_status"][status] = analysis["by_status"].get(status, 0) + 1

            # Count by priority
            priority = action.priority.value
            analysis["by_priority"][priority] = analysis["by_priority"].get(priority, 0) + 1

            # Check completion
            if action.status in [ActionStatus.COMPLETE, ActionStatus.VERIFIED]:
                completed += 1

            # Check overdue
            if action.due_date:
                due = datetime.strptime(action.due_date, "%Y-%m-%d")
                if due < self.today and action.status not in [
                    ActionStatus.COMPLETE, ActionStatus.VERIFIED
                ]:
                    days_overdue = (self.today - due).days
                    analysis["overdue"].append({
                        "action_id": action.action_id,
                        "description": action.description[:50],
                        "owner": action.owner,
                        "days_overdue": days_overdue
                    })
                elif due <= self.today + timedelta(days=14) and action.status not in [
                    ActionStatus.COMPLETE, ActionStatus.VERIFIED
                ]:
                    days_until = (due - self.today).days
                    analysis["due_soon"].append({
                        "action_id": action.action_id,
                        "description": action.description[:50],
                        "owner": action.owner,
                        "days_until_due": days_until
                    })

        if analysis["total"] > 0:
            analysis["completion_rate"] = round((completed / analysis["total"]) * 100, 1)

        return analysis

    def assess_metrics(self) -> Dict:
        """Assess quality metrics against targets."""
        metrics = self.review.metrics
        assessment = {
            "metrics": [],
            "alerts": [],
            "overall_status": "On Track"
        }

        # Define targets and assess
        checks = [
            ("Complaint Rate", metrics.complaint_rate, 0.1, "lower"),
            ("CAPA Overdue", metrics.capa_overdue, 0, "lower"),
            ("CAPA Effectiveness", metrics.capa_effectiveness, 85.0, "higher"),
            ("First Pass Yield", metrics.first_pass_yield, 95.0, "higher"),
            ("Customer Satisfaction", metrics.customer_satisfaction, 4.0, "higher"),
            ("Training Compliance", metrics.training_compliance, 95.0, "higher"),
        ]

        warnings = 0
        critical = 0

        for name, value, target, direction in checks:
            if direction == "lower":
                status = "Pass" if value <= target else "Fail"
                threshold = target * 1.2
                warning = value > target and value <= threshold
            else:
                status = "Pass" if value >= target else "Fail"
                threshold = target * 0.9
                warning = value < target and value >= threshold

            metric_result = {
                "name": name,
                "value": value,
                "target": target,
                "status": status
            }
            assessment["metrics"].append(metric_result)

            if status == "Fail":
                if warning:
                    warnings += 1
                    assessment["alerts"].append(f"WARNING: {name} at {value} (target: {target})")
                else:
                    critical += 1
                    assessment["alerts"].append(f"CRITICAL: {name} at {value} (target: {target})")

        if critical > 0:
            assessment["overall_status"] = "Critical"
        elif warnings > 0:
            assessment["overall_status"] = "Needs Attention"

        return assessment

    def generate_recommendations(self) -> List[str]:
        """Generate recommendations based on analysis."""
        recommendations = []

        # Check input readiness
        readiness = self.check_input_readiness()
        if readiness["readiness_score"] < 100:
            recommendations.append(
                f"Complete remaining review inputs: {', '.join(readiness['missing_topics'])}"
            )

        # Check actions
        action_analysis = self.analyze_actions()
        if action_analysis["overdue"]:
            recommendations.append(
                f"Address {len(action_analysis['overdue'])} overdue action(s) immediately"
            )

        # Check metrics
        metrics_assessment = self.assess_metrics()
        if metrics_assessment["overall_status"] == "Critical":
            recommendations.append(
                "Escalate critical metric failures to senior management"
            )

        # CAPA specific
        if self.review.metrics.capa_overdue > 0:
            recommendations.append(
                f"Expedite closure of {self.review.metrics.capa_overdue} overdue CAPA(s)"
            )

        if self.review.metrics.capa_effectiveness < 85:
            recommendations.append(
                "Review root cause analysis quality for ineffective CAPAs"
            )

        # Audit findings
        if self.review.metrics.audit_findings_major > 0:
            recommendations.append(
                f"Prioritize resolution of {self.review.metrics.audit_findings_major} major audit finding(s)"
            )

        if not recommendations:
            recommendations.append("Quality system performing within targets. Maintain monitoring.")

        return recommendations

    def generate_report(self) -> Dict:
        """Generate complete review status report."""
        return {
            "review_date": self.review.review_date,
            "review_type": self.review.review_type,
            "period": f"{self.review.period_start} to {self.review.period_end}",
            "input_readiness": self.check_input_readiness(),
            "action_analysis": self.analyze_actions(),
            "metrics_assessment": self.assess_metrics(),
            "recommendations": self.generate_recommendations()
        }


def format_text_report(report: Dict) -> str:
    """Format report as text output."""
    lines = [
        "=" * 70,
        "MANAGEMENT REVIEW STATUS REPORT",
        "=" * 70,
        f"Review Date: {report['review_date']}",
        f"Review Type: {report['review_type']}",
        f"Period: {report['period']}",
        "",
        "INPUT READINESS",
        "-" * 40,
        f"Readiness Score: {report['input_readiness']['readiness_score']}%",
        f"Complete: {report['input_readiness']['complete']} / {report['input_readiness']['total_required']}",
    ]

    if report['input_readiness']['missing_topics']:
        lines.append(f"Missing: {', '.join(report['input_readiness']['missing_topics'])}")

    lines.extend([
        "",
        "ACTION STATUS",
        "-" * 40,
        f"Total Actions: {report['action_analysis']['total']}",
        f"Completion Rate: {report['action_analysis']['completion_rate']}%",
    ])

    for status, count in report['action_analysis']['by_status'].items():
        lines.append(f"  {status}: {count}")

    if report['action_analysis']['overdue']:
        lines.extend([
            "",
            "OVERDUE ACTIONS:",
        ])
        for item in report['action_analysis']['overdue']:
            lines.append(f"  [{item['action_id']}] {item['description']} - {item['days_overdue']} days overdue")

    lines.extend([
        "",
        "METRICS ASSESSMENT",
        "-" * 40,
        f"Overall Status: {report['metrics_assessment']['overall_status']}",
        "",
        f"{'Metric':<25} {'Value':<10} {'Target':<10} {'Status':<10}",
        "-" * 55,
    ])

    for metric in report['metrics_assessment']['metrics']:
        lines.append(
            f"{metric['name']:<25} {metric['value']:<10} {metric['target']:<10} {metric['status']:<10}"
        )

    if report['metrics_assessment']['alerts']:
        lines.extend([
            "",
            "ALERTS:",
        ])
        for alert in report['metrics_assessment']['alerts']:
            lines.append(f"  ! {alert}")

    lines.extend([
        "",
        "RECOMMENDATIONS",
        "-" * 40,
    ])

    for i, rec in enumerate(report['recommendations'], 1):
        lines.append(f"{i}. {rec}")

    lines.append("=" * 70)
    return "\n".join(lines)


def interactive_mode():
    """Run interactive review data entry."""
    print("=" * 60)
    print("Management Review Tracker - Interactive Mode")
    print("=" * 60)

    review_date = input("\nReview Date (YYYY-MM-DD): ").strip()
    review_type = input("Review Type (Annual/Semi-annual/Quarterly): ").strip()
    period_start = input("Period Start (YYYY-MM-DD): ").strip()
    period_end = input("Period End (YYYY-MM-DD): ").strip()

    print("\nEnter Quality Metrics:")
    metrics = ReviewMetrics(
        complaint_rate=float(input("Complaint Rate (%): ") or 0),
        complaint_count=int(input("Complaint Count: ") or 0),
        capa_open=int(input("Open CAPAs: ") or 0),
        capa_overdue=int(input("Overdue CAPAs: ") or 0),
        capa_effectiveness=float(input("CAPA Effectiveness (%): ") or 0),
        audit_findings_open=int(input("Open Audit Findings: ") or 0),
        audit_findings_major=int(input("Major Audit Findings: ") or 0),
        first_pass_yield=float(input("First Pass Yield (%): ") or 0),
        customer_satisfaction=float(input("Customer Satisfaction (1-5): ") or 0),
        training_compliance=float(input("Training Compliance (%): ") or 0)
    )

    # Create review with sample inputs
    inputs = [
        ReviewInput(topic=topic, responsible=resp, status=InputStatus.COMPLETE, data_period=f"{period_start} to {period_end}")
        for topic, resp in ManagementReviewTracker.REQUIRED_INPUTS
    ]

    review = ManagementReview(
        review_date=review_date,
        review_type=review_type,
        period_start=period_start,
        period_end=period_end,
        inputs=inputs,
        actions=[],
        metrics=metrics
    )

    tracker = ManagementReviewTracker(review)
    report = tracker.generate_report()
    print("\n" + format_text_report(report))


def main():
    parser = argparse.ArgumentParser(
        description="Management Review Tracker"
    )
    parser.add_argument(
        "--data",
        type=str,
        help="JSON file with review data"
    )
    parser.add_argument(
        "--output",
        choices=["text", "json"],
        default="text",
        help="Output format"
    )
    parser.add_argument(
        "--interactive",
        action="store_true",
        help="Run in interactive mode"
    )
    parser.add_argument(
        "--sample",
        action="store_true",
        help="Generate sample review data"
    )

    args = parser.parse_args()

    if args.interactive:
        interactive_mode()
        return

    if args.sample:
        sample = {
            "review_date": "2024-06-30",
            "review_type": "Semi-annual",
            "period_start": "2024-01-01",
            "period_end": "2024-06-30",
            "inputs": [
                {"topic": "Audit Results", "responsible": "QA Manager", "status": "Complete", "data_period": "H1 2024"},
                {"topic": "Customer Feedback", "responsible": "Customer Quality", "status": "Complete", "data_period": "H1 2024"},
                {"topic": "Process Performance", "responsible": "Operations", "status": "In Progress", "data_period": "H1 2024"},
                {"topic": "CAPA Status", "responsible": "CAPA Officer", "status": "Complete", "data_period": "Current"}
            ],
            "actions": [
                {
                    "action_id": "MR-2024-001",
                    "description": "Implement enhanced CAPA tracking system",
                    "owner": "QA Manager",
                    "due_date": "2024-09-30",
                    "priority": "High",
                    "status": "In Progress",
                    "source_review": "2024-Q1"
                }
            ],
            "metrics": {
                "complaint_rate": 0.08,
                "complaint_count": 12,
                "capa_open": 8,
                "capa_overdue": 2,
                "capa_effectiveness": 88.0,
                "audit_findings_open": 5,
                "audit_findings_major": 1,
                "first_pass_yield": 96.5,
                "customer_satisfaction": 4.2,
                "training_compliance": 97.0
            }
        }
        print(json.dumps(sample, indent=2))
        return

    # Create sample review if no data provided
    if args.data:
        with open(args.data, "r") as f:
            data = json.load(f)

        inputs = [
            ReviewInput(
                topic=inp["topic"],
                responsible=inp["responsible"],
                status=InputStatus[inp["status"].upper().replace(" ", "_")],
                data_period=inp.get("data_period", "")
            )
            for inp in data.get("inputs", [])
        ]

        actions = [
            ActionItem(
                action_id=act["action_id"],
                description=act["description"],
                owner=act["owner"],
                due_date=act["due_date"],
                priority=ActionPriority[act["priority"].upper()],
                status=ActionStatus[act["status"].upper().replace(" ", "_")],
                source_review=act.get("source_review", "")
            )
            for act in data.get("actions", [])
        ]

        metrics_data = data.get("metrics", {})
        metrics = ReviewMetrics(**metrics_data)

        review = ManagementReview(
            review_date=data["review_date"],
            review_type=data["review_type"],
            period_start=data["period_start"],
            period_end=data["period_end"],
            inputs=inputs,
            actions=actions,
            metrics=metrics
        )
    else:
        # Demo data
        review = ManagementReview(
            review_date="2024-06-30",
            review_type="Semi-annual",
            period_start="2024-01-01",
            period_end="2024-06-30",
            inputs=[
                ReviewInput("Audit Results", "QA Manager", InputStatus.COMPLETE, "H1 2024"),
                ReviewInput("Customer Feedback", "Customer Quality", InputStatus.COMPLETE, "H1 2024"),
                ReviewInput("CAPA Status", "CAPA Officer", InputStatus.COMPLETE, "Current"),
            ],
            actions=[
                ActionItem("MR-2024-001", "Implement CAPA tracking", "QA Mgr", "2024-09-30",
                          ActionPriority.HIGH, ActionStatus.IN_PROGRESS, "2024-Q1"),
            ],
            metrics=ReviewMetrics(
                complaint_rate=0.08, capa_open=8, capa_overdue=2,
                capa_effectiveness=88.0, first_pass_yield=96.5,
                customer_satisfaction=4.2, training_compliance=97.0
            )
        )

    tracker = ManagementReviewTracker(review)
    report = tracker.generate_report()

    if args.output == "json":
        print(json.dumps(report, indent=2))
    else:
        print(format_text_report(report))


if __name__ == "__main__":
    main()

#!/usr/bin/env python3
"""
Quality Management System Effectiveness Monitor

Quantitatively assess QMS effectiveness using leading and lagging indicators.
Tracks trends, calculates control limits, and predicts potential quality issues
before they become failures. Integrates with CAPA and management review processes.

Supports metrics:
- Complaint rates, defect rates, rework rates
- Supplier performance
- CAPA effectiveness
- Audit findings trends
- Non-conformance statistics

Usage:
    python quality_effectiveness_monitor.py --metrics metrics.csv --dashboard
    python quality_effectiveness_monitor.py --qms-data qms_data.json --predict
    python quality_effectiveness_monitor.py --interactive
"""

import argparse
import json
import csv
import sys
from dataclasses import dataclass, field, asdict
from typing import List, Dict, Optional, Tuple
from datetime import datetime, timedelta
from statistics import mean, stdev, median



@dataclass
class QualityMetric:
    """A single quality metric data point."""
    metric_id: str
    metric_name: str
    category: str
    date: str
    value: float
    unit: str
    target: float
    upper_limit: float
    lower_limit: float
    trend_direction: str = ""  # "up", "down", "stable"
    sigma_level: float = 0.0
    is_alert: bool = False
    is_critical: bool = False


@dataclass
class QMSReport:
    """QMS effectiveness report."""
    report_period: Tuple[str, str]
    overall_effectiveness_score: float
    metrics_count: int
    metrics_in_control: int
    metrics_out_of_control: int
    critical_alerts: int
    trends_analysis: Dict
    predictive_alerts: List[Dict]
    improvement_opportunities: List[Dict]
    management_review_summary: str


class QMSEffectivenessMonitor:
    """Monitors and analyzes QMS effectiveness."""

    SIGNAL_INDICATORS = {
        "complaint_rate": {"unit": "per 1000 units", "target": 0, "upper_limit": 1.5},
        "defect_rate": {"unit": "PPM", "target": 100, "upper_limit": 500},
        "rework_rate": {"unit": "%", "target": 2.0, "upper_limit": 5.0},
        "on_time_delivery": {"unit": "%", "target": 98, "lower_limit": 95},
        "audit_findings": {"unit": "count/month", "target": 0, "upper_limit": 3},
        "capa_closure_rate": {"unit": "% within target", "target": 100, "lower_limit": 90},
        "supplier_defect_rate": {"unit": "PPM", "target": 200, "upper_limit": 1000}
    }

    def __init__(self):
        self.metrics = []

    def load_csv(self, csv_path: str) -> List[QualityMetric]:
        """Load metrics from CSV file."""
        metrics = []
        with open(csv_path, 'r', encoding='utf-8') as f:
            reader = csv.DictReader(f)
            for row in reader:
                metric = QualityMetric(
                    metric_id=row.get('metric_id', ''),
                    metric_name=row.get('metric_name', ''),
                    category=row.get('category', 'General'),
                    date=row.get('date', ''),
                    value=float(row.get('value', 0)),
                    unit=row.get('unit', ''),
                    target=float(row.get('target', 0)),
                    upper_limit=float(row.get('upper_limit', 0)),
                    lower_limit=float(row.get('lower_limit', 0)),
                )
                metrics.append(metric)
        self.metrics = metrics
        return metrics

    def calculate_sigma_level(self, metric: QualityMetric, historical_values: List[float]) -> float:
        """Calculate process sigma level based on defect rate."""
        if metric.unit == "PPM" or "rate" in metric.metric_name.lower():
            # For defect rates, DPMO = defects_per_million_opportunities
            if historical_values:
                avg_defect_rate = mean(historical_values)
                if avg_defect_rate > 0:
                    dpmo = avg_defect_rate
                    # Simplified sigma conversion (actual uses 1.5σ shift)
                    sigma_map = {
                        330000: 1.0, 620000: 2.0, 110000: 3.0, 27000: 4.0,
                        6200: 5.0, 230: 6.0, 3.4: 6.0
                    }
                    # Rough sigma calculation
                    sigma = 6.0 - (dpmo / 1000000) * 10
                    return max(0.0, min(6.0, sigma))
        return 0.0

    def analyze_trend(self, values: List[float]) -> Tuple[str, float]:
        """Analyze trend direction and significance."""
        if len(values) < 3:
            return "insufficient_data", 0.0

        x = list(range(len(values)))
        y = values

        # Linear regression
        n = len(x)
        sum_x = sum(x)
        sum_y = sum(y)
        sum_xy = sum(x[i] * y[i] for i in range(n))
        sum_x2 = sum(xi * xi for xi in x)

        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

        # Determine trend direction
        if slope > 0.01:
            direction = "up"
        elif slope < -0.01:
            direction = "down"
        else:
            direction = "stable"

        # Calculate R-squared
        if slope != 0:
            intercept = (sum_y - slope * sum_x) / n
            y_pred = [slope * xi + intercept for xi in x]
            ss_res = sum((y[i] - y_pred[i])**2 for i in range(n))
            ss_tot = sum((y[i] - mean(y))**2 for i in range(n))
            r2 = 1 - (ss_res / ss_tot) if ss_tot > 0 else 0
        else:
            r2 = 0

        return direction, r2

    def detect_alerts(self, metrics: List[QualityMetric]) -> List[Dict]:
        """Detect metrics that require attention."""
        alerts = []
        for metric in metrics:
            # Check immediate control limit violation
            if metric.upper_limit and metric.value > metric.upper_limit:
                alerts.append({
                    "metric_id": metric.metric_id,
                    "metric_name": metric.metric_name,
                    "issue": "exceeds_upper_limit",
                    "value": metric.value,
                    "limit": metric.upper_limit,
                    "severity": "critical" if metric.category in ["Customer", "Regulatory"] else "high"
                })
            if metric.lower_limit and metric.value < metric.lower_limit:
                alerts.append({
                    "metric_id": metric.metric_id,
                    "metric_name": metric.metric_name,
                    "issue": "below_lower_limit",
                    "value": metric.value,
                    "limit": metric.lower_limit,
                    "severity": "critical" if metric.category in ["Customer", "Regulatory"] else "high"
                })

            # Check for adverse trend (3+ points in same direction)
            # Need to group by metric_name and check historical data
            # Simplified: check trend_direction flag if set
            if metric.trend_direction in ["up", "down"] and metric.sigma_level > 3:
                alerts.append({
                    "metric_id": metric.metric_id,
                    "metric_name": metric.metric_name,
                    "issue": f"adverse_trend_{metric.trend_direction}",
                    "value": metric.value,
                    "severity": "medium"
                })

        return alerts

    def predict_failures(self, metrics: List[QualityMetric], forecast_days: int = 30) -> List[Dict]:
        """Predict potential failures based on trends."""
        predictions = []

        # Group metrics by name to get time series
        grouped = {}
        for m in metrics:
            if m.metric_name not in grouped:
                grouped[m.metric_name] = []
            grouped[m.metric_name].append(m)

        for metric_name, metric_list in grouped.items():
            if len(metric_list) < 5:
                continue

            # Sort by date
            metric_list.sort(key=lambda m: m.date)
            values = [m.value for m in metric_list]

            # Simple linear extrapolation
            x = list(range(len(values)))
            y = values
            n = len(x)
            sum_x = sum(x)
            sum_y = sum(y)
            sum_xy = sum(x[i] * y[i] for i in range(n))
            sum_x2 = sum(xi * xi for xi in x)
            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

            if slope != 0:
                # Forecast next value
                next_value = y[-1] + slope
                target = metric_list[0].target
                upper_limit = metric_list[0].upper_limit

                if (target and next_value > target * 1.2) or (upper_limit and next_value > upper_limit * 0.9):
                    predictions.append({
                        "metric": metric_name,
                        "current_value": y[-1],
                        "forecast_value": round(next_value, 2),
                        "forecast_days": forecast_days,
                        "trend_slope": round(slope, 3),
                        "risk_level": "high" if upper_limit and next_value > upper_limit else "medium"
                    })

        return predictions

    def calculate_effectiveness_score(self, metrics: List[QualityMetric]) -> float:
        """Calculate overall QMS effectiveness score (0-100)."""
        if not metrics:
            return 0.0

        scores = []
        for m in metrics:
            # Score based on distance to target
            if m.target != 0:
                deviation = abs(m.value - m.target) / max(abs(m.target), 1)
                score = max(0, 100 - deviation * 100)
            else:
                # For metrics where lower is better (defects, etc.)
                if m.upper_limit:
                    score = max(0, 100 - (m.value / m.upper_limit) * 100 * 0.5)
                else:
                    score = 50  # Neutral if no target
            scores.append(score)

        # Penalize for alerts
        alerts = self.detect_alerts(metrics)
        penalty = len([a for a in alerts if a["severity"] in ["critical", "high"]]) * 5
        return max(0, min(100, mean(scores) - penalty))

    def identify_improvement_opportunities(self, metrics: List[QualityMetric]) -> List[Dict]:
        """Identify metrics with highest improvement potential."""
        opportunities = []
        for m in metrics:
            if m.upper_limit and m.value > m.upper_limit * 0.8:
                gap = m.upper_limit - m.value
                if gap > 0:
                    improvement_pct = (gap / m.upper_limit) * 100
                    opportunities.append({
                        "metric": m.metric_name,
                        "current": m.value,
                        "target": m.upper_limit,
                        "gap": round(gap, 2),
                        "improvement_potential_pct": round(improvement_pct, 1),
                        "recommended_action": f"Reduce {m.metric_name} by at least {round(gap, 2)} {m.unit}",
                        "impact": "High" if m.category in ["Customer", "Regulatory"] else "Medium"
                    })

        # Sort by improvement potential
        opportunities.sort(key=lambda x: x["improvement_potential_pct"], reverse=True)
        return opportunities[:10]

    def generate_management_review_summary(self, report: QMSReport) -> str:
        """Generate executive summary for management review."""
        summary = [
            f"QMS EFFECTIVENESS REVIEW - {report.report_period[0]} to {report.report_period[1]}",
            "",
            f"Overall Effectiveness Score: {report.overall_effectiveness_score:.1f}/100",
            f"Metrics Tracked: {report.metrics_count} | In Control: {report.metrics_in_control} | Alerts: {report.critical_alerts}",
            ""
        ]

        if report.critical_alerts > 0:
            summary.append("🔴 CRITICAL ALERTS REQUIRING IMMEDIATE ATTENTION:")
            for alert in [a for a in report.predictive_alerts if a.get("risk_level") == "high"]:
                summary.append(f"  • {alert['metric']}: forecast {alert['forecast_value']} (from {alert['current_value']})")
            summary.append("")

        summary.append("📈 TOP IMPROVEMENT OPPORTUNITIES:")
        for i, opp in enumerate(report.improvement_opportunities[:3], 1):
            summary.append(f"  {i}. {opp['metric']}: {opp['recommended_action']} (Impact: {opp['impact']})")
        summary.append("")

        summary.append("🎯 RECOMMENDED ACTIONS:")
        summary.append("  1. Address all high-severity alerts within 30 days")
        summary.append("  2. Launch improvement projects for top 3 opportunities")
        summary.append("  3. Review CAPA effectiveness for recurring issues")
        summary.append("  4. Update risk assessments based on predictive trends")

        return "\n".join(summary)

    def analyze(
        self,
        metrics: List[QualityMetric],
        start_date: str = None,
        end_date: str = None
    ) -> QMSReport:
        """Perform comprehensive QMS effectiveness analysis."""
        in_control = 0
        for m in metrics:
            if not m.is_alert and not m.is_critical:
                in_control += 1

        out_of_control = len(metrics) - in_control

        alerts = self.detect_alerts(metrics)
        critical_alerts = len([a for a in alerts if a["severity"] in ["critical", "high"]])

        predictions = self.predict_failures(metrics)
        improvement_opps = self.identify_improvement_opportunities(metrics)

        effectiveness = self.calculate_effectiveness_score(metrics)

        # Trend analysis by category
        trends = {}
        categories = set(m.category for m in metrics)
        for cat in categories:
            cat_metrics = [m for m in metrics if m.category == cat]
            if len(cat_metrics) >= 2:
                avg_values = [mean([m.value for m in cat_metrics])]  # Simplistic - would need time series
                trends[cat] = {
                    "metric_count": len(cat_metrics),
                    "avg_value": round(mean([m.value for m in cat_metrics]), 2),
                    "alerts": len([a for a in alerts if any(m.metric_name == a["metric_name"] for m in cat_metrics)])
                }

        period = (start_date or metrics[0].date, end_date or metrics[-1].date) if metrics else ("", "")

        report = QMSReport(
            report_period=period,
            overall_effectiveness_score=effectiveness,
            metrics_count=len(metrics),
            metrics_in_control=in_control,
            metrics_out_of_control=out_of_control,
            critical_alerts=critical_alerts,
            trends_analysis=trends,
            predictive_alerts=predictions,
            improvement_opportunities=improvement_opps,
            management_review_summary=""  # Filled later
        )

        report.management_review_summary = self.generate_management_review_summary(report)

        return report


def format_qms_report(report: QMSReport) -> str:
    """Format QMS report as text."""
    lines = [
        "=" * 80,
        "QMS EFFECTIVENESS MONITORING REPORT",
        "=" * 80,
        f"Period: {report.report_period[0]} to {report.report_period[1]}",
        f"Overall Score: {report.overall_effectiveness_score:.1f}/100",
        "",
        "METRIC STATUS",
        "-" * 40,
        f"  Total Metrics: {report.metrics_count}",
        f"  In Control: {report.metrics_in_control}",
        f"  Out of Control: {report.metrics_out_of_control}",
        f"  Critical Alerts: {report.critical_alerts}",
        "",
        "TREND ANALYSIS BY CATEGORY",
        "-" * 40,
    ]

    for category, data in report.trends_analysis.items():
        lines.append(f"  {category}: {data['avg_value']} (alerts: {data['alerts']})")

    if report.predictive_alerts:
        lines.extend([
            "",
            "PREDICTIVE ALERTS (Next 30 days)",
            "-" * 40,
        ])
        for alert in report.predictive_alerts[:5]:
            lines.append(f"  ⚠ {alert['metric']}: {alert['current_value']} → {alert['forecast_value']} ({alert['risk_level']})")

    if report.improvement_opportunities:
        lines.extend([
            "",
            "TOP IMPROVEMENT OPPORTUNITIES",
            "-" * 40,
        ])
        for i, opp in enumerate(report.improvement_opportunities[:5], 1):
            lines.append(f"  {i}. {opp['metric']}: {opp['recommended_action']}")

    lines.extend([
        "",
        "MANAGEMENT REVIEW SUMMARY",
        "-" * 40,
        report.management_review_summary,
        "=" * 80
    ])

    return "\n".join(lines)


def main():
    parser = argparse.ArgumentParser(description="QMS Effectiveness Monitor")
    parser.add_argument("--metrics", type=str, help="CSV file with quality metrics")
    parser.add_argument("--qms-data", type=str, help="JSON file with QMS data")
    parser.add_argument("--dashboard", action="store_true", help="Generate dashboard summary")
    parser.add_argument("--predict", action="store_true", help="Include predictive analytics")
    parser.add_argument("--output", choices=["text", "json"], default="text")
    parser.add_argument("--interactive", action="store_true", help="Interactive mode")

    args = parser.parse_args()
    monitor = QMSEffectivenessMonitor()

    if args.metrics:
        metrics = monitor.load_csv(args.metrics)
        report = monitor.analyze(metrics)
    elif args.qms_data:
        with open(args.qms_data) as f:
            data = json.load(f)
        # Convert to QualityMetric objects
        metrics = [QualityMetric(**m) for m in data.get("metrics", [])]
        report = monitor.analyze(metrics)
    else:
        # Demo data
        demo_metrics = [
            QualityMetric("M001", "Customer Complaint Rate", "Customer", "2026-03-01", 0.8, "per 1000", 1.0, 1.5, 0.5),
            QualityMetric("M002", "Defect Rate PPM", "Quality", "2026-03-01", 125, "PPM", 100, 500, 0, trend_direction="down", sigma_level=4.2),
            QualityMetric("M003", "On-Time Delivery", "Operations", "2026-03-01", 96.5, "%", 98, 0, 95, trend_direction="down"),
            QualityMetric("M004", "CAPA Closure Rate", "Quality", "2026-03-01", 92.0, "%", 100, 0, 90, is_alert=True),
            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()