project-kpi-dashboard

name	project-kpi-dashboard
description	Create interactive KPI dashboards for construction projects. Track schedule, cost, quality, and safety metrics in real-time.
homepage	https://datadrivenconstruction.io
metadata	{"openclaw":{"emoji":"📊","os":["darwin","linux","win32"],"homepage":"https://datadrivenconstruction.io","requires":{"bins":["python3"]}}}

Project KPI Dashboard

Business Case

Problem Statement

Project stakeholders struggle with:

• Scattered data across multiple systems
• Delayed reporting on project health
• No real-time visibility into KPIs
• Inconsistent metric definitions

Solution

Centralized KPI dashboard that aggregates data from multiple sources and presents key metrics with drill-down capabilities.

Business Value

• Real-time visibility - Live project health status
• Data-driven decisions - Actionable insights
• Stakeholder alignment - Single source of truth
• Early warning - Proactive issue detection

Technical Implementation

import pandas as pd
from datetime import datetime, date, timedelta
from typing import Dict, Any, List, Optional
from dataclasses import dataclass, field
from enum import Enum


class KPIStatus(Enum):
    """KPI health status."""
    ON_TRACK = "on_track"
    AT_RISK = "at_risk"
    CRITICAL = "critical"
    UNKNOWN = "unknown"


class KPICategory(Enum):
    """KPI categories."""
    SCHEDULE = "schedule"
    COST = "cost"
    QUALITY = "quality"
    SAFETY = "safety"
    PRODUCTIVITY = "productivity"
    SUSTAINABILITY = "sustainability"


@dataclass
class KPIMetric:
    """Single KPI metric."""
    name: str
    category: KPICategory
    current_value: float
    target_value: float
    unit: str
    status: KPIStatus
    trend: str  # up, down, stable
    last_updated: datetime
    description: str = ""

    @property
    def variance(self) -> float:
        """Calculate variance from target."""
        if self.target_value == 0:
            return 0
        return ((self.current_value - self.target_value) / self.target_value) * 100

    @property
    def achievement(self) -> float:
        """Calculate achievement percentage."""
        if self.target_value == 0:
            return 0
        return (self.current_value / self.target_value) * 100


@dataclass
class DashboardConfig:
    """Dashboard configuration."""
    project_name: str
    project_code: str
    start_date: date
    end_date: date
    budget: float
    currency: str = "USD"
    refresh_interval_minutes: int = 15


class ProjectKPIDashboard:
    """Construction project KPI dashboard."""

    # Standard thresholds for RAG status
    THRESHOLDS = {
        'schedule': {'green': 0.95, 'amber': 0.85},
        'cost': {'green': 1.05, 'amber': 1.15},
        'quality': {'green': 0.98, 'amber': 0.95},
        'safety': {'green': 0, 'amber': 1}  # incident count
    }

    def __init__(self, config: DashboardConfig):
        self.config = config
        self.metrics: Dict[str, KPIMetric] = {}
        self.history: List[Dict[str, Any]] = []

    def add_metric(self, metric: KPIMetric):
        """Add or update a KPI metric."""
        self.metrics[metric.name] = metric
        self._record_history(metric)

    def _record_history(self, metric: KPIMetric):
        """Record metric history for trending."""
        self.history.append({
            'name': metric.name,
            'value': metric.current_value,
            'timestamp': metric.last_updated,
            'status': metric.status.value
        })

    def calculate_schedule_kpis(self,
                                 planned_activities: int,
                                 completed_activities: int,
                                 planned_duration_days: int,
                                 actual_duration_days: int) -> List[KPIMetric]:
        """Calculate schedule-related KPIs."""

        # Schedule Performance Index (SPI)
        spi = completed_activities / planned_activities if planned_activities > 0 else 0
        spi_status = self._get_status(spi, 'schedule')

        # Schedule Variance
        sv = completed_activities - planned_activities

        # Percent Complete
        pct_complete = (completed_activities / planned_activities * 100) if planned_activities > 0 else 0

        metrics = [
            KPIMetric(
                name="Schedule Performance Index",
                category=KPICategory.SCHEDULE,
                current_value=round(spi, 2),
                target_value=1.0,
                unit="ratio",
                status=spi_status,
                trend=self._calculate_trend("Schedule Performance Index"),
                last_updated=datetime.now(),
                description="SPI = Earned Value / Planned Value"
            ),
            KPIMetric(
                name="Percent Complete",
                category=KPICategory.SCHEDULE,
                current_value=round(pct_complete, 1),
                target_value=100,
                unit="%",
                status=spi_status,
                trend=self._calculate_trend("Percent Complete"),
                last_updated=datetime.now()
            ),
            KPIMetric(
                name="Schedule Variance",
                category=KPICategory.SCHEDULE,
                current_value=sv,
                target_value=0,
                unit="activities",
                status=spi_status,
                trend=self._calculate_trend("Schedule Variance"),
                last_updated=datetime.now()
            )
        ]

        for m in metrics:
            self.add_metric(m)

        return metrics

    def calculate_cost_kpis(self,
                            budgeted_cost: float,
                            actual_cost: float,
                            earned_value: float) -> List[KPIMetric]:
        """Calculate cost-related KPIs."""

        # Cost Performance Index (CPI)
        cpi = earned_value / actual_cost if actual_cost > 0 else 0
        cpi_status = self._get_status(cpi, 'cost', inverse=True)

        # Cost Variance
        cv = earned_value - actual_cost

        # Budget utilization
        budget_used = (actual_cost / budgeted_cost * 100) if budgeted_cost > 0 else 0

        metrics = [
            KPIMetric(
                name="Cost Performance Index",
                category=KPICategory.COST,
                current_value=round(cpi, 2),
                target_value=1.0,
                unit="ratio",
                status=cpi_status,
                trend=self._calculate_trend("Cost Performance Index"),
                last_updated=datetime.now(),
                description="CPI = Earned Value / Actual Cost"
            ),
            KPIMetric(
                name="Cost Variance",
                category=KPICategory.COST,
                current_value=round(cv, 2),
                target_value=0,
                unit=self.config.currency,
                status=cpi_status,
                trend=self._calculate_trend("Cost Variance"),
                last_updated=datetime.now()
            ),
            KPIMetric(
                name="Budget Utilization",
                category=KPICategory.COST,
                current_value=round(budget_used, 1),
                target_value=100,
                unit="%",
                status=cpi_status,
                trend=self._calculate_trend("Budget Utilization"),
                last_updated=datetime.now()
            )
        ]

        for m in metrics:
            self.add_metric(m)

        return metrics

    def calculate_quality_kpis(self,
                               total_inspections: int,
                               passed_inspections: int,
                               rework_items: int,
                               total_items: int) -> List[KPIMetric]:
        """Calculate quality-related KPIs."""

        # First Pass Yield
        fpy = passed_inspections / total_inspections if total_inspections > 0 else 0
        fpy_status = self._get_status(fpy, 'quality')

        # Rework Rate
        rework_rate = rework_items / total_items * 100 if total_items > 0 else 0

        metrics = [
            KPIMetric(
                name="First Pass Yield",
                category=KPICategory.QUALITY,
                current_value=round(fpy * 100, 1),
                target_value=98,
                unit="%",
                status=fpy_status,
                trend=self._calculate_trend("First Pass Yield"),
                last_updated=datetime.now()
            ),
            KPIMetric(
                name="Rework Rate",
                category=KPICategory.QUALITY,
                current_value=round(rework_rate, 1),
                target_value=2,
                unit="%",
                status=fpy_status,
                trend=self._calculate_trend("Rework Rate"),
                last_updated=datetime.now()
            )
        ]

        for m in metrics:
            self.add_metric(m)

        return metrics

    def calculate_safety_kpis(self,
                              incidents: int,
                              near_misses: int,
                              worked_hours: float,
                              safety_observations: int) -> List[KPIMetric]:
        """Calculate safety-related KPIs."""

        # TRIR (Total Recordable Incident Rate)
        trir = (incidents * 200000) / worked_hours if worked_hours > 0 else 0
        trir_status = KPIStatus.ON_TRACK if incidents == 0 else (
            KPIStatus.AT_RISK if incidents <= 2 else KPIStatus.CRITICAL
        )

        # LTIR (Lost Time Incident Rate)
        ltir = (incidents * 1000000) / worked_hours if worked_hours > 0 else 0

        metrics = [
            KPIMetric(
                name="TRIR",
                category=KPICategory.SAFETY,
                current_value=round(trir, 2),
                target_value=0,
                unit="per 200k hrs",
                status=trir_status,
                trend=self._calculate_trend("TRIR"),
                last_updated=datetime.now(),
                description="Total Recordable Incident Rate"
            ),
            KPIMetric(
                name="Safety Observations",
                category=KPICategory.SAFETY,
                current_value=safety_observations,
                target_value=50,
                unit="count",
                status=KPIStatus.ON_TRACK if safety_observations >= 50 else KPIStatus.AT_RISK,
                trend=self._calculate_trend("Safety Observations"),
                last_updated=datetime.now()
            ),
            KPIMetric(
                name="Near Miss Reports",
                category=KPICategory.SAFETY,
                current_value=near_misses,
                target_value=10,
                unit="count",
                status=KPIStatus.ON_TRACK,
                trend=self._calculate_trend("Near Miss Reports"),
                last_updated=datetime.now()
            )
        ]

        for m in metrics:
            self.add_metric(m)

        return metrics

    def _get_status(self, value: float, category: str, inverse: bool = False) -> KPIStatus:
        """Determine RAG status based on thresholds."""
        thresholds = self.THRESHOLDS.get(category, {'green': 0.95, 'amber': 0.85})

        if inverse:
            if value >= thresholds['green']:
                return KPIStatus.ON_TRACK
            elif value >= thresholds['amber']:
                return KPIStatus.AT_RISK
            else:
                return KPIStatus.CRITICAL
        else:
            if value >= thresholds['green']:
                return KPIStatus.ON_TRACK
            elif value >= thresholds['amber']:
                return KPIStatus.AT_RISK
            else:
                return KPIStatus.CRITICAL

    def _calculate_trend(self, metric_name: str) -> str:
        """Calculate trend based on historical data."""
        history = [h for h in self.history if h['name'] == metric_name]
        if len(history) < 2:
            return "stable"

        recent = history[-1]['value']
        previous = history[-2]['value']

        if recent > previous * 1.02:
            return "up"
        elif recent < previous * 0.98:
            return "down"
        return "stable"

    def get_dashboard_summary(self) -> Dict[str, Any]:
        """Generate dashboard summary."""
        by_category = {}
        for metric in self.metrics.values():
            cat = metric.category.value
            if cat not in by_category:
                by_category[cat] = []
            by_category[cat].append({
                'name': metric.name,
                'value': metric.current_value,
                'target': metric.target_value,
                'unit': metric.unit,
                'status': metric.status.value,
                'trend': metric.trend,
                'variance': round(metric.variance, 1)
            })

        # Overall health
        statuses = [m.status for m in self.metrics.values()]
        critical_count = sum(1 for s in statuses if s == KPIStatus.CRITICAL)
        at_risk_count = sum(1 for s in statuses if s == KPIStatus.AT_RISK)

        if critical_count > 0:
            overall = "CRITICAL"
        elif at_risk_count > 2:
            overall = "AT_RISK"
        else:
            overall = "ON_TRACK"

        return {
            'project': self.config.project_name,
            'project_code': self.config.project_code,
            'generated_at': datetime.now().isoformat(),
            'overall_health': overall,
            'metrics_count': len(self.metrics),
            'critical_count': critical_count,
            'at_risk_count': at_risk_count,
            'kpis_by_category': by_category
        }

    def export_to_dataframe(self) -> pd.DataFrame:
        """Export all KPIs to DataFrame."""
        data = []
        for metric in self.metrics.values():
            data.append({
                'KPI': metric.name,
                'Category': metric.category.value,
                'Current': metric.current_value,
                'Target': metric.target_value,
                'Unit': metric.unit,
                'Variance %': round(metric.variance, 1),
                'Status': metric.status.value,
                'Trend': metric.trend,
                'Last Updated': metric.last_updated
            })
        return pd.DataFrame(data)

Quick Start

from datetime import date

# Configure dashboard
config = DashboardConfig(
    project_name="Office Tower Construction",
    project_code="PRJ-2024-001",
    start_date=date(2024, 1, 1),
    end_date=date(2025, 12, 31),
    budget=50000000,
    currency="USD"
)

# Initialize dashboard
dashboard = ProjectKPIDashboard(config)

# Calculate schedule KPIs
dashboard.calculate_schedule_kpis(
    planned_activities=100,
    completed_activities=85,
    planned_duration_days=180,
    actual_duration_days=195
)

# Calculate cost KPIs
dashboard.calculate_cost_kpis(
    budgeted_cost=25000000,
    actual_cost=24500000,
    earned_value=24000000
)

# Get summary
summary = dashboard.get_dashboard_summary()
print(f"Overall Health: {summary['overall_health']}")

Common Use Cases

1. Weekly Executive Report

df = dashboard.export_to_dataframe()
critical = df[df['Status'] == 'critical']
print(f"Critical KPIs requiring attention: {len(critical)}")

2. Trend Analysis

# Get historical data for a metric
spi_history = [h for h in dashboard.history if h['name'] == 'Schedule Performance Index']

3. Multi-Project Dashboard

projects = []
for project_config in project_configs:
    dash = ProjectKPIDashboard(project_config)
    # ... calculate KPIs
    projects.append(dash.get_dashboard_summary())

Resources

• DDC Book: Chapter 4.1 - Construction Analytics
• Reference: PMI Earned Value Management