Portfolio Dashboard
Overview
Aggregate and analyze data across multiple construction projects for portfolio-level visibility. Track KPIs, identify trends, compare project performance, and support strategic resource allocation decisions.
Portfolio Analytics Framework
┌─────────────────────────────────────────────────────────────────┐
│ PORTFOLIO DASHBOARD │
├─────────────────────────────────────────────────────────────────┤
│ │
│ PROJECT A PROJECT B PROJECT C PROJECT D │
│ ↓ ↓ ↓ ↓ │
│ ┌─────────────────────────────────────────────┐ │
│ │ DATA AGGREGATION │ │
│ │ Cost | Schedule | Safety | Quality | Risk │ │
│ └─────────────────────────────────────────────┘ │
│ ↓ │
│ ┌─────────────────────────────────────────────┐ │
│ │ PORTFOLIO KPIs │ │
│ │ 📊 Total Value 📈 On-Schedule % │ │
│ │ 💰 On-Budget % 🛡️ Safety Rate │ │
│ │ ⚠️ Risk Score 📋 Resource Util │ │
│ └─────────────────────────────────────────────┘ │
│ │
└─────────────────────────────────────────────────────────────────┘Technical Implementation
from dataclasses import dataclass, field
from typing import List, Dict, Optional, Tuple
from datetime import datetime, timedelta
from enum import Enum
import statistics
class ProjectStatus(Enum):
PLANNING = "planning"
ACTIVE = "active"
ON_HOLD = "on_hold"
COMPLETE = "complete"
CANCELLED = "cancelled"
class HealthStatus(Enum):
GREEN = "green" # On track
YELLOW = "yellow" # At risk
RED = "red" # Critical
GREY = "grey" # Not started/on hold
@dataclass
class ProjectMetrics:
project_id: str
project_name: str
status: ProjectStatus
contract_value: float
percent_complete: float
# Schedule
planned_start: datetime
planned_end: datetime
actual_start: Optional[datetime]
forecast_end: datetime
schedule_variance_days: int = 0
# Cost
budget: float
actual_cost: float
forecast_cost: float
cost_variance: float = 0.0
cpi: float = 1.0
spi: float = 1.0
# Safety
recordable_incidents: int = 0
total_hours: float = 0
trir: float = 0.0
# Quality
defects_open: int = 0
rework_cost: float = 0.0
# Risk
risk_score: float = 0.0
critical_risks: int = 0
@property
def health(self) -> HealthStatus:
"""Determine overall project health."""
if self.status in [ProjectStatus.ON_HOLD, ProjectStatus.CANCELLED]:
return HealthStatus.GREY
# Critical if significantly over budget/schedule
if self.cpi < 0.85 or self.spi < 0.85 or self.critical_risks > 3:
return HealthStatus.RED
# At risk if moderately off track
if self.cpi < 0.95 or self.spi < 0.95 or self.critical_risks > 0:
return HealthStatus.YELLOW
return HealthStatus.GREEN
@dataclass
class PortfolioSummary:
report_date: datetime
total_projects: int
active_projects: int
total_contract_value: float
total_budget: float
total_actual_cost: float
total_forecast_cost: float
# Performance
avg_cpi: float
avg_spi: float
on_budget_pct: float
on_schedule_pct: float
# Safety
portfolio_trir: float
total_incidents: int
# Health distribution
green_count: int
yellow_count: int
red_count: int
# Trends
cost_trend: str
schedule_trend: str
@dataclass
class ProjectComparison:
metric: str
projects: Dict[str, float]
avg: float
best: Tuple[str, float]
worst: Tuple[str, float]
class PortfolioDashboard:
"""Multi-project portfolio analytics."""
# Health thresholds
THRESHOLDS = {
"cpi_warning": 0.95,
"cpi_critical": 0.85,
"spi_warning": 0.95,
"spi_critical": 0.85,
"trir_warning": 2.0,
"risk_score_warning": 7.0
}
def __init__(self, portfolio_name: str):
self.portfolio_name = portfolio_name
self.projects: Dict[str, ProjectMetrics] = {}
self.snapshots: List[Dict] = [] # Historical data
def add_project(self, metrics: ProjectMetrics):
"""Add or update project in portfolio."""
self.projects[metrics.project_id] = metrics
def import_projects(self, projects_data: List[Dict]) -> int:
"""Import multiple projects from data."""
count = 0
for p in projects_data:
metrics = ProjectMetrics(
project_id=p['id'],
project_name=p['name'],
status=ProjectStatus(p.get('status', 'active')),
contract_value=p['contract_value'],
percent_complete=p.get('percent_complete', 0),
planned_start=p['planned_start'],
planned_end=p['planned_end'],
actual_start=p.get('actual_start'),
forecast_end=p.get('forecast_end', p['planned_end']),
budget=p['budget'],
actual_cost=p.get('actual_cost', 0),
forecast_cost=p.get('forecast_cost', p['budget']),
cpi=p.get('cpi', 1.0),
spi=p.get('spi', 1.0),
recordable_incidents=p.get('incidents', 0),
total_hours=p.get('total_hours', 0),
risk_score=p.get('risk_score', 0),
critical_risks=p.get('critical_risks', 0)
)
# Calculate derived metrics
metrics.cost_variance = metrics.budget - metrics.actual_cost
metrics.schedule_variance_days = (metrics.planned_end - metrics.forecast_end).days
if metrics.total_hours > 0:
metrics.trir = (metrics.recordable_incidents * 200000) / metrics.total_hours
self.add_project(metrics)
count += 1
return count
def get_active_projects(self) -> List[ProjectMetrics]:
"""Get list of active projects."""
return [p for p in self.projects.values()
if p.status == ProjectStatus.ACTIVE]
def calculate_portfolio_summary(self) -> PortfolioSummary:
"""Calculate portfolio-level summary metrics."""
active = self.get_active_projects()
all_projects = list(self.projects.values())
if not all_projects:
return None
# Totals
total_contract = sum(p.contract_value for p in all_projects)
total_budget = sum(p.budget for p in all_projects)
total_actual = sum(p.actual_cost for p in all_projects)
total_forecast = sum(p.forecast_cost for p in all_projects)
# Performance averages (weighted by budget)
if total_budget > 0:
avg_cpi = sum(p.cpi * p.budget for p in active) / sum(p.budget for p in active) if active else 1.0
avg_spi = sum(p.spi * p.budget for p in active) / sum(p.budget for p in active) if active else 1.0
else:
avg_cpi = avg_spi = 1.0
# On budget/schedule percentages
on_budget = len([p for p in active if p.cpi >= 0.95])
on_schedule = len([p for p in active if p.spi >= 0.95])
on_budget_pct = (on_budget / len(active) * 100) if active else 100
on_schedule_pct = (on_schedule / len(active) * 100) if active else 100
# Safety metrics
total_incidents = sum(p.recordable_incidents for p in all_projects)
total_hours = sum(p.total_hours for p in all_projects)
portfolio_trir = (total_incidents * 200000 / total_hours) if total_hours > 0 else 0
# Health distribution
green = len([p for p in active if p.health == HealthStatus.GREEN])
yellow = len([p for p in active if p.health == HealthStatus.YELLOW])
red = len([p for p in active if p.health == HealthStatus.RED])
# Trends (compare to previous snapshot if available)
cost_trend = "stable"
schedule_trend = "stable"
if self.snapshots:
prev = self.snapshots[-1]
if avg_cpi > prev.get('avg_cpi', 1.0):
cost_trend = "improving"
elif avg_cpi < prev.get('avg_cpi', 1.0):
cost_trend = "declining"
if avg_spi > prev.get('avg_spi', 1.0):
schedule_trend = "improving"
elif avg_spi < prev.get('avg_spi', 1.0):
schedule_trend = "declining"
return PortfolioSummary(
report_date=datetime.now(),
total_projects=len(all_projects),
active_projects=len(active),
total_contract_value=total_contract,
total_budget=total_budget,
total_actual_cost=total_actual,
total_forecast_cost=total_forecast,
avg_cpi=avg_cpi,
avg_spi=avg_spi,
on_budget_pct=on_budget_pct,
on_schedule_pct=on_schedule_pct,
portfolio_trir=portfolio_trir,
total_incidents=total_incidents,
green_count=green,
yellow_count=yellow,
red_count=red,
cost_trend=cost_trend,
schedule_trend=schedule_trend
)
def compare_projects(self, metric: str) -> ProjectComparison:
"""Compare projects by specific metric."""
active = self.get_active_projects()
if not active:
return None
metric_map = {
"cpi": lambda p: p.cpi,
"spi": lambda p: p.spi,
"percent_complete": lambda p: p.percent_complete,
"cost_variance": lambda p: p.cost_variance,
"trir": lambda p: p.trir,
"risk_score": lambda p: p.risk_score
}
if metric not in metric_map:
raise ValueError(f"Unknown metric: {metric}")
getter = metric_map[metric]
values = {p.project_name: getter(p) for p in active}
avg = statistics.mean(values.values())
# Best/worst depends on metric (higher CPI good, lower TRIR good)
if metric in ["trir", "risk_score"]:
best = min(values.items(), key=lambda x: x[1])
worst = max(values.items(), key=lambda x: x[1])
else:
best = max(values.items(), key=lambda x: x[1])
worst = min(values.items(), key=lambda x: x[1])
return ProjectComparison(
metric=metric,
projects=values,
avg=avg,
best=best,
worst=worst
)
def get_projects_at_risk(self) -> List[ProjectMetrics]:
"""Get projects that need attention."""
return [p for p in self.get_active_projects()
if p.health in [HealthStatus.YELLOW, HealthStatus.RED]]
def get_top_risks(self, limit: int = 10) -> List[Dict]:
"""Get top risks across portfolio."""
risks = []
for p in self.get_active_projects():
if p.risk_score > 0:
risks.append({
"project": p.project_name,
"risk_score": p.risk_score,
"critical_risks": p.critical_risks,
"cpi": p.cpi,
"spi": p.spi
})
return sorted(risks, key=lambda x: -x['risk_score'])[:limit]
def forecast_cash_needs(self, months: int = 6) -> List[Dict]:
"""Forecast cash needs across portfolio."""
forecasts = []
for month in range(1, months + 1):
month_date = datetime.now() + timedelta(days=month * 30)
month_spend = 0
for p in self.get_active_projects():
# Simple linear projection based on remaining work
remaining = p.forecast_cost - p.actual_cost
months_remaining = max(1, (p.forecast_end - datetime.now()).days / 30)
monthly_burn = remaining / months_remaining
month_spend += monthly_burn
forecasts.append({
"month": month_date.strftime("%Y-%m"),
"projected_spend": month_spend
})
return forecasts
def save_snapshot(self):
"""Save current state for trend analysis."""
summary = self.calculate_portfolio_summary()
if summary:
self.snapshots.append({
"date": datetime.now(),
"avg_cpi": summary.avg_cpi,
"avg_spi": summary.avg_spi,
"on_budget_pct": summary.on_budget_pct,
"on_schedule_pct": summary.on_schedule_pct,
"total_forecast": summary.total_forecast_cost
})
def generate_report(self) -> str:
"""Generate portfolio dashboard report."""
summary = self.calculate_portfolio_summary()
if not summary:
return "No projects in portfolio"
lines = [
"# Portfolio Dashboard",
"",
f"**Portfolio:** {self.portfolio_name}",
f"**Report Date:** {summary.report_date.strftime('%Y-%m-%d')}",
"",
"## Executive Summary",
"",
f"| Metric | Value |",
f"|--------|-------|",
f"| Total Projects | {summary.total_projects} ({summary.active_projects} active) |",
f"| Total Contract Value | ${summary.total_contract_value:,.0f} |",
f"| Total Budget | ${summary.total_budget:,.0f} |",
f"| Actual Cost to Date | ${summary.total_actual_cost:,.0f} |",
f"| Forecast at Completion | ${summary.total_forecast_cost:,.0f} |",
"",
"## Performance Indicators",
"",
f"| KPI | Value | Trend |",
f"|-----|-------|-------|",
f"| Avg CPI | {summary.avg_cpi:.2f} | {summary.cost_trend} |",
f"| Avg SPI | {summary.avg_spi:.2f} | {summary.schedule_trend} |",
f"| On Budget | {summary.on_budget_pct:.0f}% | |",
f"| On Schedule | {summary.on_schedule_pct:.0f}% | |",
f"| Portfolio TRIR | {summary.portfolio_trir:.2f} | |",
"",
"## Health Distribution",
"",
f"🟢 Green: {summary.green_count} | 🟡 Yellow: {summary.yellow_count} | 🔴 Red: {summary.red_count}",
""
]
# Projects at risk
at_risk = self.get_projects_at_risk()
if at_risk:
lines.extend([
"## Projects Requiring Attention",
"",
"| Project | Health | CPI | SPI | Critical Risks |",
"|---------|--------|-----|-----|----------------|"
])
for p in sorted(at_risk, key=lambda x: x.cpi):
health_icon = "🟡" if p.health == HealthStatus.YELLOW else "🔴"
lines.append(
f"| {p.project_name} | {health_icon} | {p.cpi:.2f} | {p.spi:.2f} | {p.critical_risks} |"
)
lines.append("")
# Project comparison
lines.extend([
"## Project Comparison - CPI",
"",
"| Project | CPI |",
"|---------|-----|"
])
cpi_compare = self.compare_projects("cpi")
if cpi_compare:
for name, value in sorted(cpi_compare.projects.items(), key=lambda x: -x[1]):
lines.append(f"| {name} | {value:.2f} |")
return "\n".join(lines)Quick Start
from datetime import datetime, timedelta
# Initialize dashboard
dashboard = PortfolioDashboard("Regional Construction Portfolio")
# Import project data
projects = [
{
"id": "PRJ-001",
"name": "Downtown Office Tower",
"status": "active",
"contract_value": 50000000,
"budget": 48000000,
"actual_cost": 25000000,
"forecast_cost": 49000000,
"percent_complete": 55,
"planned_start": datetime(2024, 1, 1),
"planned_end": datetime(2025, 6, 30),
"forecast_end": datetime(2025, 7, 15),
"cpi": 0.92,
"spi": 0.95,
"incidents": 2,
"total_hours": 150000,
"risk_score": 7.5,
"critical_risks": 2
},
{
"id": "PRJ-002",
"name": "Hospital Expansion",
"status": "active",
"contract_value": 80000000,
"budget": 75000000,
"actual_cost": 30000000,
"forecast_cost": 74000000,
"percent_complete": 40,
"planned_start": datetime(2024, 3, 1),
"planned_end": datetime(2026, 2, 28),
"forecast_end": datetime(2026, 2, 28),
"cpi": 1.02,
"spi": 1.00,
"incidents": 0,
"total_hours": 100000,
"risk_score": 4.0,
"critical_risks": 0
}
]
dashboard.import_projects(projects)
# Get portfolio summary
summary = dashboard.calculate_portfolio_summary()
print(f"Portfolio Value: ${summary.total_contract_value:,.0f}")
print(f"Avg CPI: {summary.avg_cpi:.2f}")
print(f"On Budget: {summary.on_budget_pct:.0f}%")
# Find projects at risk
at_risk = dashboard.get_projects_at_risk()
print(f"Projects at risk: {len(at_risk)}")
# Compare projects
cpi_comparison = dashboard.compare_projects("cpi")
print(f"Best CPI: {cpi_comparison.best[0]} ({cpi_comparison.best[1]:.2f})")
# Generate report
print(dashboard.generate_report())Requirements
pip install (no external dependencies)