Cwicr Productivity Tracker

CWICR Productivity Tracker

Business Case

Problem Statement

Project performance tracking requires:

• Comparing actual vs planned productivity
• Identifying underperforming activities
• Forecasting completion dates
• Learning from historical data

Solution

Track productivity by comparing actual hours/quantities against CWICR norms, generating variance analysis and forecasts.

Business Value

• Performance visibility - Real-time productivity metrics
• Early warning - Identify issues before escalation
• Continuous improvement - Learn from variances
• Accurate forecasting - Data-driven predictions

Technical Implementation

import pandas as pd
import numpy as np
from typing import Dict, Any, List, Optional, Tuple
from dataclasses import dataclass, field
from datetime import datetime, timedelta
from enum import Enum
from collections import defaultdict

class PerformanceStatus(Enum):
    """Performance status categories."""
    EXCELLENT = "excellent"      # >110% productivity
    ON_TARGET = "on_target"      # 90-110%
    BELOW_TARGET = "below_target"  # 70-90%
    CRITICAL = "critical"        #  PerformanceStatus:
        """Determine performance status from productivity rate."""
        if productivity_rate >= 110:
            return PerformanceStatus.EXCELLENT
        elif productivity_rate >= 90:
            return PerformanceStatus.ON_TARGET
        elif productivity_rate >= 70:
            return PerformanceStatus.BELOW_TARGET
        else:
            return PerformanceStatus.CRITICAL

    def calculate_productivity(self,
                               work_item_code: str,
                               actual_hours: float,
                               actual_quantity: float,
                               date: datetime = None) -> ProductivityRecord:
        """Calculate productivity for single work item."""

        if date is None:
            date = datetime.now()

        if self._work_index is not None and work_item_code in self._work_index.index:
            work_item = self._work_index.loc[work_item_code]
            labor_norm = float(work_item.get('labor_norm', 0) or 0)
            planned_hours = labor_norm * actual_quantity

            # Productivity rate (planned/actual * 100)
            productivity_rate = (planned_hours / actual_hours * 100) if actual_hours > 0 else 0

            # Variances
            hours_variance = planned_hours - actual_hours
            cost_variance = hours_variance * self.labor_rate

            return ProductivityRecord(
                work_item_code=work_item_code,
                description=str(work_item.get('description', '')),
                date=date,
                planned_hours=round(planned_hours, 2),
                actual_hours=actual_hours,
                planned_quantity=actual_quantity,  # Using actual as target
                actual_quantity=actual_quantity,
                productivity_rate=round(productivity_rate, 1),
                status=self._get_status(productivity_rate),
                variance_hours=round(hours_variance, 2),
                labor_cost_variance=round(cost_variance, 2)
            )
        else:
            return ProductivityRecord(
                work_item_code=work_item_code,
                description="NOT FOUND",
                date=date,
                planned_hours=0,
                actual_hours=actual_hours,
                planned_quantity=actual_quantity,
                actual_quantity=actual_quantity,
                productivity_rate=0,
                status=PerformanceStatus.CRITICAL,
                variance_hours=0,
                labor_cost_variance=0
            )

    def track_daily_production(self,
                                records: List[Dict[str, Any]]) -> ProductivitySummary:
        """Track daily production from multiple records."""

        productivity_records = []

        for record in records:
            prod = self.calculate_productivity(
                work_item_code=record.get('work_item_code', record.get('code')),
                actual_hours=record.get('actual_hours', 0),
                actual_quantity=record.get('actual_quantity', 0),
                date=record.get('date', datetime.now())
            )
            productivity_records.append(prod)

        # Aggregate
        total_planned = sum(r.planned_hours for r in productivity_records)
        total_actual = sum(r.actual_hours for r in productivity_records)

        overall_productivity = (total_planned / total_actual * 100) if total_actual > 0 else 0

        # By status
        by_status = defaultdict(int)
        for r in productivity_records:
            by_status[r.status.value] += 1

        # Get date range
        dates = [r.date for r in productivity_records if r.date]
        period_start = min(dates) if dates else datetime.now()
        period_end = max(dates) if dates else datetime.now()

        return ProductivitySummary(
            period_start=period_start,
            period_end=period_end,
            total_planned_hours=round(total_planned, 2),
            total_actual_hours=round(total_actual, 2),
            overall_productivity=round(overall_productivity, 1),
            hours_variance=round(total_planned - total_actual, 2),
            cost_variance=round((total_planned - total_actual) * self.labor_rate, 2),
            records=productivity_records,
            by_status=dict(by_status),
            by_category={},
            trend=[]
        )

    def forecast_completion(self,
                            remaining_work: List[Dict[str, Any]],
                            current_productivity: float,
                            available_hours_per_day: float = 80) -> Dict[str, Any]:
        """Forecast completion based on current productivity."""

        # Calculate remaining planned hours
        total_planned = 0
        for item in remaining_work:
            code = item.get('work_item_code', item.get('code'))
            qty = item.get('quantity', 0)

            if self._work_index is not None and code in self._work_index.index:
                work_item = self._work_index.loc[code]
                labor_norm = float(work_item.get('labor_norm', 0) or 0)
                total_planned += labor_norm * qty

        # Adjust for productivity
        if current_productivity > 0:
            actual_hours_needed = total_planned / (current_productivity / 100)
        else:
            actual_hours_needed = total_planned

        # Days to complete
        days_to_complete = actual_hours_needed / available_hours_per_day if available_hours_per_day > 0 else 0

        return {
            'remaining_planned_hours': round(total_planned, 1),
            'estimated_actual_hours': round(actual_hours_needed, 1),
            'current_productivity': current_productivity,
            'days_to_complete': int(np.ceil(days_to_complete)),
            'forecasted_completion': datetime.now() + timedelta(days=int(np.ceil(days_to_complete))),
            'productivity_impact': round(actual_hours_needed - total_planned, 1)
        }

    def analyze_variance(self,
                         summary: ProductivitySummary) -> Dict[str, Any]:
        """Analyze productivity variances in detail."""

        # Get critical items
        critical = [r for r in summary.records if r.status == PerformanceStatus.CRITICAL]
        below_target = [r for r in summary.records if r.status == PerformanceStatus.BELOW_TARGET]

        # Top impact items (by cost variance)
        sorted_by_impact = sorted(summary.records, key=lambda x: x.labor_cost_variance)
        top_negative = [r for r in sorted_by_impact[:5] if r.labor_cost_variance  0]

        return {
            'overall_productivity': summary.overall_productivity,
            'total_hours_variance': summary.hours_variance,
            'total_cost_variance': summary.cost_variance,
            'critical_items_count': len(critical),
            'below_target_count': len(below_target),
            'critical_items': [
                {'code': r.work_item_code, 'productivity': r.productivity_rate, 'variance': r.labor_cost_variance}
                for r in critical
            ],
            'top_negative_impact': [
                {'code': r.work_item_code, 'variance': r.labor_cost_variance}
                for r in top_negative
            ],
            'top_positive_impact': [
                {'code': r.work_item_code, 'variance': r.labor_cost_variance}
                for r in top_positive
            ],
            'recommendations': self._generate_recommendations(critical, below_target)
        }

    def _generate_recommendations(self,
                                   critical: List[ProductivityRecord],
                                   below_target: List[ProductivityRecord]) -> List[str]:
        """Generate improvement recommendations."""
        recommendations = []

        if len(critical) > 0:
            recommendations.append(
                f"Immediate attention needed for {len(critical)} critical items"
            )

        if len(below_target) > 3:
            recommendations.append(
                "Consider crew training or method review for underperforming activities"
            )

        # Check for patterns
        critical_codes = [r.work_item_code for r in critical]
        if any('CONC' in code for code in critical_codes):
            recommendations.append("Review concrete work methods and crew composition")
        if any('EXCV' in code for code in critical_codes):
            recommendations.append("Check equipment availability and operator skills for excavation")

        return recommendations

    def export_report(self,
                      summary: ProductivitySummary,
                      output_path: str) -> str:
        """Export productivity report to Excel."""

        with pd.ExcelWriter(output_path, engine='openpyxl') as writer:
            # Details
            details_df = pd.DataFrame([
                {
                    'Work Item': r.work_item_code,
                    'Description': r.description,
                    'Date': r.date.strftime('%Y-%m-%d'),
                    'Planned Hours': r.planned_hours,
                    'Actual Hours': r.actual_hours,
                    'Productivity %': r.productivity_rate,
                    'Status': r.status.value,
                    'Hours Variance': r.variance_hours,
                    'Cost Variance': r.labor_cost_variance
                }
                for r in summary.records
            ])
            details_df.to_excel(writer, sheet_name='Details', index=False)

            # Summary
            summary_df = pd.DataFrame([{
                'Period Start': summary.period_start.strftime('%Y-%m-%d'),
                'Period End': summary.period_end.strftime('%Y-%m-%d'),
                'Total Planned Hours': summary.total_planned_hours,
                'Total Actual Hours': summary.total_actual_hours,
                'Overall Productivity %': summary.overall_productivity,
                'Hours Variance': summary.hours_variance,
                'Cost Variance': summary.cost_variance
            }])
            summary_df.to_excel(writer, sheet_name='Summary', index=False)

            # By Status
            status_df = pd.DataFrame([
                {'Status': status, 'Count': count}
                for status, count in summary.by_status.items()
            ])
            status_df.to_excel(writer, sheet_name='By Status', index=False)

        return output_path

class ProductivityDashboard:
    """Generate productivity dashboard data."""

    def __init__(self, tracker: CWICRProductivityTracker):
        self.tracker = tracker

    def get_kpis(self, summary: ProductivitySummary) -> Dict[str, Any]:
        """Get key performance indicators."""
        return {
            'overall_productivity': summary.overall_productivity,
            'productivity_status': 'Good' if summary.overall_productivity >= 90 else 'Needs Attention',
            'hours_saved': max(0, summary.hours_variance),
            'hours_over': abs(min(0, summary.hours_variance)),
            'cost_impact': summary.cost_variance,
            'items_on_target': summary.by_status.get('on_target', 0) + summary.by_status.get('excellent', 0),
            'items_below_target': summary.by_status.get('below_target', 0) + summary.by_status.get('critical', 0)
        }

    def get_trend_data(self,
                       historical_summaries: List[ProductivitySummary]) -> pd.DataFrame:
        """Get productivity trend data for charting."""
        data = []
        for s in historical_summaries:
            data.append({
                'date': s.period_end.strftime('%Y-%m-%d'),
                'productivity': s.overall_productivity,
                'planned_hours': s.total_planned_hours,
                'actual_hours': s.total_actual_hours
            })
        return pd.DataFrame(data)

Quick Start

# Load CWICR data
cwicr = pd.read_parquet("ddc_cwicr_en.parquet")

# Initialize tracker
tracker = CWICRProductivityTracker(cwicr, labor_rate=35.0)

# Track daily production
records = [
    {'work_item_code': 'CONC-001', 'actual_hours': 45, 'actual_quantity': 50},
    {'work_item_code': 'REBAR-002', 'actual_hours': 32, 'actual_quantity': 2000},
    {'work_item_code': 'EXCV-003', 'actual_hours': 28, 'actual_quantity': 100}
]

summary = tracker.track_daily_production(records)

print(f"Overall Productivity: {summary.overall_productivity}%")
print(f"Hours Variance: {summary.hours_variance}")
print(f"Cost Variance: ${summary.cost_variance:,.2f}")

Common Use Cases

1. Variance Analysis

analysis = tracker.analyze_variance(summary)
for rec in analysis['recommendations']:
    print(rec)

2. Completion Forecast

remaining = [
    {'work_item_code': 'CONC-001', 'quantity': 100},
    {'work_item_code': 'REBAR-002', 'quantity': 5000}
]
forecast = tracker.forecast_completion(remaining, current_productivity=85.0)
print(f"Days to Complete: {forecast['days_to_complete']}")

3. Export Report

tracker.export_report(summary, "productivity_report.xlsx")

Resources

• GitHub: OpenConstructionEstimate-DDC-CWICR
• DDC Book: Chapter 3.1 - Productivity Management

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: datadrivenconstruction
• Source: datadrivenconstruction/DDCSkillsforAIAgentsin_Construction
• License: MIT
• Homepage: https://datadrivenconstruction.io/

Install and usage instructions live in the source repository linked above.