Multi-Organization Consolidated Reporting

Early Access - The Dcycle CLI is currently available for enterprise customers. Contact us to learn more about access.

For holding companies and enterprise groups, this guide shows how to automate consolidated sustainability reporting across multiple organizations.

Use Case

Your company structure:

Acme Holding Corp
├── Acme Spain (manufacturing)
├── Acme France (manufacturing)
├── Acme UK (sales)
└── Acme Logistics (transport)

You need to:

Generate a single consolidated report
Compare performance across subsidiaries
Track group-wide reduction targets

Quick Start: Export All Organizations

#!/bin/bash
# export_all_orgs.sh - Export data from all organizations

YEAR=${1:-2024}
OUTPUT_DIR="reports/$YEAR"
mkdir -p "$OUTPUT_DIR"

echo "📊 Exporting data for all organizations ($YEAR)..."

# Get list of all organizations
ORG_IDS=$(dc org list --format json | jq -r '.[].id')

for org_id in $ORG_IDS; do
    # Get organization name (sanitized for filename)
    org_name=$(dc org show $org_id --format json | jq -r '.name' | tr ' /' '_')

    echo "  Exporting: $org_name"

    # Set organization context
    dc org set $org_id

    # Export key data
    dc facility list --format json > "$OUTPUT_DIR/${org_name}_facilities.json"
    dc vehicle list --format json > "$OUTPUT_DIR/${org_name}_vehicles.json"

    echo "    ✓ Exported to $OUTPUT_DIR/${org_name}_*.json"
done

echo "✅ Export complete: $OUTPUT_DIR/"

Consolidated Emissions Report

Generate a single report with emissions from all subsidiaries:

#!/bin/bash
# consolidated_emissions.sh

YEAR=${1:-2024}
OUTPUT_FILE="reports/consolidated_emissions_$YEAR.json"

echo "📊 Generating consolidated emissions report for $YEAR..."

# Start JSON array
echo "[" > "$OUTPUT_FILE"
first=true

for org_id in $(dc org list --format json | jq -r '.[].id'); do
    # Set context
    dc org set $org_id > /dev/null

    # Get org details
    org_info=$(dc org show $org_id --format json)
    org_name=$(echo $org_info | jq -r '.name')
    org_country=$(echo $org_info | jq -r '.country')

    # Get emissions (assuming this command exists)
    # Adapt to actual CLI commands available
    emissions=$(dc emissions summary --year $YEAR --format json 2>/dev/null || echo '{}')

    # Add comma separator
    if [ "$first" = true ]; then
        first=false
    else
        echo "," >> "$OUTPUT_FILE"
    fi

    # Write organization data
    cat >> "$OUTPUT_FILE" << EOF
  {
    "organization_id": "$org_id",
    "name": "$org_name",
    "country": "$org_country",
    "year": $YEAR,
    "emissions": $emissions
  }
EOF

    echo "  ✓ $org_name"
done

# Close JSON array
echo "]" >> "$OUTPUT_FILE"

echo "✅ Report saved to: $OUTPUT_FILE"

Comparison Report

Compare performance across subsidiaries:

#!/bin/bash
# compare_subsidiaries.sh

YEAR=${1:-2024}

echo "═══════════════════════════════════════════════════════════════════"
echo "  SUSTAINABILITY PERFORMANCE BY SUBSIDIARY - $YEAR"
echo "═══════════════════════════════════════════════════════════════════"
echo ""
printf "%-25s %10s %10s %10s %12s\n" "Organization" "Scope 1" "Scope 2" "Scope 3" "Total"
printf "%-25s %10s %10s %10s %12s\n" "────────────" "───────" "───────" "───────" "─────"

TOTAL_S1=0
TOTAL_S2=0
TOTAL_S3=0
TOTAL_ALL=0

for org_id in $(dc org list --format json | jq -r '.[].id'); do
    dc org set $org_id > /dev/null

    name=$(dc org show $org_id --format json | jq -r '.name' | cut -c1-25)

    # Get emissions by scope
    # Adapt these commands to your actual CLI
    scope1=$(dc vehicle list --format json | jq '[.[].emissions_tco2e // 0] | add // 0')
    scope2=$(dc invoice list --year $YEAR --format json 2>/dev/null | jq '[.[].emissions_tco2e // 0] | add // 0' || echo "0")
    scope3=$(dc purchase list --year $YEAR --format json 2>/dev/null | jq '[.[].emissions_tco2e // 0] | add // 0' || echo "0")

    total=$(echo "$scope1 + $scope2 + $scope3" | bc)

    printf "%-25s %10.1f %10.1f %10.1f %12.1f\n" "$name" "$scope1" "$scope2" "$scope3" "$total"

    TOTAL_S1=$(echo "$TOTAL_S1 + $scope1" | bc)
    TOTAL_S2=$(echo "$TOTAL_S2 + $scope2" | bc)
    TOTAL_S3=$(echo "$TOTAL_S3 + $scope3" | bc)
    TOTAL_ALL=$(echo "$TOTAL_ALL + $total" | bc)
done

echo "────────────────────────────────────────────────────────────────────"
printf "%-25s %10.1f %10.1f %10.1f %12.1f\n" "GROUP TOTAL" "$TOTAL_S1" "$TOTAL_S2" "$TOTAL_S3" "$TOTAL_ALL"
echo ""
echo "All values in tCO2e"

Output:

═══════════════════════════════════════════════════════════════════
  SUSTAINABILITY PERFORMANCE BY SUBSIDIARY - 2024
═══════════════════════════════════════════════════════════════════

Organization              Scope 1    Scope 2    Scope 3       Total
────────────              ───────    ───────    ───────       ─────
Acme Spain                  456.2      234.5      678.9      1369.6
Acme France                 234.1      189.3      456.7       880.1
Acme UK                      98.4      123.4      234.5       456.3
Acme Logistics              987.6      145.2      567.8      1700.6
────────────────────────────────────────────────────────────────────
GROUP TOTAL                1776.3      692.4     1937.9      4406.6

All values in tCO2e

Python Consolidation Script

For more complex reporting needs:

#!/usr/bin/env python3
# consolidate_report.py

import json
import subprocess
from dataclasses import dataclass
from typing import Optional

@dataclass
class OrgEmissions:
    org_id: str
    name: str
    country: str
    scope_1: float = 0
    scope_2: float = 0
    scope_3: float = 0

    @property
    def total(self) -> float:
        return self.scope_1 + self.scope_2 + self.scope_3

def run_dc(*args) -> dict:
    """Run dc command and return JSON output"""
    result = subprocess.run(
        ['dc'] + list(args) + ['--format', 'json'],
        capture_output=True,
        text=True
    )
    if result.returncode != 0:
        return {}
    return json.loads(result.stdout) if result.stdout else {}

def get_all_organizations() -> list[dict]:
    """Get list of all accessible organizations"""
    return run_dc('org', 'list')

def set_organization(org_id: str):
    """Set active organization context"""
    subprocess.run(['dc', 'org', 'set', org_id], capture_output=True)

def get_org_emissions(org_id: str, year: int) -> OrgEmissions:
    """Get emissions for a single organization"""
    set_organization(org_id)

    # Get org info
    org_info = run_dc('org', 'show', org_id)

    emissions = OrgEmissions(
        org_id=org_id,
        name=org_info.get('name', 'Unknown'),
        country=org_info.get('country', '??')
    )

    # Get scope 1 (vehicles)
    vehicles = run_dc('vehicle', 'list')
    if isinstance(vehicles, list):
        emissions.scope_1 = sum(v.get('emissions_tco2e', 0) for v in vehicles)

    # Get scope 2 (invoices) - adapt to actual commands
    # emissions.scope_2 = ...

    # Get scope 3 (purchases, logistics) - adapt to actual commands
    # emissions.scope_3 = ...

    return emissions

def generate_consolidated_report(year: int) -> dict:
    """Generate consolidated emissions report"""
    orgs = get_all_organizations()
    all_emissions = []

    for org in orgs:
        emissions = get_org_emissions(org['id'], year)
        all_emissions.append(emissions)

    # Calculate totals
    total_s1 = sum(e.scope_1 for e in all_emissions)
    total_s2 = sum(e.scope_2 for e in all_emissions)
    total_s3 = sum(e.scope_3 for e in all_emissions)

    return {
        'year': year,
        'generated_at': datetime.now().isoformat(),
        'organizations': [
            {
                'id': e.org_id,
                'name': e.name,
                'country': e.country,
                'scope_1_tco2e': e.scope_1,
                'scope_2_tco2e': e.scope_2,
                'scope_3_tco2e': e.scope_3,
                'total_tco2e': e.total,
            }
            for e in all_emissions
        ],
        'group_totals': {
            'scope_1_tco2e': total_s1,
            'scope_2_tco2e': total_s2,
            'scope_3_tco2e': total_s3,
            'total_tco2e': total_s1 + total_s2 + total_s3,
        }
    }

if __name__ == '__main__':
    import sys
    from datetime import datetime

    year = int(sys.argv[1]) if len(sys.argv) > 1 else 2024
    report = generate_consolidated_report(year)
    print(json.dumps(report, indent=2))

Year-over-Year Tracking

Track progress against reduction targets:

#!/bin/bash
# yoy_tracking.sh

CURRENT_YEAR=2024
BASELINE_YEAR=2020
TARGET_REDUCTION=50  # percent by 2030

echo "═══════════════════════════════════════════════════════════════════"
echo "  YEAR-OVER-YEAR PROGRESS TRACKING"
echo "  Baseline: $BASELINE_YEAR | Current: $CURRENT_YEAR | Target: -${TARGET_REDUCTION}% by 2030"
echo "═══════════════════════════════════════════════════════════════════"
echo ""

printf "%-20s %12s %12s %10s %10s\n" "Organization" "$BASELINE_YEAR" "$CURRENT_YEAR" "Change" "On Track?"
printf "%-20s %12s %12s %10s %10s\n" "────────────" "────────" "────────" "──────" "─────────"

for org_id in $(dc org list --format json | jq -r '.[].id'); do
    dc org set $org_id > /dev/null

    name=$(dc org show $org_id --format json | jq -r '.name' | cut -c1-20)

    # Get emissions for both years (adapt to actual commands)
    baseline=1000  # placeholder - replace with actual query
    current=800    # placeholder - replace with actual query

    if [ "$baseline" -gt 0 ]; then
        change=$(echo "scale=1; (($current - $baseline) / $baseline) * 100" | bc)
        # Required rate to hit target by 2030
        years_remaining=$((2030 - CURRENT_YEAR))
        years_elapsed=$((CURRENT_YEAR - BASELINE_YEAR))
        required_rate=$(echo "scale=1; $TARGET_REDUCTION / (2030 - $BASELINE_YEAR) * $years_elapsed" | bc)
        actual_reduction=$(echo "scale=1; -1 * $change" | bc)

        if (( $(echo "$actual_reduction >= $required_rate" | bc -l) )); then
            status="✅ Yes"
        else
            status="⚠️  Behind"
        fi

        printf "%-20s %12.1f %12.1f %9.1f%% %10s\n" "$name" "$baseline" "$current" "$change" "$status"
    fi
done

Automated Monthly Report

Schedule a monthly consolidated report:

# .github/workflows/monthly-consolidated-report.yml
name: Monthly Consolidated Report

on:
  schedule:
    - cron: '0 7 1 * *'  # 1st of each month at 7 AM

jobs:
  generate-report:
    runs-on: ubuntu-latest
    env:
      DCYCLE_API_KEY: ${{ secrets.DCYCLE_API_KEY }}

    steps:
      - uses: actions/checkout@v4

      - name: Install dependencies
        run: brew install dcy

      - name: Generate consolidated report
        run: |
          python scripts/consolidate_report.py $(date +%Y) > report.json

      - name: Upload report artifact
        uses: actions/upload-artifact@v4
        with:
          name: consolidated-report-${{ github.run_number }}
          path: report.json

      - name: Send to stakeholders
        run: |
          # Send via email, Slack, or upload to shared drive
          curl -X POST "${{ secrets.SLACK_WEBHOOK }}" \
            -H 'Content-Type: application/json' \
            -d @- << EOF
          {
            "text": "📊 Monthly consolidated sustainability report is ready",
            "blocks": [
              {
                "type": "section",
                "text": {
                  "type": "mrkdwn",
                  "text": "*Monthly Consolidated Report*\nGenerated: $(date)\nDownload from: ${{ github.server_url }}/${{ github.repository }}/actions/runs/${{ github.run_id }}"
                }
              }
            ]
          }
          EOF

Best Practices

Consistent Data Periods

Ensure all subsidiaries report for the same time period to enable accurate consolidation.

Currency Normalization

If using spend-based factors, normalize currencies before consolidating.

Boundary Definitions

Document which entities are included/excluded from consolidated reports.

Intercompany Elimination

Avoid double-counting internal logistics or shared services.

Next Steps

CI/CD Pipeline

Automate with GitHub Actions or GitLab CI

Logistics Automation

Daily logistics data pipeline

Getting Started

Commands

Examples

Multi-Organization Consolidated Reporting

Use Case

Quick Start: Export All Organizations

Consolidated Emissions Report

Comparison Report

Python Consolidation Script

Year-over-Year Tracking

Automated Monthly Report

Best Practices

Consistent Data Periods

Currency Normalization

Boundary Definitions

Intercompany Elimination

Next Steps

CI/CD Pipeline

Logistics Automation

​Use Case

​Quick Start: Export All Organizations

​Consolidated Emissions Report

​Comparison Report

​Python Consolidation Script

​Year-over-Year Tracking

​Automated Monthly Report

​Best Practices

Consistent Data Periods

Currency Normalization

Boundary Definitions

Intercompany Elimination

​Next Steps

CI/CD Pipeline

Logistics Automation

Use Case

Quick Start: Export All Organizations

Consolidated Emissions Report

Comparison Report

Python Consolidation Script

Year-over-Year Tracking

Automated Monthly Report

Best Practices

Next Steps