Skip to main content
Client Project Manufacturing

Finance Process Automation

Month-End Close Optimization for Swiss Manufacturing

Month-end close went from a 12-day scramble to a streamlined 5-day process. — Anonymized client, Precision Manufacturing, Zürich region

Client details anonymized at their request. Metrics are estimates based on project data and may not reflect exact outcomes.

~40h Estimated monthly time saved
~CHF 48K Estimated annual savings
~4.1mo Estimated payback period
~58% Estimated close time reduction

Executive Summary

Industry: Precision Manufacturing
Location: Canton of Zürich, Switzerland
Company Size: 85 employees, CHF 18M revenue
Finance Team: 4 people (1 CFO, 2 accountants, 1 controller)
ERP System: SAP Business One
Implementation: Claude AI + Custom Python Scripts + Power BI

Estimated Results

Metric Before After Improvement
Month-End Close 12 days 5 days -58%
Reconciliation Time 16 hours 4 hours -75%
Variance Analysis 8 hours 1.5 hours -81%
Report Generation 6 hours 30 min -92%
Error Rate 4.2% 0.3% -93%
Annual Cost Savings CHF 48,000

The Challenge

A precision manufacturing company with 40+ years of history was struggling with outdated month-end processes:

Manual Reconciliation

Bank reconciliation across 3 accounts, AP/AR matching with 200+ vendors and customers, inventory reconciliation against physical counts—all done in Excel with manual cross-referencing.

16 hours/month just on reconciliation

Variance Analysis Bottleneck

Budget vs. actual analysis required exporting data from SAP, reformatting in Excel, manually identifying variances, and writing explanations. The controller spent days on this alone.

8 hours/month with frequent errors

Report Assembly Chaos

Executive reports were assembled from 7 different data sources. Each month, the CFO manually created P&L, balance sheet, cash flow, and KPI reports in PowerPoint.

6 hours/month of copy-paste work

Late Financial Insights

By the time financials were ready (day 12), the data was stale. Management decisions were based on 6-week-old information. Competitors with faster closes were more agile.

12 days average close time

The Tipping Point

When the controller announced her retirement, the CFO realized the institutional knowledge locked in Excel macros and manual processes would be lost. They needed to systematize and automate before it was too late.

The Solution

Using the eflury Method™, we implemented a phased automation approach over 8 weeks.

1

Automated Reconciliation Engine

Built a Claude-powered reconciliation system that:

  • Imports bank statements automatically via SWIFT/MT940
  • Matches transactions against SAP entries using fuzzy logic
  • Flags exceptions for human review instead of manual matching
  • Generates reconciliation reports with variance explanations
Reconciliation time reduced from 16 hours to 4 hours
2

Intelligent Variance Analysis

Created a Claude Skill that:

  • Pulls budget and actual data from SAP automatically
  • Calculates variances by cost center, project, and GL account
  • Generates natural language explanations for significant variances
  • Suggests investigation priorities based on materiality
Variance analysis reduced from 8 hours to 1.5 hours
3

Executive Reporting Dashboard

Implemented Power BI with AI-assisted narrative generation:

  • Real-time connection to SAP Business One
  • Automated P&L, balance sheet, and cash flow statements
  • KPI dashboard with drill-down capability
  • Claude-generated executive summary for each report
Report generation reduced from 6 hours to 30 minutes
4

Close Calendar Automation

Built a workflow orchestration system:

  • Automated task scheduling and dependencies
  • Slack notifications for task completion and blockers
  • Progress tracking dashboard for CFO visibility
  • Historical close metrics for continuous improvement
Month-end close reduced from 12 days to 5 days

Detailed Results

Time Savings

40 hours/month

Team now focuses on analysis and strategy instead of data entry and reconciliation.

Financial Impact

CHF 48K annual savings

Reduced overtime, eliminated temp staff during close, avoided backfill for retiring controller.

Data Quality

93% error reduction

From 4.2% error rate to 0.3%. Auditors noted "significantly improved controls" in annual review.

Decision Speed

7 days faster

Management now receives financial data by day 5 instead of day 12—enabling faster strategic decisions.

Return on Investment

Implementation Cost CHF 16,500
Annual Savings CHF 48,000
Payback Period 4.1 months
Year 1 ROI 191%

Technical Implementation

AI Engine: Claude Sonnet 4 for variance analysis and narrative generation
Data Pipeline: Python scripts for SAP data extraction and transformation
Visualization: Power BI Premium for real-time dashboards
Orchestration: Microsoft Power Automate for workflow management
Hosting: Azure Switzerland North (data residency compliant)

Data Security

All financial data remains within Swiss borders (Azure Switzerland North). The implementation complies with Swiss FADP, GDPR, and industry-standard SOC 2 controls. No financial data is sent to external AI providers—Claude processes only anonymized variance descriptions.

Ready to Transform Your Finance Operations?

If your finance team spends more time on data entry than analysis, we can help.