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Most invoice templates look good until you actually need to use them.
That was the problem I wanted to solve when I built this Professional Invoice Management System in Excel.
Instead of creating another static invoice sheet, I designed a complete invoicing workflow that helps freelancers, agencies, and small businesses generate clean, professional invoices with less manual work.
What it does:
✓ Automatically generates invoice numbers
✓ Calculates totals, taxes, balances, and payment status
✓ Tracks due dates and payment information
✓ Includes a built in product and service database
✓ Keeps business settings centralized for easy customization
✓ Creates a polished, client ready invoice experience
The goal was simple:
Make invoicing feel less like paperwork and more like a professional business process.
What I enjoyed most about this project was combining spreadsheet automation with user experience design. A lot of Excel tools are powerful but overwhelming. I focused on making this one clean, intuitive, and easy for anyone to use.
Projects like this remind me that no code and low code tools can be incredibly powerful when they're designed around real business needs.
Always building.
Always improving.
Always looking for smarter ways to automate everyday work.
excelautamationnocodebusinesssystemsMicrosoft ExcelData AnalysisLow-Code/No-Code DevelopmentExcel VBAMicrosoft Outlook
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ABC Classification (Pareto Analysis) — SQL-Based Inventory Prioritization
Analyzed 293,569 orders and 2,350 SKUs to identify which products actually drive revenue — and which don't.
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Built a CTE-based ABC classification in SQL Server, ranking products by cumulative revenue contribution using window functions (SUM() OVER, CUME_DIST(), PERCENT_RANK()), then segmented into three tiers based on Pareto thresholds.
Result:
Category A (20% of products) → 70%+ of total revenue
Category B (30% of products) → 20-25% of revenue
Category C (50% of products) → 5-10% of revenue
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This reprioritizes where operational effort should go — tighter stock monitoring and faster reordering for Category A, reduced tracking overhead for Category C. The same logic fed into a related late-delivery root cause analysis on the same dataset.
Stack: SQL Server · Tableau
