Understanding why actual performance deviates from budget is one of the most critical functions in corporate finance. This project simulates an FP&A-style variance analysis across 36 months, identifying key drivers of budget deviation and providing management-level commentary to support strategic decision-making.
- Compare budgeted vs actual financial performance across 36 months
- Calculate and categorize variances as favorable or unfavorable
- Identify root causes of budget deviations by product and region
- Present findings through executive-level dashboards
- Provide actionable management commentary
| Metric | Value |
|---|---|
| Total Budget Revenue | $21,450,952 |
| Total Actual Revenue | $21,258,843 |
| Total Variance | -$192,109 |
| Overall Variance % | -0.9% |
| Favorable Records | 205 |
| Unfavorable Records | 227 |
| Best Performing Product | Office Supplies |
| Worst Performing Product | Technology |
| Best Performing Region | West |
| Worst Performing Region | East |
- Budget vs Actual Revenue
- Budget vs Actual Profit
- Variance ($) and Variance (%)
- Favorable vs Unfavorable Variance Count
- YTD Cumulative Variance
- Variance by Product & Region
- Generated realistic 36 month budget and actuals dataset
- Calculated revenue, cost and profit variances
- Categorized variances as favorable or unfavorable
- Monthly budget vs actual queries
- Variance drivers by product and region
- YTD cumulative variance tracking
- Best and worst performing months
- Monthly budget vs actual trend visualization
- Variance breakdown by product and region
- Favorable vs unfavorable pie chart
- YTD cumulative variance area chart
- Executive variance overview
- Variance deep dive analysis
- Management commentary page
- Total revenue missed budget by $192,109 (-0.9%) — a marginal but consistent underperformance
- 227 unfavorable records vs 205 favorable — indicating more months missed budget than exceeded it
- Overall variance is contained within 1% of budget
- Office Supplies is the best performing product vs budget
- Technology showed the largest unfavorable variance despite being the highest revenue segment
- Product mix shifts are a key driver of overall budget deviation
- West region consistently outperforms budget targets
- East region shows the largest unfavorable variance
- Regional resource reallocation toward West could improve overall performance
- Unfavorable variances are concentrated in specific months
- Seasonal budget assumptions require revision for Q1 periods
- YTD cumulative variance highlights compounding underperformance risk
- Revise Technology budget assumptions — consistent underperformance suggests overly optimistic targets
- Reallocate resources to West region — strongest variance performance across the period
- Review East region cost structure — largest unfavorable variance requires immediate attention
- Adjust Q1 budget assumptions to reflect seasonal patterns
- Implement monthly variance review process for faster management response
| Tool | Usage |
|---|---|
| Python | Dataset generation, variance analysis, visualization |
| SQL | Variance queries and KPI aggregation |
| Power BI | Interactive executive variance dashboard |
| pandas & matplotlib | Data manipulation and visualization |
budget-vs-actuals-analysis/
│
├── data/
│ ├── raw/ # Generated budget vs actuals dataset
│ └── processed/ # Cleaned variance dataset
├── sql/
│ └── variance_queries.sql # Budget vs actual SQL queries
├── python/
│ ├── data_preparation.ipynb # Dataset generation and cleaning
│ └── variance_analysis.ipynb # Variance analysis and visuals
├── powerbi/
│ └── variance_dashboard.pbix # 3 page executive dashboard
├── reports/
│ └── *.png # Exported visualizations
└── README.md
This project demonstrates corporate finance expertise combined with end-to-end analytics execution, delivering variance insights suitable for FP&A, Financial Planning, and Business Analyst roles.
Dataset is simulated to reflect realistic corporate financial planning scenarios across 36 months.
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## Commit message:
Update README with real variance analysis insights and financial metrics