Optimizing Financial Control in Public Infrastructure
A statistical analysis of stage-wise billing data for Taneja Vidyut Control Pvt. Ltd. Leveraged Pareto Concentration and Linear Regression to mitigate operational risk and achieve a 96.7% accuracy in final project cost projection.
View Data & Methodology on GitHubIn large-scale infrastructure, financial visibility is often obscured by fragmented data architectures. TVC Pvt. Ltd. faced significant challenges with siloed billing data across RA bills and recovery sheets. As a Business Analyst, my objective was to transition the firm from descriptive accounting to predictive financial management, identifying cost-drivers before they impacted the bottom line.
The Civil division demonstrated a strong linear correlation between timeline and expenditure, allowing for highly accurate mid-project forecasting.
Pareto analysis revealed that 6.7% of billable items accounted for 80% of total expenditure, isolating the "Vital Few" for executive monitoring.
Discovered systemic data integrity flaws in the Electrical division, including a ₹8.75M reconciliation variance in a single reporting cycle.
Developed division-specific models: Linear Regression for Civil (steady-state) and front-loaded trend analysis for Electrical procurement.
Utilized Pareto logic to refocus management bandwidth on high-impact line items like Tubular Steel and MRL Lift procurement.
Implemented stage-wise linear modeling to project final project outlays based on early-stage RA bill trends.
Quantitative reconciliation of Reported vs. Calculated values to expose and mitigate systematic accounting errors.
Establish automated reconciliation protocols to eliminate the high variance found in manual RA bill processing.
Deploy real-time tracking for the "Vital 7" cost drivers to prevent budget overruns and optimize cash flow.
Standardize regression-based forecasting after the 3rd billing cycle to move from reactive budgeting to proactive capital allocation.