Improving WPI Forecasting for Stable Toll Revenue Growth

Client Overview

A national investment trust managing road infrastructure assets needed greater predictability in toll revenue planning. Since toll charges were directly linked to the Wholesale Price Index, frequent WPI fluctuations created uncertainty in pricing, revenue projections, and long-term investment planning.

For leadership, the challenge was not limited to forecasting inflation-linked toll adjustments. It was about improving financial visibility, reducing pricing uncertainty, and supporting more confident asset valuation decisions.

Problem Statement

The client’s toll pricing model was highly dependent on WPI movements, making revenue forecasts vulnerable to macroeconomic volatility.

Key challenges included:

• Toll charges were directly linked to WPI, creating revenue uncertainty
• Lack of an accurate forecasting model led to unpredictable pricing fluctuations
• Inconsistent WPI projections affected financial planning and budgeting
• Limited visibility into macroeconomic trends made it difficult to align with RBI estimates
• Long-term investment decisions were exposed to uncertainty around toll escalation assumptions

For CXO teams, this created a broader strategic concern: unstable forecasts could impact revenue confidence, cost of capital assumptions, and investor decision-making.

Solution Provided

Decimal Point Analytics developed a predictive analytics framework to improve WPI forecasting across short, medium, and long-term horizons.

The solution included:

ML-Powered Forecasting Models

Built machine learning models to generate more accurate WPI forecasts across multiple time periods.

Macroeconomic Indicator Integration

Integrated relevant macroeconomic variables and AI-based predictive analytics to strengthen forecast reliability.

Benchmark Alignment

Compared projections with RBI quarterly estimates to improve credibility and decision-making confidence.

Real-Time Model Updates

Fine-tuned models with updated data inputs to ensure continuity, relevance, and accuracy.

Monitoring and Drift Detection

Implemented a structured monitoring framework with monthly, quarterly, and annual model runs, including drift detection and performance recalibration.

Interactive Simulation Portal

Developed a visualization portal that enabled users to test economic scenarios, adjust key drivers, and view forecast outcomes.

Outcome

The predictive analytics framework helped the client strengthen toll revenue planning and reduce uncertainty around WPI-linked pricing.

Key results included:

• WPI projections exceeded RBI quarterly estimates in accuracy
• Forecasts were 20% to 30% more accurate than RBI benchmarks
• Year-on-year toll forecasts were maintained within 1% to 1.2% deviation
• Improved toll pricing stability enabled more predictable annual increases
• Enhanced financial planning supported more confident investment decisions
• Scenario-based simulations improved visibility into economic drivers and forecast outcomes
• Predictable toll escalation assumptions helped reduce cost of capital pressure

Key Takeaway

For road infrastructure investors and asset managers, WPI volatility can directly influence revenue confidence, asset valuation, and capital planning.

By applying predictive analytics to WPI forecasting, Decimal Point Analytics helped the client move from reactive pricing assumptions to a more stable, data-driven forecasting framework. This enabled stronger financial planning, better investment visibility, and improved confidence in long-term toll revenue projections.

Explore how Decimal Point Analytics helps infrastructure and investment leaders improve forecasting accuracy, strengthen revenue visibility, and make confident data-driven decisions.