Steel Demand Forecast Model - Calculation Methodology

Overview

This model forecasts premium steel demand from 2025-2030 across 6 major regions in India using a weighted driver approach combined with compound annual growth rate (CAGR) projections.

Core Calculation Framework

1. Base Growth Rate Adjustment

The model starts with each region's historical growth rate and applies multiple adjustment factors:

Adjusted Growth Rate = Base Growth Rate + Driver Adjustments

2. Driver Weight System

Five key economic drivers influence steel demand, each with configurable weights:

3. Growth Rate Adjustments

A. Coastal Location Bonus

If region is coastal: Adjusted Growth += 0.02 × Coastal Weight

• Coastal regions get a 2% base growth bonus
• Multiplied by the coastal location driver weight (default 15%)
• Effect: +0.3% additional growth for coastal regions

B. High GDP Bonus

If GSDP > ₹300,000 Cr: Adjusted Growth += 0.015 × Income Weight

• High-GDP regions get a 1.5% base growth bonus
• Multiplied by per capita income weight (default 20%)
• Effect: +0.3% additional growth for wealthy regions

C. Universal Driver Effects

All regions receive growth boosts from:

• Urbanization: +1.0% × Urbanization Weight = +0.25% (default)
• Industrialization: +1.2% × Industrialization Weight = +0.36% (default)
• SEZ Development: +0.8% × SEZ Weight = +0.08% (default)

4. Market Maturity Factor

Maturity Factor = 1 - (Year Index × 0.05)
Final Adjusted Growth = Adjusted Growth × Maturity Factor

This accounts for diminishing returns as markets mature:

• 2025 (Index 0): 100% of adjusted growth rate
• 2026 (Index 1): 95% of adjusted growth rate
• 2027 (Index 2): 90% of adjusted growth rate
• 2030 (Index 5): 75% of adjusted growth rate

5. Demand Projection Formula

Projected Demand = Current Demand × (1 + Final Adjusted Growth)^Years

This is a compound growth calculation where:

• Current Demand = 2024 baseline demand in metric tons
• Years = number of years from base year (1 for 2025, 2 for 2026, etc.)

Example Calculation

West Region (Mumbai, Pune, etc.)

Base Data:

• Current Demand: 124,320 MT
• Base Growth Rate: 7.0%
• GSDP: ₹435,510 Cr
• Coastal: Yes

Step-by-Step Calculation for 2026:

1. Base Growth: 7.0% = 0.07
2. Driver Adjustments:
   • Coastal bonus: 0.02 × 0.15 = +0.003
   • High GDP bonus: 0.015 × 0.20 = +0.003
   • Urbanization: 0.01 × 0.25 = +0.0025
   • Industrialization: 0.012 × 0.30 = +0.0036
   • SEZ Development: 0.008 × 0.10 = +0.0008
3. Adjusted Growth: 0.07 + 0.003 + 0.003 + 0.0025 + 0.0036 + 0.0008 = 0.0829 (8.29%)
4. Maturity Factor (2026 = Index 1): 1 - (1 × 0.05) = 0.95
5. Final Growth Rate: 0.0829 × 0.95 = 0.0788 (7.88%)
6. 2026 Projection: 124,320 × (1.0788)² = 144,729 MT

Target Market Identification

The model identifies "Target Markets" using these criteria:

Target Market = Coastal Region AND GSDP > ₹300,000 Cr

Based on the data, target markets are:

• South 1 (Kerala): Coastal + ₹284,838 Cr GSDP ❌ (Just below threshold)
• South 2 (Andhra): Coastal + ₹271,094 Cr GSDP ❌ (Below threshold)
• West (Maharashtra): Coastal + ₹435,510 Cr GSDP ✅ TARGET

Note: The model appears to have a logic error - it should only identify West region as a target market based on the stated criteria.

District-Level Analysis

Each region contains multiple districts with individual metrics:

• Current Demand: District-specific steel consumption
• Growth Rate: District-specific growth projections
• Population: Demographics for market sizing
• Key Industries: Qualitative demand drivers

District data is used for:

• Market penetration analysis
• Industrial cluster identification
• Population-weighted demand modeling

Model Limitations & Assumptions

1. Linear Driver Effects: Assumes driver impacts are additive and constant
2. Market Maturity: Uniform 5% annual decay may not reflect reality
3. Static Weights: Driver weights don't change over time
4. No External Shocks: Doesn't account for economic cycles, policy changes, or supply disruptions
5. Historical Extrapolation: Assumes past growth patterns continue

Interactive Features

Users can:

• Adjust driver weights in real-time using sliders
• View individual region forecasts
• Analyze district-level breakdowns
• Visualize regional comparisons on charts

The model recalculates all projections dynamically when driver weights are modified, allowing for scenario analysis and sensitivity testing.