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AirLine_Fuel_Price_Dilemma

Strategic Decision Engine designed to optimize profitability and liquidity for an Indian regional airline facing a 25% fuel price shock. This project integrates Monte Carlo simulations, Linear Programming, and Real-time Financial Data to evaluate strategic trade-offs between pricing elasticity, government subsidy expansion (UDAN), and structural fleet redesign.

📋 Project Overview Problem Statement: A fast-growing regional airline faces a profitability crisis driven by a 15–25% surge in Aviation Turbine Fuel (ATF) prices. With 30–40% of routes becoming structurally unviable and load factor breakevens exceeding 85%, the airline required a data-driven turnaround strategy. The Solution: A Python-based simulation engine that ingests real-time market data (Brent Crude, USD/INR) to stress-test three strategic horizons:

  1. Network Optimization: Dynamic pricing and route rationalization.
  2. UDAN Expansion: Government-backed subsidy prioritization for downside protection.
  3. Structural Redesign: Long-term fleet transition analysis (Turboprop to Airbus A220/Embraer E195).

🚀 Key Features

  1. Real-Time Volatility Modeling • Fetches live market data using yfinance to model Brent Crude volatility and FX risk (USD/INR). • Constructs a dynamic Unit Economics Model calculating RASK, CASK, and contribution margins per route under stochastic fuel scenarios.
  2. Risk Analysis (Monte Carlo Simulation) • Performs 1,000+ iterations to quantify the "Risk of Ruin" (probability of negative EBITDA) for various strategic options. • Visualizes risk profiles using Kernel Density Estimation (KDE) plots to compare "fat-tail" risks of commercial routes vs. the stability of subsidized routes.
  3. Route Optimization Engine • Implements Integer Linear Programming (PuLP) to solve the "Knapsack Problem" for subsidy allocation. • Optimizes route selection to maximize total margin preserved and social connectivity under strict government budget constraints.
  4. Strategic Consulting Frameworks • Profitability Tree Analysis (McKinsey): Decomposes EBITDA drivers into volume, yield, fuel, and fixed cost levers. • MECE Decision Trees (Bain): Segments routes into "Safe," "Salvageable," "At-Risk," and "Unviable" quadrants for targeted intervention.

🛠️ Tech Stack & Methodologies • Languages: Python 3.10+ • Data Analysis: Pandas, NumPy, SciPy • Financial APIs: yfinance (Yahoo Finance), FRED (Federal Reserve Economic Data) • Optimization: PuLP (Linear Programming), Statsmodels • Visualization: Matplotlib, Seaborn • Strategy Frameworks: MECE, Sensitivity Analysis (Tornado Charts), Unit Economics, Cost-Benefit Analysis (NPV/IRR).

📊 Business Impact & Results • Quantified Risk: Demonstrated that the "Network Optimization" strategy carried a 35% risk of failure due to demand elasticity, whereas the "UDAN Subsidy" strategy reduced downside risk to <5%. • Turnaround Roadmap: Developed a 24-month "Blended Strategy" roadmap projected to swing EBITDA from -₹50 Cr to +₹650 Cr. • Subsidy Optimization: Identified that pure commercial pricing strategies failed due to the "Operating Leverage Paradox," necessitating a shift to government-backed revenue floors.

📂 Repository Structure ├── data/ │ ├── atf_timeseries.csv # Historical fuel price data [18] │ └── route_economics.csv # Route-level P&L and distance data ├── notebooks/ │ ├── 01_volatility_model.ipynb # Monte Carlo simulation of fuel prices │ ├── 02_route_optimization.ipynb # Linear programming for subsidy allocation [10] │ └── 03_sensitivity_analysis.ipynb # Tornado charts for profitability drivers [19] ├── src/ │ ├── financial_models.py # Unit economics and CASK/RASK calculators │ └── strategy_engine.py # Core decision logic ├── reports/ │ ├── executive_summary.pdf # Management consulting deliverable [2] │ └── risk_profile_charts.png # Visualization of strategic options [20, 21] └── README.md

📈 SEO Keywords • Financial Modeling Python • Monte Carlo Simulation • Airline Strategy Consulting • Business Intelligence Analyst • Linear Programming Optimization • Risk Management • Data Analytics Capstone • Unit Economics Analysis • Revenue Management • Python for Finance

⚖️ License & Attribution This project was developed for the Winter Consulting Capstone 2025 by the Consulting & Analytics Club, IIT Guwahati. Market data derived from public APIs; strategic frameworks adapted from standard management consulting methodologies (McKinsey/Bain).
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