Solving IndiGo’s Pilot Inventory Crisis Through Safety Stock and Service Level Optimization
Management of Inventory Systems | Course Project - Spring Semester 2026 | Department of Industrial and Systems Engineering, IIT Kharagpur
Author: Sri Vaishnavi Viswanathan
| File | Description |
|---|---|
Management_of_Inventory_Systems_Course_Project - Case Study |
Case study report on optimizing Indigo's pilot safety stock and service level to prevent the 2025 operational crisis |
simulation_1_monte_carlo.ipynb |
Monte Carlo validation of theoretical service levels |
simulation_1_monte_carlo.png |
Simulation Results Graph |
Validates the theoretical service levels derived analytically in the case study by simulating monthly pilot demand from the fitted normal distribution and measuring empirical stockout frequency.
Model: D ~ N(μ=2085, σ=197) where σ combines absence, operational, and seasonal variability.
What it does:
- Runs 10,000 simulated months of pilot demand per service level
- Measures empirical stockout frequency and compares it to the theoretical target
- Plots the demand histogram with stockout zone highlighted
- Compares theoretical vs simulated service levels across all 5 candidates (90%, 95%, 97%, 99%, 99.5%)
Output: simulation_1_monte_carlo.png
python simulation_1_monte_carlo.py
Sample output:
SL 90.0% | N=2,337 | Theoretical: 90.0% | Simulated: 90.05% | Δ = +0.05pp | Stockouts: 995
SL 95.0% | N=2,410 | Theoretical: 95.0% | Simulated: 95.12% | Δ = +0.12pp | Stockouts: 488
SL 97.0% | N=2,455 | Theoretical: 97.0% | Simulated: 96.95% | Δ = -0.05pp | Stockouts: 305
SL 99.0% | N=2,544 | Theoretical: 99.0% | Simulated: 98.94% | Δ = -0.06pp | Stockouts: 106
SL 99.5% | N=2,593 | Theoretical: 99.5% | Simulated: 99.48% | Δ = -0.02pp | Stockouts: 52
numpy
scipy
matplotlib
Install with:
pip install numpy scipy matplotlib
See the full case study for references to IndiGo annual reports, FDTL regulatory documents, salary data, and aviation operations literature.