Fleet Expansion &
Operational Feasibility Study
Developing a Monte Carlo simulation to evaluate the financial impact of adding a 3rd vehicle to a courier fleet. Using probabilistic modeling to calculate ROI and break-even points under fluctuating demand.
Tool
Microsoft Excel
Focus
Data Analysis
Overview
This project modeled demand and capacity using statistical distributions to optimize logistics and fleet sizes for a mid-scale distribution center. By leveraging advanced Monte Carlo simulation techniques within a spreadsheet environment, I provided stakeholders with a robust framework to evaluate capital investments under high-uncertainty conditions.
Methodology
Strategic Pillars
Demand & Loss Modeling
Using Uniform distributions for hourly package volume and Poisson for lost/failed deliveries.
Traffic Impact Analysis
Modeling delivery performance drops during peak hours (09:00 & 17:00) using stochastic traffic coefficients.
Revenue Elasticity
Implementing an inverse-linear relationship between daily revenue levels and incoming cargo volume.
Amortization Analysis
Determining the 1.5-year payback period for a 1.000.000 ₺ investment based on net profit simulations.
The "How"
Technical Deep Dive
Logic Engineering
Developed a dynamic simulation model using complex nested IF statements and probability-weighted lookup tables.
Iteration Engine
Utilized Excel’s Data Tables to run iterative scenarios, observing the system's performance across different revenue tiers.
Fleet Optimization
Compared 2-vehicle vs. 3-vehicle configurations to identify the threshold where a new driver becomes profitable.
Key Learnings
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Practical Risk Modeling
Applying theoretical distributions to solve real-world business problems like logistics bottlenecks.
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Investment Intuition
Understanding how operational costs and fixed investments interact with market demand.