Optimizing helicopter support : a simulation framework based on historical data analysis for effective order timing and quantity planning
Βελτιστοποίηση εφοδιαστικής υποστήριξης ελικοπτέρων : πλαίσιο προσομοίωσης βασισμένο σε ανάλυση ιστορικών δεδομένων για αποτελεσματικό προγραμματισμό παραγγελιών

Master Thesis
Author
Tsemperlides, Ioannis
Τσεμπερλίδης, Ιωάννης
Date
2025-05Advisor
Eirinakis, PavlosΕιρηνάκης, Παύλος
View/ Open
Keywords
Discrete-event simulation ; Monte Carlo sampling ; Aviation logistics ; Repairable inventory ; Buffer time ; Fleet availabilityAbstract
Fleet readiness in rotor-wing operations relies on timely decisions about whether to repair or procure high-value assemblies whose failure behavior is inherently uncertain.
Motivated by everyday challenges faced by logistics managers, this thesis proposes a spreadsheet-based, discrete-event Monte Carlo simulation that transforms historical records into a forward-looking decision-support tool. The conceptual model maps each
replacement event, its preceding uncertainties and time-between-replacements, repair yield, turnaround time, lead time and the budget flows that follow, deliberately focusing on the occurrence of a replacement rather than its technical cause.
Moreover, to demonstrate the approach, a hypothetical case study: the Captain-Planet Company problem is constructed with real but confidential helicopter data. The scenario frames a logistics department that must sustain a single Boeing Vertol 234
Chinook over finite flight-hour horizons while operating under fixed parameters. All stochastic drivers are parameterized with empirical or triangular distributions and sampled through Microsoft Excel, allowing the model to be replicated without specialized
software. Scenario experimentation highlights how alternative utilization rates reshape the envelope of expected replacements, buffer times and funding profiles, thus clarifying the trade-off between availability and cost.
Beyond the numerical results, the model could assist managers in several areas. In contract negotiations, it might offer objective estimates of spare‐part quantities and likely costs. For multi-year budgeting, it could clarify when expenditures are most likely to
occur. In logistics operations, linking replacement schedules with material flows may help determine warehouse capacity and workforce needs. Because the framework is independent of specific designs or maintenance policies, it could be adapted to any asset class that keeps basic replacement records from mining trucks to power-plant turbines.
Overall, this lightweight Monte Carlo approach has the potential to turn historical data into guidance for balancing readiness and cost.