A Simulation Modeling Approach to Optimize Order Policy for Critical Helicopter Spare Parts Under Uncertainty

Ioannis Tsemperlides (), Pavlos Eirinakis () and Dimitrios Emiris ()
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Ioannis Tsemperlides: University of Piraeus, Department of Industrial Management and Technology
Pavlos Eirinakis: University of Piraeus, Department of Industrial Management and Technology
Dimitrios Emiris: University of Piraeus, Department of Industrial Management and Technology

A chapter in Advanced Data Analytics, Machine Learning and AI in Business, 2026, pp 456-472 from Springer

Abstract: Abstract Simulation modeling is a reliable and cost-effective approach, to analyze and optimize complex real-world systems. This paper presents a simulation model for a mission-readiness production system, where critical helicopter spare parts are treated as inputs and fleet availability as the primary output. The study proposes a dynamic, discrete-event simulation model that utilizes stochasticity to capture demand variability driven by the need to replace critical components as flight hours accumulate. Key sources of uncertainty are recognized and incorporated into the model, including the yield of repairs and their respective turnaround times, as well as procurement lead times. The model is structured to help logistics managers better anticipate shortages, optimize inventory levels, and allocate budgets efficiently within a defined planning horizon. Randomness is introduced via Monte Carlo sampling, based on probability distributions that best fit the available historical data. Parametric analysis is performed to evaluate the influence of system parameters on the production of flight hours and to quantify the risk of aircraft on ground occurrence due to component shortages. The findings indicate that, under uncertainty, efficient spare parts management relies on a combined strategy of repairs and procurements. Components with identical overhaul time intervals are found to exhibit different aircraft on ground probabilities, ranging in some cases from near zero to over 50% under increased flight hour production target. The variability in time between replacements and associated lead times, indicate that the analysis of historical data prior to repair and inventory planning can yield significant cost savings and reveal otherwise hidden risks. This approach proves valuable, especially in systems where real-world experimentation is impractical.

Keywords: Simulation modeling; Helicopter readiness; Uncertainty modeling; Monte Carlo sampling; Ordering policy (search for similar items in EconPapers)
Date: 2026
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Persistent link: https://EconPapers.repec.org/RePEc:spr:lnopch:978-3-032-23493-3_28

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DOI: 10.1007/978-3-032-23493-3_28

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