RESEARCH PAPER
Intermittent Demand Forecasting of Aircraft Components through Installed Base Forecasting: A Case Study
1
Department of Transport Management and Logistics Engineering, University of Moratuwa, Sri Lanka
2
Center for Supply Chain, Operations and Logistics Optimization, University of Moratuwa, Sri Lanka
3
Department of Production Engineering and Management, Wroclaw University of Science and Technology, Poland
Submission date: 2025-12-23
Final revision date: 2026-02-20
Acceptance date: 2026-02-24
Online publication date: 2026-02-26
Corresponding author
Maria Rosienkiewicz
Department of Production Engineering and Management, Wroclaw University of Science and Technology, Lukasiewicza 5, 50-371, Wroclaw, Poland
Department of Production Engineering and Management, Wroclaw University of Science and Technology, Lukasiewicza 5, 50-371, Wroclaw, Poland
KEYWORDS
artificial neural networkforecasting modeldemand forecastingspare partsaircraft componentsinstalled base
TOPICS
ABSTRACT
This research addresses the inefficiencies in traditional forecasting methods for the intermittent and erratic demand for aircraft components, which typically results to either high inventory costs or expensive aircraft-on-ground situations from stockouts. The main objective is to build an enhanced forecasting model that uses installed base information to improve demand forecasting accuracy for aircraft components. An extensive literature review followed by a Delphi method study is used to identify the significant contextual factors impacting the demand and integrated into an Artificial Neural Network forecasting model. The model's performance is evaluated using Mean Squared Error and benchmarked against the Croston method. This research provides efficient forecasting practices to the airline industry, aiming to reduce operating expenses and improve maintenance as well as service quality.
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