Digital Twin-Enabled Predictive Maintenance and Supply Chain Optimization for Fighter Aircraft
Abdulmajeed Alghamdi
Abdulmajeed M. Alghamdi, Independent Researcher, Saudi Arabia.
Manuscript received on 06 December 2025 | First Revised Manuscript received on 04 March 2026 | Second Revised Manuscript received on 06 May 2026 | Manuscript Accepted on 15 May 2026 | Manuscript published on 30 May 2026 | PP: 8-11 | Volume-16 Issue-2, May 2026 | Retrieval Number: 100.1/ijsce.F370415060126 | DOI: 10.35940/ijsce.F3704.16020526
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open-access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: This paper examines how a digital twin can support predictive maintenance and improve supply chain performance in fighter aircraft operations. The study reviews existing practices in condition monitoring and identifies the limitations of conventional maintenance and inventory planning methods used for military aviation fleets. A conceptual framework is then proposed to show how sensor data, aircraft health models, maintenance planning tools, and supply chain systems can interact as a single ecosystem. The framework demonstrates how real-time information can be used to anticipate component degradation, schedule maintenance more effectively, and align spare-parts procurement with predicted requirements. The findings highlight the potential of digital-twin technology to enhance aircraft readiness and reduce operational disruptions by improving both technical decision-making and material availability.
Keywords: Digital Twin, Predictive Maintenance, Fighter Jets, Supply Chain, Cost–Benefit Analysis.
Scope of the Article: Artificial Intelligence