Optimizing supply chain of aviation arm of defence service: harnessing predictive analytics for enhanced spare forecasting accuracy

Prayas Sharma (), Vivek Kamthan, Anirudh Singh and Chanderkant Sheoran
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Prayas Sharma: Babasaheb Bhimrao Ambedkar University
Vivek Kamthan: UPES
Anirudh Singh: UPES
Chanderkant Sheoran: Indian Airforce

International Journal of System Assurance Engineering and Management, 2025, vol. 16, issue 9, No 10, 3090-3125

Abstract: Abstract The rapid advancement of technologies such as artificial intelligence (AI), big data, and the Internet of Things (IoT) has transformed industries by enhancing productivity and precision. This paper explores the application of predictive analytics and artificial neural networks (ANN) to optimize spare forecasting accuracy within the aviation arm of defence service. Currently, the aviation arm of defence service relies on an outdated forecasting model, which hampers effective inventory management and supply chain efficiency. By leveraging unclassified data, document analysis, and spare demand patterns, this study evaluates the potential of modern predictive tools to address these challenges. The research utilizes a descriptive methodology and a quantitative approach, focusing on key questions regarding the accuracy of the current forecasting model and the integration of advanced statistical tools. The findings suggest that the adoption of AI and big data analytics could significantly enhance forecasting accuracy and supply chain efficiency, addressing issues such as long lead times and complex logistics. The study aims to provide actionable insights for improving the aviation arm of defence service’s supply chain management and ensuring better preparedness and operational efficiency.

Keywords: Predictive analytics; Artificial neural networks (ANN); Spare forecasting; Supply chain management; Big data; Inventory management; Forecasting accuracy; Logistics optimization (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s13198-025-02860-y

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