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

Author

Listed:
  • Prayas Sharma

    (Babasaheb Bhimrao Ambedkar University)

  • Vivek Kamthan

    (UPES)

  • Anirudh Singh

    (UPES)

  • Chanderkant Sheoran

    (Indian Airforce)

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 Currently, the aviation arm of defence service relies on an outdated forecasting model, which hampers effective inventory management and supply chain efficiency.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.

Suggested Citation

  • Prayas Sharma & Vivek Kamthan & Anirudh Singh & Chanderkant Sheoran, 2025. "Optimizing supply chain of aviation arm of defence service: harnessing predictive analytics for enhanced spare forecasting accuracy," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 16(9), pages 3090-3125, September.
    • Handle: RePEc:spr:ijsaem:v:16:y:2025:i:9:d:10.1007_s13198-025-02860-y
    DOI: 10.1007/s13198-025-02860-y
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    References listed on IDEAS

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