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Adur Kannan, B., Kodi, G., Padilla, O., Gray, D., & Smith, B. C. (2020). Forecasting Spare Parts Sporadic Demand Using Traditional Methods and Machine Learning—A Comparative Study. SMU Data Science Review, 3, 9.

has been cited by the following article:

• TITLE: Adoption of Artificial Intelligence Techniques for Inventory Management: A Case Study in the Aviation Sector

  AUTHORS: Vikram Cathiyavadi Grahamspet Narendran, Arumugam Seetharaman, Koilakuntla Maddulety

  KEYWORDS: Inventory Management, Artificial Intelligence, Machine Learning, Deep Learning, Spares Management, Multi-Criteria Inventory Classification

  JOURNAL NAME: American Journal of Industrial and Business Management, Vol.14 No.5, May 30, 2024

  ABSTRACT: Spares management is of great significance as it not only regulates the flow of the inventory but also impacts the financial status of the company’s balance sheet. All organizations continuously try to maintain optimum inventory levels in order to not only meet their supplies and demands but also ensure there is no “excess or less inventory” that can directly influence the financial data. To perform the balancing act of these conflicting requirements is an ongoing process, especially in volatile industries like aviation with fluctuations in demand, lead time, and criticality. The aim is to categorize spares based on multiple criteria using AI techniques in the aviation sector by expanding the traditional VED classification method to achieve better inventory control.