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Blending enterprise resource planning on supply chain management in the aerospace sector in India and analysis using multi-scale adaptive dilated convolutional LSTM

Author

Listed:
  • Joydeep Banerjee
  • Santanu Kumar Das

Abstract

Aerospace organisations, which have already executed enterprise resource management (ERP) tools along with the supply chain management (SCM) models are considered in this research work. At first, information is gathered from these organisations as a form of structured questions. These questions are then evaluated using relevant statistical methods to obtain the necessary goal. After that, the distribution of the questions to the authorised parties of these organisations is done. The authorities of these industries are requested provide information asked more precisely. At the final stage, the effectiveness of integrating the SCM with ERP is validated with the help of multi-scale adaptive dilated convolutional long-short-term memory (MADC-LSTM). The optimisation of the MADC-LSTM network's parameters is carried out by Golden Eagle with bee collecting pollen optimisation algorithm (GE-BCPOA). The effectiveness of integrating SCM with the ERP is analysed by conducting diverse experiments.

Suggested Citation

  • Joydeep Banerjee & Santanu Kumar Das, 2025. "Blending enterprise resource planning on supply chain management in the aerospace sector in India and analysis using multi-scale adaptive dilated convolutional LSTM," International Journal of Business Continuity and Risk Management, Inderscience Enterprises Ltd, vol. 15(1), pages 63-94.
  • Handle: RePEc:ids:ijbcrm:v:15:y:2025:i:1:p:63-94
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