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An Empirical Framework Using Weighted Feed Forward Neural Network for Supply Chain Resilience (SCR) Strategy Selection

Author

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  • Manikandan Rajagopal

    (CHRIST (Deemed to be University))

  • Ramkumar Sivasakthivel

    (CHRIST (Deemed to be University))

Abstract

Artificial intelligence (AI)-based systems are normally data driven applications, where the model is trained to think on its own based on the external circumstances. The power of AI has reached every facet of business and common life and is even being largely explored to be adopted in life sciences and medical domains. It supports the human in decision-making through the cognitive utilities which arises out of self-learning capabilities of a model. With the exponential growth of data, supply chain management and analytics have attracted a large community of researchers to build intelligent systems which can lead to re-invention of data-driven decision systems powered by AI. Systems and literature of the past shows that AI-based technologies are promising in intelligent supply chain management (SCM) and building resilient SCMs. There is a gap in literature which addresses on the framework for decision support systems in SCM and application of AI methods for building a robust supply chain resilience (SCR) leading to more exploration on the topic. In this paper, a decision framework is proposed by incorporating fuzzy logic and recurrent neural networks (RNN) for disclosing the patterns of various AI-enabled techniques for SCRs. The proposed analysis involved data from leading literatures to determine the most adoptable and significant applications of AI in SCRs. The analysis shows that techniques such as fuzzy programing, network based algorithms, and genetic algorithms have large impact on building SCRs. The results help in decision-making by exhibiting an integrated framework which can help the AI practitioners for developing SCRs.

Suggested Citation

  • Manikandan Rajagopal & Ramkumar Sivasakthivel, 2024. "An Empirical Framework Using Weighted Feed Forward Neural Network for Supply Chain Resilience (SCR) Strategy Selection," SN Operations Research Forum, Springer, vol. 5(2), pages 1-19, June.
    • Handle: RePEc:spr:snopef:v:5:y:2024:i:2:d:10.1007_s43069-024-00313-z
    DOI: 10.1007/s43069-024-00313-z
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    References listed on IDEAS

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    1. S. M. Misbauddin & Mohammad Jahangir Alam & Chitra Lekha Karmaker & Md. Noor Un Nabi & Md. Mahedi Hasan, 2023. "Exploring the Antecedents of Supply Chain Viability in a Pandemic Context: An Empirical Study on the Commercial Flower Supply Chain of an Emerging Economy," Sustainability, MDPI, vol. 15(3), pages 1-19, January.
    2. Barbara Ocicka & Wioletta Mierzejewska & Jakub Brzeziński, 2022. "Correction: Creating supply chain resilience during and post-COVID-19 outbreak: the organizational ambidexterity perspective," DECISION: Official Journal of the Indian Institute of Management Calcutta, Springer;Indian Institute of Management Calcutta, vol. 49(3), pages 361-361, September.
    3. Amine Belhadi & Sachin Kamble & Samuel Fosso Wamba & Maciel M. Queiroz, 2022. "Building supply-chain resilience: an artificial intelligence-based technique and decision-making framework," International Journal of Production Research, Taylor & Francis Journals, vol. 60(14), pages 4487-4507, July.
    4. Angela Pennisi di Floristella & Xuechen Chen, 2022. "Building resilient supply chains in uncertain times: a comparative study of EU and ASEAN approaches to supply chain resilience," Asia Europe Journal, Springer, vol. 20(4), pages 457-475, December.
    5. Debashree Das & Avik Datta & Patanjal Kumar & Yigit Kazancoglu & Mangey Ram, 2022. "Building supply chain resilience in the era of COVID-19: An AHP-DEMATEL approach," Operations Management Research, Springer, vol. 15(1), pages 249-267, June.
    6. R. Rajesh, 2022. "A novel advanced grey incidence analysis for investigating the level of resilience in supply chains," Annals of Operations Research, Springer, vol. 308(1), pages 441-490, January.
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