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

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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.

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Data Availability

The data that support the findings of this study are available on request from the corresponding author.

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Authors and Affiliations

  1. School of Business and Management, Lean Operations and Systems, CHRIST (Deemed to be University), Bangalore, India

    Manikandan Rajagopal

  2. Department of Computer Science, School of Sciences, CHRIST (Deemed to be University), Bangalore, India

    Ramkumar Sivasakthivel

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Contributions

Manikandan Rajagopal has conceived the research idea, prepared the manuscript and carried out the language check. Ramkumar Sivasakthivel has performed the data collection, survey, and model building.

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Correspondence to Manikandan Rajagopal.

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Rajagopal, M., Sivasakthivel, R. An Empirical Framework Using Weighted Feed Forward Neural Network for Supply Chain Resilience (SCR) Strategy Selection. Oper. Res. Forum 5, 33 (2024). https://doi.org/10.1007/s43069-024-00313-z

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