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<p>Fluctuations in communication networks can significantly affect the performance and reliability of a digital twin. High latency can disrupt synchronization, low bandwidth can result in loss of data or delayed, and communication networks are vulnerable to different cyber-attacks. Variability in communication networks can reduce the reliability of a supply chain digital twin. So, this study presents a reliability assessment framework for supply chain digital twin using an integrated approach of Markov Chains and Bayesian Networks. The reliability model implements Markov Chain structures to handle the three network characteristics which include latency, bandwidth, and cybersecurity. Data transmission and security in digital twin systems experience dynamic disturbances due to fluctuations in these network characteristics. A Bayesian Network developed to show communication network factor dependencies on supply chain performance indicators such as visibility, data transmission velocity and lead time using system current data status. The study highlights how varying network factors influence the performance and resilience of supply chain operations, providing valuable insights for improving decision-making and risk management in digital twin applications.</p>

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Reliability assessment of supply chain digital twin: an integrated Markov Chain and Bayesian Network approach

  • 期刊论文

  • 发表日期：

    2025年7月17日

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  • Paras Garg,
  • Gunjan Soni,
  • Arvind Keprate,
  • O. P. Yadav,
  • A. P. S. Rathore

摘要

Fluctuations in communication networks can significantly affect the performance and reliability of a digital twin. High latency can disrupt synchronization, low bandwidth can result in loss of data or delayed, and communication networks are vulnerable to different cyber-attacks. Variability in communication networks can reduce the reliability of a supply chain digital twin. So, this study presents a reliability assessment framework for supply chain digital twin using an integrated approach of Markov Chains and Bayesian Networks. The reliability model implements Markov Chain structures to handle the three network characteristics which include latency, bandwidth, and cybersecurity. Data transmission and security in digital twin systems experience dynamic disturbances due to fluctuations in these network characteristics. A Bayesian Network developed to show communication network factor dependencies on supply chain performance indicators such as visibility, data transmission velocity and lead time using system current data status. The study highlights how varying network factors influence the performance and resilience of supply chain operations, providing valuable insights for improving decision-making and risk management in digital twin applications.