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A Bayesian Inference Analysis of Supply Chain Enablers, Supply Chain Management Practices, and Performance

  • Behnam AzhdariEmail author
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 966)

Abstract

In this study, a Causal Bayesian network (CBN) model of the causal relationships between supply chain enablers, supply chain management practices and supply chain performances is empirically developed and analyzed. Study data collected from a sample of 199 manufacturing firms producing the most influential products in Iran’s economy. Resultant CBN model revealed important causalities between study variables of interest. Afterwards, using Dirichlet estimator of TETRAD 6-4-0 software, conditional probability estimation with Bayesian networks, also known as Bayesian inference was developed. The outcomes of this study in general, support the idea that SC enablers, especially IT technologies, don’t have direct impact on SC performance. Also forward Bayesian inference provided deeper understanding of causal relationships in supply chain context, such as what antecedents must be available to reach better level at each critical supply chain performance measures. Also it is found out that in any tier of supply chain concepts; there may be some important intra-relations which worth of further studies.

Keywords

Supply chain management Supply chain performance Causal Bayesian network Bayesian inference 

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of ManagementIslamic Azad University, Khark BranchKhark IslandIran

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