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- W2921148412 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." @default.
- W2921148412 created "2019-03-22" @default.
- W2921148412 creator A5015186975 @default.
- W2921148412 date "2019-01-01" @default.
- W2921148412 modified "2023-09-25" @default.
- W2921148412 title "A Bayesian Inference Analysis of Supply Chain Enablers, Supply Chain Management Practices, and Performance" @default.
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- W2921148412 doi "https://doi.org/10.1007/978-3-030-16035-7_3" @default.
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