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  Early detection for supply chain disruption using bayesian network  
Diupload oleh : Arif Wibisono, S.Kom, M.Sc
Pengarang : Arif Wibisono, Amna Shifia Nisafani, Hyerim Bae, You-jin Park
Tahun : 2014
Dipublikasikan di : Asia Pacific Business Process Management, Brisbane, Australia, 3-4 June 2014
Jenis Jurnal : International Conference
Eksternal Link : http://
Bidang Penelitian : Business Process Management
Abstrak : Supply Chain Disruptions (SCDs) such as labor disputes, bad weather, defective materials, and transportation matters can make a huge impact on both the responsiveness and effectiveness of a supply chain. Therefore, it is necessary to develop a reliable tool to foreseeing SCDs in their early stages. Here, this study aims to develop a reliable method to detect disruption in the supply chain process (SCP) using Bayesian Network (BN) and K2 Algorithm. The method is in twofold: first, an independent engine generates a new structure of Bayesian Network by periodically invokes K2 algorithm; second, the independent engine uses the generated BN to find the most disruptive point(s) in SCP. The prominent feature of this study is its ability to support many process structures such as sequence, XOR, and iterative.
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