Bayesian belief network-based framework for sourcing risk analysis during supplier selection

Bimal Nepal and Om Prakash Yadav

International Journal of Production Research, 2015, vol. 53, issue 20, 6114-6135

Abstract: Increasing trend in global business integration and movement of material around the world has caused supply chain system susceptible to disruption involving higher risks. This paper presents a methodology for supplier selection in a global sourcing environment by considering multiple cost and risk factors. Failure modes and effects analysis technique from reliability engineering field and Bayesian belief networks are used to quantify the risk posed by each factor. The probability and the cost of each risk are then incorporated into a decision tree model to compute the total expected costs for each supply option. The supplier selection decision is made based on the total purchasing costs including both deterministic costs (such as product and transportation costs) and the risk-associated costs. The proposed approach is demonstrated using an example of a US-based Chemical distributor. This framework provides a visual tool for supply chain managers to see how cost and risks are distributed across the different alternatives. Lastly, managers can calculate expected value of perfect information to avoid a certain risk.

Date: 2015
References: Add references at CitEc
Citations: View citations in EconPapers (10)

Downloads: (external link)
http://hdl.handle.net/10.1080/00207543.2015.1027011 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:tprsxx:v:53:y:2015:i:20:p:6114-6135

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/TPRS20

DOI: 10.1080/00207543.2015.1027011

Access Statistics for this article

International Journal of Production Research is currently edited by Professor A. Dolgui

More articles in International Journal of Production Research from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().