A Framework for Assessing Disruptions in a Clinical Supply Chain Using Bayesian Belief Networks

Mark Rodgers; Dashi Singham

doi:10.1007/s12247-019-09396-2

A Framework for Assessing Disruptions in a Clinical Supply Chain Using Bayesian Belief Networks

Mark Rodgers, Dashi Singham

Rutgers Business School, Supply Chain Management

Research output: Contribution to journal › Article › peer-review

9 Scopus citations

Abstract

Purpose: Clinical trial study failures cause significant disruptions to supply chain operations, which lead to operational inefficiencies and financial losses. Methods: In this paper, a framework to construct a Bayesian belief network (BBN) by leveraging subject matter expertise and probabilistic elicitation methods to quantify the probability of a disruption to a clinical supply chain is presented. Results: The effect of varying input factors on a disruption probability is studied, and new metrics are developed to evaluate the significance of a disruption. Conclusions: This framework allows practitioners to assess the probability of disruptions to their network, thus enabling targeted strategies to be developed and implemented.

Original language	English (US)
Pages (from-to)	467-481
Number of pages	15
Journal	Journal of Pharmaceutical Innovation
Volume	15
Issue number	3
DOIs	https://doi.org/10.1007/s12247-019-09396-2
State	Published - Sep 1 2020

All Science Journal Classification (ASJC) codes

Pharmaceutical Science
Drug Discovery

Keywords

Bayesian belief networks
Clinical trials
Disruption
Probabilistic elicitation
Risk analysis
Supply chain management

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10.1007/s12247-019-09396-2

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title = "A Framework for Assessing Disruptions in a Clinical Supply Chain Using Bayesian Belief Networks",

abstract = "Purpose: Clinical trial study failures cause significant disruptions to supply chain operations, which lead to operational inefficiencies and financial losses. Methods: In this paper, a framework to construct a Bayesian belief network (BBN) by leveraging subject matter expertise and probabilistic elicitation methods to quantify the probability of a disruption to a clinical supply chain is presented. Results: The effect of varying input factors on a disruption probability is studied, and new metrics are developed to evaluate the significance of a disruption. Conclusions: This framework allows practitioners to assess the probability of disruptions to their network, thus enabling targeted strategies to be developed and implemented.",

keywords = "Bayesian belief networks, Clinical trials, Disruption, Probabilistic elicitation, Risk analysis, Supply chain management",

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AU - Singham, Dashi

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N2 - Purpose: Clinical trial study failures cause significant disruptions to supply chain operations, which lead to operational inefficiencies and financial losses. Methods: In this paper, a framework to construct a Bayesian belief network (BBN) by leveraging subject matter expertise and probabilistic elicitation methods to quantify the probability of a disruption to a clinical supply chain is presented. Results: The effect of varying input factors on a disruption probability is studied, and new metrics are developed to evaluate the significance of a disruption. Conclusions: This framework allows practitioners to assess the probability of disruptions to their network, thus enabling targeted strategies to be developed and implemented.

AB - Purpose: Clinical trial study failures cause significant disruptions to supply chain operations, which lead to operational inefficiencies and financial losses. Methods: In this paper, a framework to construct a Bayesian belief network (BBN) by leveraging subject matter expertise and probabilistic elicitation methods to quantify the probability of a disruption to a clinical supply chain is presented. Results: The effect of varying input factors on a disruption probability is studied, and new metrics are developed to evaluate the significance of a disruption. Conclusions: This framework allows practitioners to assess the probability of disruptions to their network, thus enabling targeted strategies to be developed and implemented.

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KW - Disruption

KW - Probabilistic elicitation

KW - Risk analysis

KW - Supply chain management

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A Framework for Assessing Disruptions in a Clinical Supply Chain Using Bayesian Belief Networks

Abstract

All Science Journal Classification (ASJC) codes

Keywords

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