Risk assessment in pharmaceutical supply chains under unknown input-model parameters

Alp Akcay, Tugce Martagan, Canan G. Corlu

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Abstract

We consider a pharmaceutical supply chain where the manufacturer sources a customized product with unique attributes from a set of unreliable suppliers. We model the likelihood of a supplier to successfully deliver the product via Bayesian logistic regression and use simulation to obtain the posterior distribution of the unknown parameters of this model. We study the role of so-called input-model uncertainty in estimating the likelihood of the supply failure, which is the probability that none of the suppliers in a given supplier portfolio can successfully deliver the product. We investigate how the input-model uncertainty changes with respect to the characteristics of the historical data on the past realizations of the supplier performances and the product attributes.

Original languageEnglish
Title of host publicationWSC 2018 - 2018 Winter Simulation Conference
Subtitle of host publicationSimulation for a Noble Cause
EditorsM. Rabe, A.A. Juan, N. Mustafee, A. Skoogh, S. Jain, B. Johansson
Place of PublicationPiscataway
PublisherInstitute of Electrical and Electronics Engineers
Pages3132-3143
Number of pages12
ISBN (Electronic)9781538665725
DOIs
Publication statusPublished - 31 Jan 2019
Event2018 Winter Simulation Conference, WSC 2018 - Gothenburg, Sweden
Duration: 9 Dec 201812 Dec 2018

Conference

Conference2018 Winter Simulation Conference, WSC 2018
Abbreviated titleWSC 2018
CountrySweden
CityGothenburg
Period9/12/1812/12/18

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