Stochastic simulation under input uncertainty for contract manufacturer selection in pharmaceutical industry

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Abstract

We consider a pharmaceutical company that sources a biological product from a set of unreliable contract manufacturers. The likelihood of a manufacturer to successfully deliver the product is estimated via logistic regression as a function of the product attributes. The assignment of a product to the right contract manufacturers is of critical importance for the pharmaceutical company, and simulation-based optimization is used to identify the optimal sourcing decision. However, the input uncertainty due to the uncertain parameters of the logistic regression model often leads to poor sourcing decisions. We quantify the decrease in the expected profit due to input uncertainty as a function of the size of the historical data set, the level of dispersion in the historical data of a product attribute, and the number of products. We also introduce a sampling-based algorithm that reduces the expected decrease in the expected profit.
Original languageEnglish
Title of host publicationProceedings of the 2016 Winter Simulation Conference
EditorsT.M.K Roeder, P.I. Frazier, R. Szechtman, E. Zhou, T. Huschka, S.E. Chick
Place of PublicationWashington, D.C.
PublisherINFORMS Institute for Operations Research and the Management Sciences
Pages2292-2303
Number of pages12
DOIs
Publication statusPublished - 2016
Event2016 Winter Simulation Conference (WSC 2016) - Washington, D.C., Washington, United States
Duration: 11 Dec 201614 Dec 2016
http://informs-sim.org/wsc16papers/by_area.html

Conference

Conference2016 Winter Simulation Conference (WSC 2016)
Abbreviated titleWSC 2016
CountryUnited States
CityWashington
Period11/12/1614/12/16
Internet address

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Cite this

Akçay, A. E., & Martagan, T. G. (2016). Stochastic simulation under input uncertainty for contract manufacturer selection in pharmaceutical industry. In T. M. K. Roeder, P. I. Frazier, R. Szechtman, E. Zhou, T. Huschka, & S. E. Chick (Eds.), Proceedings of the 2016 Winter Simulation Conference (pp. 2292-2303). Washington, D.C.: INFORMS Institute for Operations Research and the Management Sciences. https://doi.org/10.1109/WSC.2016.7822270