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

A.E. Akçay; T.G. Martagan

doi:10.1109/WSC.2016.7822270

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

A.E. Akçay, T.G. Martagan

Operations Planning Accounting & Control

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

5 Citations (Scopus)

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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 language	English
Title of host publication	Proceedings of the 2016 Winter Simulation Conference
Editors	T.M.K Roeder, P.I. Frazier, R. Szechtman, E. Zhou, T. Huschka, S.E. Chick
Place of Publication	Washington, D.C.
Publisher	INFORMS Institute for Operations Research and the Management Sciences
Pages	2292-2303
Number of pages	12
DOIs	https://doi.org/10.1109/WSC.2016.7822270
Publication status	Published - 2016
Event	2016 Winter Simulation Conference, WSC 2016 - Washington, D.C., Arlington, United States Duration: 11 Dec 2016 → 14 Dec 2016 http://informs-sim.org/wsc16papers/by_area.html

Conference

Conference	2016 Winter Simulation Conference, WSC 2016
Abbreviated title	WSC 2016
Country/Territory	United States
City	Arlington
Period	11/12/16 → 14/12/16
Internet address	http://informs-sim.org/wsc16papers/by_area.html

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10.1109/WSC.2016.7822270

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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). INFORMS Institute for Operations Research and the Management Sciences. https://doi.org/10.1109/WSC.2016.7822270

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

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title = "Stochastic simulation under input uncertainty for contract manufacturer selection in pharmaceutical industry",

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.",

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booktitle = "Proceedings of the 2016 Winter Simulation Conference",

publisher = "INFORMS Institute for Operations Research and the Management Sciences",

address = "United States",

note = "2016 Winter Simulation Conference, WSC 2016, WSC 2016 ; Conference date: 11-12-2016 Through 14-12-2016",

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Akçay, AE & Martagan, TG 2016, Stochastic simulation under input uncertainty for contract manufacturer selection in pharmaceutical industry. in TMK Roeder, PI Frazier, R Szechtman, E Zhou, T Huschka & SE Chick (eds), Proceedings of the 2016 Winter Simulation Conference. INFORMS Institute for Operations Research and the Management Sciences, Washington, D.C., pp. 2292-2303, 2016 Winter Simulation Conference, WSC 2016, Arlington, District of Columbia, United States, 11/12/16. https://doi.org/10.1109/WSC.2016.7822270

Stochastic simulation under input uncertainty for contract manufacturer selection in pharmaceutical industry. / Akçay, A.E.; Martagan, T.G.
Proceedings of the 2016 Winter Simulation Conference. ed. / T.M.K Roeder; P.I. Frazier; R. Szechtman; E. Zhou; T. Huschka; S.E. Chick. Washington, D.C.: INFORMS Institute for Operations Research and the Management Sciences, 2016. p. 2292-2303.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

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AU - Akçay, A.E.

AU - Martagan, T.G.

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N2 - 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.

AB - 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.

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DO - 10.1109/WSC.2016.7822270

M3 - Conference contribution

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BT - Proceedings of the 2016 Winter Simulation Conference

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A2 - Frazier, P.I.

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PB - INFORMS Institute for Operations Research and the Management Sciences

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Akçay AE , Martagan TG. Stochastic simulation under input uncertainty for contract manufacturer selection in pharmaceutical industry. In Roeder TMK, Frazier PI, Szechtman R, Zhou E, Huschka T, Chick SE, editors, Proceedings of the 2016 Winter Simulation Conference. Washington, D.C.: INFORMS Institute for Operations Research and the Management Sciences. 2016. p. 2292-2303 doi: 10.1109/WSC.2016.7822270

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

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