2026-09-19T17:22:03Zhttps://dspace.mit.edu/server/oai/request

oai:dspace.mit.edu:1721.1/395952022-01-28T21:21:19Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023

Roy E. Welsch and Gregory J. McRae. Heiney, John P. (John Patrick) Leaders for Manufacturing Program. Leaders for Manufacturing Program at MIT Massachusetts Institute of Technology. Department of Chemical Engineering Sloan School of Management 2007-11-16T14:30:27Z 2007-11-16T14:30:27Z 2007 2007 http://hdl.handle.net/1721.1/39595 176074993 Thesis (M.B.A.)--Massachusetts Institute of Technology, Sloan School of Management; and, (S.M.)--Massachusetts Institute of Technology, Dept. of Chemical Engineering; in conjunction with the Leaders for Manufacturing Program at MIT, 2007. Includes bibliographical references (p. 55-56). In early-stage drug discovery, thousands of compounds must be tested using in vitro assays to determine their exposure and safety characteristics. This data is used to guide the selection of potential drug candidates and to help chemists in optimize the properties of those compounds. At Novartis, an internal service organization called Preclinical Compound Profiling (PCP) provides these services to the company as a whole. The purpose of this internship was to help PCP make significant improvements in cycle time and cost effectiveness without reducing the quality of information provided to their customers. The project utilized a series of deterministic and stochastic models to predict the impact of multiple operational changes on cost and cycle time. The data from each model was synthesized to create a unified view allowing combinations of changes to be analyzed together. This data was evaluated in the context of the customer needs and organizational strategy to present recommendations. Changes were implemented that will reduce materials spending by $500,000 per year while simultaneously increasing capacity, reducing cycle time, and improving customer value. Additional recommendations were developed that will enable further improvements. by John P. Heiney. S.M. M.B.A. 60 p. eng Massachusetts Institute of Technology M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission. http://dspace.mit.edu/handle/1721.1/7582 Sloan School of Management. Chemical Engineering. Leaders for Manufacturing Program. Optimization of preclinical profiling operations in drug discovery Thesis application/pdf Publication unknown unknown <Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="745ac0a6-4dca-4b9a-8f7f-a68bade8a198"> <Type xmlns="https://www.openaire.eu/cerif-profile/vocab/COAR_Publication_Types">http://purl.org/coar/resource_type/c_1843</Type> <Language>eng</Language> <Title>Optimization of preclinical profiling operations in drug discovery</Title> <PublishedIn> <Publication> </Publication> </PublishedIn> <PublicationDate>2007</PublicationDate> <Authors> <Author> <DisplayName>Heiney, John P. (John Patrick)</DisplayName> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> </Authors> <Editors> </Editors> <Publishers> <Publisher> <DisplayName>Massachusetts Institute of Technology</DisplayName> <OrgUnit /> </Publisher> </Publishers> <License>http://dspace.mit.edu/handle/1721.1/7582</License> <Keyword>Sloan School of Management.</Keyword> <Keyword>Chemical Engineering.</Keyword> <Keyword>Leaders for Manufacturing Program.</Keyword> <Abstract>In early-stage drug discovery, thousands of compounds must be tested using in vitro assays to determine their exposure and safety characteristics. This data is used to guide the selection of potential drug candidates and to help chemists in optimize the properties of those compounds. At Novartis, an internal service organization called Preclinical Compound Profiling (PCP) provides these services to the company as a whole. The purpose of this internship was to help PCP make significant improvements in cycle time and cost effectiveness without reducing the quality of information provided to their customers. The project utilized a series of deterministic and stochastic models to predict the impact of multiple operational changes on cost and cycle time. The data from each model was synthesized to create a unified view allowing combinations of changes to be analyzed together. This data was evaluated in the context of the customer needs and organizational strategy to present recommendations. Changes were implemented that will reduce materials spending by $500,000 per year while simultaneously increasing capacity, reducing cycle time, and improving customer value. Additional recommendations were developed that will enable further improvements.</Abstract> <Access xmlns="http://purl.org/coar/access_right" > </Access> </Publication>