2026-09-19T07:26:23Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/591872022-01-27T21:55:01Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Ernst R. Berndt and Charles L. Cooney.
Thedinga, Angela (Angela Marie)
Leaders for Global Operations Program.
Leaders for Global Operations Program at MIT
Massachusetts Institute of Technology. Department of Chemical Engineering
Sloan School of Management
2010-10-12T18:10:39Z
2010-10-12T18:10:39Z
2010
http://hdl.handle.net/1721.1/59187
659832484
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, 2010.
"June 2010." Cataloged from PDF version of thesis.
Includes bibliographical references (p. 64).
This thesis investigates the use of a task-based Monte Carlo simulation model to forecast headcount and manufacturing capacity requirements for a drug development organization. A pharmaceutical drug development group is responsible for designing the manufacturing process for new potential drug products, testing the product quality, and supplying product for clinical trials. The drug development process is complex and uncertain. The speed to market is critical to a company's success. Therefore, it is important to have an adequate number of employees and available manufacturing capacity to support timely and efficient drug development. The employees and manufacturing capacity can either be supplied internally or externally, through contract manufacturing organizations. This thesis formulates and empirically evaluates a simulation model designed using the Novartis Biologics drug development process and is adaptable to other pharmaceutical organization. The model demonstrates 7% accuracy when compared with historical data, and estimates within 13% of the currently accepted manufacturing capacity forecasting tool. Additionally, three case studies are included to demonstrate how the model can be used to evaluate strategic decisions. The case studies include: a drug development process improvement evaluation, an outsourcing evaluation, and an "at risk" development evaluation.
by Angela Thedinga.
S.M.
M.B.A.
70 p.
eng
Massachusetts Institute of Technology
M.I.T. theses are protected by
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http://dspace.mit.edu/handle/1721.1/7582
Sloan School of Management.
Chemical Engineering.
Leaders for Global Operations Program.
Forecasting resource requirements for drug development long range planning
Thesis
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Publication
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<Abstract>This thesis investigates the use of a task-based Monte Carlo simulation model to forecast headcount and manufacturing capacity requirements for a drug development organization. A pharmaceutical drug development group is responsible for designing the manufacturing process for new potential drug products, testing the product quality, and supplying product for clinical trials. The drug development process is complex and uncertain. The speed to market is critical to a company's success. Therefore, it is important to have an adequate number of employees and available manufacturing capacity to support timely and efficient drug development. The employees and manufacturing capacity can either be supplied internally or externally, through contract manufacturing organizations. This thesis formulates and empirically evaluates a simulation model designed using the Novartis Biologics drug development process and is adaptable to other pharmaceutical organization. The model demonstrates 7% accuracy when compared with historical data, and estimates within 13% of the currently accepted manufacturing capacity forecasting tool. Additionally, three case studies are included to demonstrate how the model can be used to evaluate strategic decisions. The case studies include: a drug development process improvement evaluation, an outsourcing evaluation, and an "at risk" development evaluation.</Abstract>
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