{"id":{"repo_id":"mit","oai_identifier":"oai:dspace.mit.edu:1721.1/59187"},"canonical_url":"https://search.dev.ndltd.org/etd/mit/oai:dspace.mit.edu:1721.1/59187","repository":{"repo_id":"mit","name":"MIT","base_url":"https://dspace.mit.edu/oai/request"},"display":{"title":"Forecasting resource requirements for drug development long range planning","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_html":"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_has_math":false,"creators":["Thedinga, Angela (Angela Marie)"],"institution":"Massachusetts Institute of Technology","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":"Leaders for Global Operations Program at MIT","school":null,"contributors":[],"advisors":["Ernst R. Berndt and Charles L. Cooney."],"committee_chairs":[],"committee_members":[],"year":2010,"date_issued":"2010","date_published":"2010","updated_at":"2026-07-22T22:21:37Z","subjects":["Sloan School of Management.","Chemical Engineering.","Leaders for Global Operations Program."],"languages":["eng"],"rights":["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."],"rights_urls":["http://dspace.mit.edu/handle/1721.1/7582"],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/1721.1/59187","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Ernst R. Berndt and Charles L. Cooney."]},{"key":"dc:contributor.department","label":"Department","values":["Leaders for Global Operations Program at MIT","Massachusetts Institute of Technology. Department of Chemical Engineering","Sloan School of Management"]},{"key":"dc:contributor.other","label":"Dc Contributor Other","values":["Leaders for Global Operations Program."]},{"key":"dc:creator","label":"Author","values":["Thedinga, Angela (Angela Marie)"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2010-10-12T18:10:39Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2010-10-12T18:10:39Z"]},{"key":"dc:date.issued","label":"Date","values":["2010"]},{"key":"dc:publisher","label":"Institution","values":["Massachusetts Institute of Technology"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Sloan School of Management.","Chemical Engineering.","Leaders for Global Operations Program."]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["eng"]},{"key":"dc:rights","label":"Dc Rights","values":["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."]},{"key":"dc:rights.uri","label":"Rights URI","values":["http://dspace.mit.edu/handle/1721.1/7582"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["http://hdl.handle.net/1721.1/59187"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["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)."]},{"key":"dc:description.abstract","label":"Abstract","values":["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."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["S.M.","M.B.A."]},{"key":"dc:title","label":"Title","values":["Forecasting resource requirements for drug development long range planning"]}]}],"canonical_facts":{"dc:contributor.advisor":["Ernst R. Berndt and Charles L. Cooney."],"dc:contributor.department":["Leaders for Global Operations Program at MIT","Massachusetts Institute of Technology. Department of Chemical Engineering","Sloan School of Management"],"dc:contributor.other":["Leaders for Global Operations Program."],"dc:creator":["Thedinga, Angela (Angela Marie)"],"dc:date.accessioned":["2010-10-12T18:10:39Z"],"dc:date.available":["2010-10-12T18:10:39Z"],"dc:date.issued":["2010"],"dc:description":["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)."],"dc:description.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."],"dc:description.degree":["S.M.","M.B.A."],"dc:identifier.uri":["http://hdl.handle.net/1721.1/59187"],"dc:language.iso":["eng"],"dc:publisher":["Massachusetts Institute of Technology"],"dc:rights":["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."],"dc:rights.uri":["http://dspace.mit.edu/handle/1721.1/7582"],"dc:subject":["Sloan School of Management.","Chemical Engineering.","Leaders for Global Operations Program."],"dc:title":["Forecasting resource requirements for drug development long range planning"],"dc:type":["Thesis"]},"updated_at":"2026-07-22T22:21:37Z"}