Thesis (M.B.A.)--Massachusetts Institute of Technology, Sloan School of Management; and, (S.M.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering; in conjunction with the Leaders for Global Operations Program at MIT, 2011. / Cataloged from PDF version of thesis. / Includes bibliographical references (p. 75-76). / The Novartis BioPharmOps division is responsible for manufacturing large molecule products, including monoclonal antibodies, for late stage clinical trials and commercial sales. The BioPharmOps site in Huningue, France is expanding their product line but is also trying to reduce costs; cost pressures are increasing as biotech products become a larger part of Novartis' pipeline. The site uses a standard cost method to calculate their product costs. However, when using standard costs it can be time-consuming to extrapolate and predict costs when inputs and assumptions (such as product mix or process parameters) are changed. This project describes development of a model that allows the factory to quickly and easily simulate new product mixes and process flows. This model provides the site with a different view of their costs that will help them understand their cost drivers more completely and thereby help enable strategic decision-making at the site. A model of this type can be used to provide unexpected insights but the data in it are not meant to stand alone. By using results from a cost model like this along with operational metrics like throughput time or changeover time, a site should be able to quickly predict the cost impact of process changes or changes in the production plan. / by Christina M. Simpson. / S.M. / M.B.A.
| Identifer | oai:union.ndltd.org:MIT/oai:dspace.mit.edu:1721.1/66050 |
| Date | January 2011 |
| Creators | Simpson, Christina M. (Christina Margaret) |
| Contributors | Charles L. Cooney and Roy Welsch., Leaders for Global Operations Program., Leaders for Global Operations Program at MIT, Massachusetts Institute of Technology. Department of Mechanical Engineering, Sloan School of Management |
| Publisher | Massachusetts Institute of Technology |
| Source Sets | M.I.T. Theses and Dissertation |
| Language | English |
| Detected Language | English |
| Type | Thesis |
| Format | 76 p., application/pdf |
| 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., http://dspace.mit.edu/handle/1721.1/7582 |
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