Development of operations based long range network capacity planning models

Planning for vaccines manufacturing capacity is both a complex task requiring many inputs and an important function of manufacturers to ensure the supply of vaccines that prevent life-threatening illnesses. This thesis explores the development of an operations based long range capacity planning model to facilitate the annual strategic capacity planning review at Novartis Vaccines. This model was developed in conjunction with process owners at Novartis Vaccines and utilizes operations principles, non-linear optimization, and process data to efficiently calculate the capacity of the vaccine manufacturing network. The resulting network capacity is then compared to the long range demand for vaccine production to determine capacity deficits and surpluses in the current manufacturing network as well as analyzing options for more efficient capacity usage. Although this model was developed specifically with respect to the Novartis Vaccines manufacturing network, the capacity calculation and gap analysis tools for single and multiproduct facilities as well as batch allocation for in multi-product, multi-facility networks are also applicable to other companies and industries that utilize batch processing. The model was validated utilizing process information from a production line that was already operating near capacity and showed a 95% agreement with the data from this line. Additionally, this operations based planning model was able to achieve buy-in from both process owners and the global strategy organization allowing it to be implemented in the planning cycle. Use of this tool enables efficiency and transparency in capacity analysis as well as the tools to examine the impact of a range of scenarios on the manufacturing network.

Department dc:contributor.department

Leaders for Global Operations Program at MIT

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2011

Author dc:creator

• Wilson, Cynthia M. (Cynthia Marie)

Advisor dc:contributor.advisor

• Stephen Graves and Charles Cooney.

• Sloan School of Management.
• Chemical Engineering.
• Leaders for Global Operations Program.

Statement 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.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

http://hdl.handle.net/1721.1/66039

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/66039