Decoding the secret to faster drug production through simulation modeling

For many manufacturing facilities, process improvement efforts are a luxury when faced with heightened competitive pressures and a fast-paced work environment where fires are constantly being fought. This need for speed is even more important for startup companies who are racing against time to get their product to the market. Continuous improvement and Lean projects typically span from trial-and-error experiments to changes based on lengthy analyses. Biotech startup Company XYZ has felt the effects of these forces and launched a new effort to improve its operations via continuous improvement and Lean, ultimately reducing costs and improving productivity of operations. This thesis examines one example of a process improvement effort at Company XYZ's pre-clinical manufacturing facility. This project involved characterizing the cycle time and process flow, leading to targeted actions to increase the throughput and reduce the amount of time and effort to manufacture their drugs. Tools and ideas from Lean and Six Sigma were applied and a recommendation and next steps were presented to the company. This thesis also provides a broader demonstration of how such continuous improvement efforts can fit into the pharmaceutical and biotechnology industries.

Department dc:contributor.department

Massachusetts Institute of Technology. Supply Chain Management Program.

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2016

Author dc:creator

• Tsai, Mimi L

Advisor dc:contributor.advisor

• Bruce C. Arntzen.

• Supply Chain Management Program.
• Engineering Systems Division.

Statement dc:rights

• MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written 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/1721.1/107506

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/107506