MIT Libraries logoDSpace@MIT

MIT
View Item 
  • DSpace@MIT Home
  • MIT Libraries
  • MIT Theses
  • Graduate Theses
  • View Item
  • DSpace@MIT Home
  • MIT Libraries
  • MIT Theses
  • Graduate Theses
  • View Item
JavaScript is disabled for your browser. Some features of this site may not work without it.

Modeling drug substance purification manufacturing through schedule optimization and simulation

Author(s)
McIntire, Seth (Seth Cullen)
Thumbnail
DownloadFull printable version (5.299Mb)
Other Contributors
Leaders for Global Operations Program.
Advisor
Donald B. Rosenfield and Stanley Gershwin.
Terms of use
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. http://dspace.mit.edu/handle/1721.1/7582
Metadata
Show full item record
Abstract
This thesis develops a method by which overtime could be reduced in a highly variable drug substance purification manufacturing environment. Purification production overtime (20%) is a big cost driver at Building XX1 (BXX). Current production planning and labor resource evaluation methods at BXX Purification are manual, do not capture schedule delays, and do not adequately account for labor availability. Because of this, BXX is unable to accurately evaluate to what extent labor resource contributes to bottlenecking or how to improve overtime. A tool is devised in the Virtually Exhaustive Combinatorial System (VirtECS®) Scheduler software whereby purification production schedules are modeled and optimized. The model simulates production delays and the flow of production. Results lead to a more accurate understanding of how labor resource constrains the lot cycle time and where improvements in shift structure could be made to improve lot cycle time and variability of lot cycle time. The purification production schedules of two monoclonal antibodies (mAb) were modeled with the use of VirtECS® Scheduler. These two drug substances are selected to reflect the majority of BXX's mAb pipeline. The plant, BXX, produces a high mix of clinical and commercial launch drug substances, and is subject to a number of stochastic scheduling delays. Excel® is used to generate random sets of process times to simulate delays. These process times are fed into VirtECS®, a production schedule optimization tool, which then produces a simulated set of production schedules. Scheduling decisions of shift labor allocation and when manufacturing should start production during the week are simulated using the model. Results from this evaluation illustrate opportunities for BXX to improve overtime. Lot cycle time is found to be reduced by up to 5.9% based on model results by moving the start of production towards the end of the week and allocating more resources to the third shift from second shift. Additionally, cycle time variability, could be reduced by up to 22%. The model makes a number of assumptions which simplify purification operations whose effect should be further investigated. Future improvements for VirtECS® are proposed to better model BXX processes.
Description
Thesis: M.B.A., Massachusetts Institute of Technology, Sloan School of Management, in conjunction with the Leaders for Global Operations Program at MIT, 2017.
 
Thesis: S.M., Massachusetts Institute of Technology, Department of Mechanical Engineering, in conjunction with the Leaders for Global Operations Program at MIT, 2017.
 
Cataloged from PDF version of thesis.
 
Includes bibliographical references (pages 35-36).
 
Date issued
2017
URI
http://hdl.handle.net/1721.1/111489
Department
Leaders for Global Operations Program at MIT; Massachusetts Institute of Technology. Department of Mechanical Engineering; Sloan School of Management
Publisher
Massachusetts Institute of Technology
Keywords
Sloan School of Management., Mechanical Engineering., Leaders for Global Operations Program.

Collections
  • Graduate Theses

Browse

All of DSpaceCommunities & CollectionsBy Issue DateAuthorsTitlesSubjectsThis CollectionBy Issue DateAuthorsTitlesSubjects

My Account

Login

Statistics

OA StatisticsStatistics by CountryStatistics by Department
MIT Libraries
PrivacyPermissionsAccessibilityContact us
MIT
Content created by the MIT Libraries, CC BY-NC unless otherwise noted. Notify us about copyright concerns.