Modeling drug substance purification manufacturing through schedule optimization and simulation
Name
1003322545-MIT.pdf
Description
Full printable version
Size
5.3 MB
Format
Adobe PDF
Checksum (MD5)
25b40c4e60f537832597e9f45238ac72
Author(s)
McIntire, Seth (Seth Cullen)
Advisor(s)
Donald B. Rosenfield and Stanley Gershwin.
Date Issued
2017
Publisher
Massachusetts Institute of Technology
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).
Subjects
Sloan School of Management.
Mechanical Engineering.
Leaders for Global Operations Program.
MIT Department
Leaders for Global Operations Program at MIT
Massachusetts Institute of Technology. Department of Mechanical Engineering
Sloan School of Management
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