Strategic capacity planning using data science, optimization, and machine learning
Name
1191623969-MIT.pdf
Size
4.62 MB
Format
Adobe PDF
Checksum (MD5)
c0b08c6c3d80757926fda55037269118
Author(s)
Nowak, Hans,II(Hans Antoon)
Advisor(s)
Roy Welsch and Daniel Frey.
Date Issued
2020
Publisher
Massachusetts Institute of Technology
Abstract
Raytheon's Circuit Card Assembly (CCA) factory in Andover, MA is Raytheon's largest factory and the largest Department of Defense (DOD) CCA manufacturer in the world. With over 500 operations, it manufactures over 7000 unique parts with a high degree of complexity and varying levels of demand. Recently, the factory has seen an increase in demand, making the ability to continuously analyze factory capacity and strategically plan for future operations much needed. This study seeks to develop a sustainable strategic capacity optimization model and capacity visualization tool that integrates demand data with historical manufacturing data. Through automated data mining algorithms of factory data sources, capacity utilization and overall equipment effectiveness (OEE) for factory operations are evaluated. Machine learning methods are then assessed to gain an accurate estimate of cycle time (CT) throughout the factory. Finally, a mixed-integer nonlinear program (MINLP) integrates the capacity utilization framework and machine learning predictions to compute the optimal strategic capacity planning decisions. Capacity utilization and OEE models are shown to be able to be generated through automated data mining algorithms. Machine learning models are shown to have a mean average error (MAE) of 1.55 on predictions for new data, which is 76.3% lower than the current CT prediction error. Finally, the MINLP is solved to optimality within a tolerance of 1.00e-04 and generates resource and production decisions that can be acted upon.
Description
Thesis: M.B.A., Massachusetts Institute of Technology, Sloan School of Management, in conjunction with the Leaders for Global Operations Program at MIT, May, 2020
Thesis: S.M., Massachusetts Institute of Technology, Department of Mechanical Engineering, in conjunction with the Leaders for Global Operations Program at MIT, May, 2020
Cataloged from the official PDF of thesis.
Includes bibliographical references (pages 101-104).
Subjects
Sloan School of Management.
Mechanical Engineering.
Leaders for Global Operations Program.
MIT Department
Sloan School of Management
Massachusetts Institute of Technology. Department of Mechanical Engineering
Leaders for Global Operations Program
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