Forecasting quality in robotic controller supply chain
Name
1011505443-MIT.pdf
Description
Full printable version
Size
11.17 MB
Format
Adobe PDF
Checksum (MD5)
eb06506d09797a049cdacc5e5149d147
Author(s)
Patel, Sonny
Advisor(s)
Roy Welsch and Kamal Youcef-Toumi.
Date Issued
2017
Publisher
Massachusetts Institute of Technology
Abstract
Between 2017 and 2019, industrial robot installations are estimated to increase by 13% on average per year. As the industrial robot market has grown, so too have customer demands. Many industrial robot manufacturers are in a position to capture this growth by improving in on-time delivery, quality performance, and product offerings. This master's thesis is devoted to providing manufacturers methods for increasing quality performance for robotic controllers. To improve quality performance, we focus on finding the connection between controller quality performance at suppliers, manufacturing, and customer sites. We consider this valuable in the context of a manufacturer's R&D to set operational quality targets and predict the cascade effect in the supply chain. This study includes a deep dive in quality performance and methods to predict future performance. The analysis includes a look at quality metrics in the robot industry. We forecast future performance against these metrics using an available dataset and regression modeling. Because we do not discover strong regression models, we propose dataset statistics to forecast future quality performance. We have 2 recommendations based on our research. Moving forward, we recommend increasing transparency for quality data collection to create a more robust model with stronger prediction capabilities. We also recommend a total cost of quality approach in determining ideal quality metrics.
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 53-54).
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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