2026-09-18T22:58:55Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/1226022021-07-05T14:03:20Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Roy Welsch and Patrick Henry Winston.
Goldberg, DavidM.B.A.Sloan School of Management.
Sloan School of Management.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.
Leaders for Global Operations Program.
Sloan School of Management
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Leaders for Global Operations Program
2019-10-11T22:25:30Z
2019-10-11T22:25:30Z
2019
2019
2019
https://hdl.handle.net/1721.1/122602
1119537956
Thesis: M.B.A., Massachusetts Institute of Technology, Sloan School of Management, 2019, In conjunction with the Leaders for Global Operations Program at MIT
Thesis: S.M., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2019, In conjunction with the Leaders for Global Operations Program at MIT
Page 75 blank. Cataloged from PDF version of thesis.
Includes bibliographical references (page 67).
This project focuses on the creation of a novel tool to detect and flag potential errors within Amgen's capacity management forecast data, in an automated manner using statistical analysis, artificial intelligence and machine learning. User interaction allows the tool to learn from experience, improving over time. While the tool created here focuses on a specific set of Amgen's data, the framework, approach and techniques offered herein can more broadly be applied to detect anomalies and errors in other sets of data from across industries and functions. By detecting errors in Amgen's data, the tool improves data robustness and forecasts, which drive decisions, actions and ultimately results. Flagging and correcting this data allows for overcoming errors, which would otherwise damage the accurate allocation of Amgen's human resources to activities in the drug pipeline, ultimately hampering Amgen's ability to develop drugs for patients efficiently. A user interface (UI) dashboard evaluates the tool's performance, tracking the number of errors correctly identified, the accuracy rate, and the estimated business impact. To date the tool has identified 893 corrected errors with a 99.2% accuracy rate and an estimated business impact of $77.798M optimized resources. Using the paradigm of intelligent augmentation (IA), this tool empowers employees by focusing their attention and saving them time. The tool handles the human-impossible task of sifting through thousands of lines and hundreds of thousands of data points. The human user then makes decisions and takes action based on the tool provided output.
by David Goldberg.
M.B.A.
S.M.
M.B.A. Massachusetts Institute of Technology, Sloan School of Management
S.M. Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science
75 pages
eng
Massachusetts Institute of Technology
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
Sloan School of Management.
Electrical Engineering and Computer Science.
Leaders for Global Operations Program.
Improving project timelines using Al / ML to detect forecasting errors
Improving project timelines using artificial intelligence/ machine learning to detect forecasting errors
Thesis
application/pdf
2019-10-11T22:25:29Z
Publication
Master
Sloan
EECS
unknown
unknown
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<Abstract>This project focuses on the creation of a novel tool to detect and flag potential errors within Amgen's capacity management forecast data, in an automated manner using statistical analysis, artificial intelligence and machine learning. User interaction allows the tool to learn from experience, improving over time. While the tool created here focuses on a specific set of Amgen's data, the framework, approach and techniques offered herein can more broadly be applied to detect anomalies and errors in other sets of data from across industries and functions. By detecting errors in Amgen's data, the tool improves data robustness and forecasts, which drive decisions, actions and ultimately results. Flagging and correcting this data allows for overcoming errors, which would otherwise damage the accurate allocation of Amgen's human resources to activities in the drug pipeline, ultimately hampering Amgen's ability to develop drugs for patients efficiently. A user interface (UI) dashboard evaluates the tool's performance, tracking the number of errors correctly identified, the accuracy rate, and the estimated business impact. To date the tool has identified 893 corrected errors with a 99.2% accuracy rate and an estimated business impact of $77.798M optimized resources. Using the paradigm of intelligent augmentation (IA), this tool empowers employees by focusing their attention and saving them time. The tool handles the human-impossible task of sifting through thousands of lines and hundreds of thousands of data points. The human user then makes decisions and takes action based on the tool provided output.</Abstract>
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