2026-09-18T23:59:39Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/1179642022-01-27T21:23:20Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Maria Yang, Charles Fine, and Roy Welsch.
Schmidt, Rachel Marie, S.M. Sloan School of Management
Leaders for Global Operations Program.
Leaders for Global Operations Program at MIT
Massachusetts Institute of Technology. Department of Mechanical Engineering
Sloan School of Management
2018-09-17T15:51:47Z
2018-09-17T15:51:47Z
2018
2018
http://hdl.handle.net/1721.1/117964
1051237674
Thesis: M.B.A., Massachusetts Institute of Technology, Sloan School of Management, in conjunction with the Leaders for Global Operations Program at MIT, 2018.
Thesis: S.M., Massachusetts Institute of Technology, Department of Mechanical Engineering, in conjunction with the Leaders for Global Operations Program at MIT, 2018.
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 68-69).
Li & Fung (LF) is a global supply chain manager for consumer product brands and retailers. Worldwide, LF contracts with over 13,000 factories. Frequently, these factories experience incidents, which are internally defined as "unplanned / unwanted events which have the potential to escalate or have already caused damage to stakeholders within the supply chain." In the factory context, this includes fires, labor strikes, and unauthorized subcontracting events, among others. Every incident costs the factories, LF, and the customers extensive time and resources to mitigate and recover from. Currently, LF manages incidents as they occur. Moving forward, LF strives to proactively mitigate risk by forecasting the probability that each factory in the supply chain will experience an incident. In addition to avoiding potential factory worker injuries, predicting risk will: (1) save LF time (and money) by being alert to incidents before they occur, (2) protect the LF reputation and maintain trust, and (3) demonstrate how LF is using advanced analytics to build a better supply chain. This project includes three primary components. First, an assessment to evaluate the impact of incidents on LF was performed, by investigating several case studies of different incident types in different regions of the world. Second, a predictive analytics model to forecast the probability that each factory will have an incident was developed, using historical internal and external data sources. The results are presented quantitatively and visually to provide clear and effective messaging and recommendations to LF management. Insights and challenges are outlined in detail to provide a thorough understanding of the model and recommend future alterations. Finally, the team developed short term and long term action plans to drive responsible sourcing decisions using the available data and initiate industry change.
by Rachel Marie Schmidt.
M.B.A.
S.M.
78, 14, [2] 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.
Mechanical Engineering.
Leaders for Global Operations Program.
Using predictive analytics to address risk in complex supply chains
Thesis
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Publication
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<Abstract>Li & Fung (LF) is a global supply chain manager for consumer product brands and retailers. Worldwide, LF contracts with over 13,000 factories. Frequently, these factories experience incidents, which are internally defined as "unplanned / unwanted events which have the potential to escalate or have already caused damage to stakeholders within the supply chain." In the factory context, this includes fires, labor strikes, and unauthorized subcontracting events, among others. Every incident costs the factories, LF, and the customers extensive time and resources to mitigate and recover from. Currently, LF manages incidents as they occur. Moving forward, LF strives to proactively mitigate risk by forecasting the probability that each factory in the supply chain will experience an incident. In addition to avoiding potential factory worker injuries, predicting risk will: (1) save LF time (and money) by being alert to incidents before they occur, (2) protect the LF reputation and maintain trust, and (3) demonstrate how LF is using advanced analytics to build a better supply chain. This project includes three primary components. First, an assessment to evaluate the impact of incidents on LF was performed, by investigating several case studies of different incident types in different regions of the world. Second, a predictive analytics model to forecast the probability that each factory will have an incident was developed, using historical internal and external data sources. The results are presented quantitatively and visually to provide clear and effective messaging and recommendations to LF management. Insights and challenges are outlined in detail to provide a thorough understanding of the model and recommend future alterations. Finally, the team developed short term and long term action plans to drive responsible sourcing decisions using the available data and initiate industry change.</Abstract>
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