Using predictive analytics to address risk in complex supply chains

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.

Department dc:contributor.department

Leaders for Global Operations Program at MIT

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2018

Author dc:creator

• Schmidt, Rachel Marie, S.M. Sloan School of Management

Advisor dc:contributor.advisor

• Maria Yang, Charles Fine, and Roy Welsch.

• Sloan School of Management.
• Mechanical Engineering.
• Leaders for Global Operations Program.

Statement dc:rights

• 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.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

http://hdl.handle.net/1721.1/117964

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/117964