A predictive approach for identifying high performance factories
Name
921152996-MIT.pdf
Description
Full printable version
Size
5.03 MB
Format
Adobe PDF
Checksum (MD5)
fd6e16fc15c403d30ceca004212a9fa0
Author(s)
Chan, Albert T. (Albert Tak Chun)
Advisor(s)
Charles H. Fine and David Simchi-Levi.
Date Issued
2015
Publisher
Massachusetts Institute of Technology
Abstract
Li and Fung, the world's largest apparel sourcing company, is facing rapid changes as customers demand lower prices and faster development cycles. To support the transformation of the supply chain, data analytics is used to explore leading indicators for firm survival in the garment industry. This project seeks to identify the major drivers of factory success through the lens of current factory performance metrics (quality, delivery, and compliance) and through a qualitative survey distributed to factories in China, Bangladesh, and Turkey. Based on modeled historical trends, we find that current factory metrics vary significantly in their ability to signal long-term performance. Whereas on-time delivery is universally correlated with factory success, compliance is not. Furthermore, we find that there may be secondary indicators that are strongly associated with high performance factories, including technical audit scores. These insights on the underlying drivers of high performance will increase internal transparency and enable improved data-driven strategic sourcing decisions. It is recommended that supply chain companies continue to explore these themes with data analytics. By proactively identifying high performance factories, the project enable transparent and sustainable supply chains, giving companies a powerful long-term competitive advantage.
Description
Thesis: M.B.A., Massachusetts Institute of Technology, Sloan School of Management, 2015. In conjunction with the Leaders for Global Operations Program at MIT.
Thesis: S.M., Massachusetts Institute of Technology, Engineering Systems Division, 2015. In conjunction with the Leaders for Global Operations Program at MIT.
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 54-55).
Subjects
Sloan School of Management.
Engineering Systems Division.
Leaders for Global Operations Program.
MIT Department
Leaders for Global Operations Program at MIT
Massachusetts Institute of Technology. Engineering Systems Division
Sloan School of Management
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