Using factory-level digital tools to improve quality and productivity in garment factories

• Main Author: Hsu, Kevin(Kevin Ta-Zhi)
• Other Authors: Maria Yang and Charles Fine.
• Format: Others
• Language: English
• Published: Massachusetts Institute of Technology 2019
• Subjects: Sloan School of Management.; Mechanical Engineering.; Leaders for Global Operations Program.
• Online Access: https://hdl.handle.net/1721.1/122590

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 Mechanical Engineering, 2019, In conjunction with the Leaders for Global Operations Program at MIT === Cataloged from PDF version of thesis. === Includes bibliographical references (pages 66-67). === The retail landscape is rapidly changing as evolving consumer habits are resulting in smaller batch quantities and shorter lead times, requiring Li & Fung to have a more digitally connected, nimble vendor base. Li & Fung uses a supplier network of thousands of garment factories around the world, the majority of whom are still capturing quality and production data manually, resulting in incomplete and inaccurate records. Factories see the value in making their operations digital, but most are low margin businesses that do not have the capital to make significant investments. This project is focused on the development of a cost-effective, digital tool to capture quality and production data at the end of a production line. This new tool will: -- Allow managers to quickly access real-time data analytics on their factory, -- Enable factories to make immediate root cause corrections in the sewing line, -- Serve as a gateway for Li & Fung to more proactively manage its vendor base,-- === Give Li & Fung visibility to eliminate unnecessary inspection activities and reduce costs. The project began with an initial hardware prototype created in 2017 that evolved into the Phase One version of a mobile application which was delivered in early 2018. User testing was performed in three factories in India and Malaysia, where feedback was incorporated into a comprehensive redesign in Phase Two. The thesis will detail the needs and challenges from both the factory and Li & Fung viewpoints, and how this digital tool seeks to address them. For garment factories, the tool is cost-effective and simple, enabling factories to become digital in a very accessible way. The tool introduces garment factories to technology and Internet of Things without over-complicating their operations. === For Li & Fung, the tool provides much-needed insights into the actual performance of the vendor base, allowing Li & Fung to achieve many of its strategic initiatives related to inspection cost reduction, vendor selection, and production tracking. === by Kevin Hsu. === M.B.A. === S.M. === M.B.A. Massachusetts Institute of Technology, Sloan School of Management === S.M. Massachusetts Institute of Technology, Department of Mechanical Engineering