Leveraging smart system design to collect and analyze factory production data

Li & Fung deals with many factories that are very geographically dispersed. These facilities generally do not have the capital available to invest in new technologies and processes, and the extremely manual nature of garment fabrication is the standard as a result. As customers continue to demand quicker product turn-arounds and higher levels of customization, factories need to better understand their current process limitations in an effort to optimize their internal operations. Since most of these factories collect virtually no process data, managers have a hard time focusing on areas in which to improve. This project is approaching the question of "how can we use technology in a responsible and sustainable way to better understand our process?" from the perspective of a factory manager, who cannot necessarily invest in sophisticated software and hardware systems that other industries have adopted to monitor quality. As a result, this project focuses heavily on the user experience of both the operator (quality inspector) and the manager, as both need to be able to interact with the proposed data system easily and reliably. The primary goal of this thesis is to detail the design and implementation of a data collection platform (built during internship) for use in low-tech garment factories that will: -- Enable the procurement of process data (specifically as it relates to quality) from operators in real-time. -- Allow factory management to easily view and analyze collected data. -- Employ an intuitive front-end user interface that allows operators to quickly and reliably collect data. Since a substantial portion of this internship was spent designing, building, and testing this data collection interface, the thesis will reflect the nuances associated with building and implementing factory data systems in low-tech factories where human interaction is the primary driver of system adoption. The design and deployment of this system was ultimately successful and resulted in a robust prototype that continues to provide Li & Fung with insights into how to achieve their ultimate goal of connecting their factory network to a centralized data platform.

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

• Jennings, Brandon Douglas

Advisor dc:contributor.advisor

• Maria Yang and Charles Fine.

• 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/1721.1/117969

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/117969