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Massachusetts Institute of Technology

A data-driven approach to vendor rationalization and engagement for sustainable supply chains

Abstract

dc:description.abstract

Demands for shorter lead times and smaller order quantities, a greater emphasis on sustainable sourcing, and better management of supply chain risks are challenges Li & Fung is addressing by reshaping its relationship with its vendor base. The current work seeks to develop a data-driven approach for vendor base rationalization and vendor engagement as part of a larger initiative within the company to move from transactional vendor relationships to ones of greater collaboration and support. The primary contribution of this project is to provide Li & Fung with a rationalization and engagement methodology that leverages vendor performance and capability data collected by Li & Fung, as well as the author's own on-site observations of vendors, to address three main topic areas: vendor evaluation, vendor selection, and vendor engagement. 1. Vendor evaluation addresses the question of how Li & Fung measures the performance of vendors. This is an important aspect of vendor rationalization because the performance parameters used to evaluate the vendors are the behaviors that are promoted. A balanced scorecard taking into account a variety of performance considerations is presented as the tool to evaluate vendor performance. 2. Vendor selection addresses the question of how Li & Fung decides which vendors to continue to do business with and to what extent. These are essential questions to answer because the strength of the supply chain depends on the strength of the links in that chain. Two data streams providing a holistic picture of a vendor's historical performance, production capacity, production capabilities, and engagement level are used to select the right mix of vendors fit for the business's needs. 3. Vendor engagement addresses the question of how to build vendor relationships in a way that provides mutual incentive and benefits in improving performance and profitability over time. Presented as the foundation for this relationship is a vendor engagement package, which includes an objective set of performance data to monitor the vendor over time. It is through this vendor engagement package that Li & Fung exercises its influence to commit vendors to improvement plans aligned with business goals. The short-term accomplishment of the work was to successfully implement the rationalization methodology on a pilot product category within an operating group to reduce the vendor base from 39 to 19 and to identify three vendors for strategic partnerships. The long-term accomplishment of the work was to provide a robust vendor rationalization and engagement methodology that can be improved upon over time and applied across the remainder of the product categories within the operating group.

Degree

thesis:*
Department dc:contributor.department
Leaders for Global Operations Program at MIT
Grantor dc:publisher
Massachusetts Institute of Technology
Year dc:date.issued
2016

Author and committee

dc:creator, dc:contributor.*
Author dc:creator
  • Prout, Jonathan (Jonathan Paul)
Advisor dc:contributor.advisor
  • Charles Fine and David Simchi-Levi.

Subjects

dc:subject × 4

Rights

dc:rights
Statement dc:rights
  • M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.
Language dc:language.iso
eng

Identifiers

dc:identifier.*
Handle dc:identifier.uri
http://hdl.handle.net/1721.1/105630
OAI identifier oai:identifier
oai:dspace.mit.edu:1721.1/105630

Chain of custody

source
Harvested from
MIT
Base URL
dspace.mit.edu/oai/request
Last updated
2026-07-22
Source record
OAI-PMH GetRecord
citation

Prout, Jonathan (Jonathan Paul). A data-driven approach to vendor rationalization and engagement for sustainable supply chains. Massachusetts Institute of Technology, 2016. http://hdl.handle.net/1721.1/105630