Quantifying a fair labor value for garment production through prediction of factory efficiency

Abstract

Li&Fung is a world leader in logistics, sourcing, and procurement; connecting manufacturing vendors with major retail brands. Recently, there has been an increasing consumer demand for more sustainably produced and sourced garments. When sourcing a garment, costs such as materials, shipping, and taxes are widely understood, but there is a lot of ambiguity on labor's contribution to the cost breakdown. This ambiguity can lead to incorrect garment pricing which can result in factories using cost cutting measures in order to meet production agreements. This in turn can lead to poor production quality as well as adverse conditions to the health and wellbeing of workers. Through advancements in technology and data collection it has become possible to digitize a large portion of the sourcing process, allowing for a quantifiable approach to costing out labor. The primary goal of this thesis is to detail a methodology to quantify a fair labor value for new orders placed with Li&Fung. A fair labor wage, in accordance with the UN International Labor Organization (ILO), will satisfy the basic needs of workers and their families while providing some discretionary income. The resulting framework was used to create a model that leverages Li&Fung's internal supplier data currently being collected, as well as external environmental data that drives garment production efficiency. The model provides a standardized, intuitive method for both Li&Fung merchandisers and their customers to determine what a fair labor value is when producing a garment. Additionally, Li&Fung can leverage the tool as a method to attract new brands who are interested in offering fair trade products to their customers, but do not have the capacity or supply chain expertise to do so.