A predictive approach for identifying high performance factories

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.