Integrating Machine Learning, Modularity and Supply Chain Integration for Branding 4.0

While brands use technologies in various ways to improve their performance, they appear to struggle with achieving Branding 4.0 standards. This new generation of brand development has brought an era of hyper-customized experiences to benefit brand performance. With the Branding 4.0 literature still in its infancy, questions remain regarding how brands can maintain their identity while delivering a hyper-personalized customer experience. This study draws on mass customization, artificial intelligence, and supply chain management literature to investigate how three core organizational capabilities and resources—machine learning, modularity, and supply chain integration—helpful in achieving production flexibility could jointly enable companies to transition to and maintain a Branding 4.0 philosophy through more efficient personalization of their product offerings. This paper reports findings from 15 in-depth interviews with top executives from brands, including some Fortune Global 500 companies, in China's garment and footwear industries to provide insights into Branding 4.0 and the possible contribution of machine learning, modularity application, and supply chain integration. Our findings inform a two-tier response strategy and a three-dimensional analytical framework which provide a theoretical basis for operationalizing Branding 4.0 and exploring, through a resource orchestration lens, how brands can respond to the related adoption challenges. Specifically, our findings show how machine learning's data analysis, knowledge conversion, and transmission capabilities could benefit both modular management and supply chain tasks to optimize product co-design processes and timely responses to customers' changing demands.