Research at FAST

Institute of Textiles & Clothing Institute of Textiles & Clothing 159 Qualification BCom (Concordia) PhD (PolyU) ORCID ID 0000-0002-0145-7948 Dr LO Kwan Yu Chris Associate Professor Research Overview Dr Chris K. Y. Lo is a one of the leading scholars on sustainable operations and innovations. His publication appeared in top-tier journals, such as Organization Sciences, Manufacturing & Service Operations Management, Journal of Operations Management, Decision Science, International Journal of Production Economics, Journal of Business Ethics, Journal of Business Research, Information and Management, etc. His research works were mentioned by international press, such as the Guardian, UCLA Anderson Review, The Sydney Morning Herald and the Brisbane Times. One of his paper was selected as the winner of Responsible Research in Management Award co-sponsored by Responsible Research in Business and Management and International Association for Chinese Management Research (The largest research community by Chinese business and management scholars). Dr Lo also bring his researches into practices through industry knowledge transfer. For example, he recently develops augmented reality (AR) system for warehouse management and quality control for yarn manufacturing to manage their fully automated warehouse using AR systems. He commercialized his research quick response quality management system for small and medium size garment factories to adopt advance quality management and inspection tools without huge investment. The system has been adopted by factories in China, and Vietnam, bringing practical impacts to the fashion and textiles supply chain. Receiver operating charateristics (ROC) curve of the prediction for the envionrmental violation prediction model, using random‐select 80% of the data to predict the remaining 20% of violations records. The area under ROC curve is 0.815. In a recent joint work with Monash University (Dr Paul Zhou) and UCLA (Prof. Christopher Tang), Dr Lo’s research team developed a prediction model that can out‐perform the random inspection approach of firms’ environmental violation in China. The model exposes over 71% of the violations of the following year by inspecting only 21.5% of the firms with risk scores above the top 80 percentile.