Exploring the supply chain management of fair trade business:Case study of a fair trade craft company in China

For two decades, fair trade has served as an alternative approach of trading that encourages minimal returns, sustainability, and ethics, by offering producers in developing countries better trading conditions and secured rights. This movement has emerged recently in China, with companies involving domestic trading between richer and poorer regions. However, lack of third-party certification, standardization, process control, public awareness, and brand recognition continue to be challenges. To understand the current fair trade business in China, this paper investigates important decision-making areas from a supply chain management perspective. With the nature of empirical studies, an in-depth case analysis of a fair trade craft company has been conducted along with the purchasing and supplier relationship management, internal operations, and marketing and customer relationship management. This company currently combines the role of fair trade organization and retailer, by implementing an in-house certification system and vertically integrating the supply chain. Findings also highlight risk at each stage of supply chain. Compared with the western society, the unique features of Chinese fair trade business are captured with prioritized areas for improvement. This research contributes to the fair trade literature by providing exploratory study into emerging issues in the supply chain, particularly inside developing countries. The recommendations also create value for policy-makers and practitioners of fair trade companies

Auto-Encoding Adversarial Imitation Learning

Reinforcement learning (RL) provides a powerful framework for decision-making, but its application in practice often requires a carefully designed reward function. Adversarial Imitation Learning (AIL) sheds light on automatic policy acquisition without access to the reward signal from the environment. In this work, we propose Auto-Encoding Adversarial Imitation Learning (AEAIL), a robust and scalable AIL framework. To induce expert policies from demonstrations, AEAIL utilizes the reconstruction error of an auto-encoder as a reward signal, which provides more information for optimizing policies than the prior discriminator-based ones. Subsequently, we use the derived objective functions to train the auto-encoder and the agent policy. Experiments show that our AEAIL performs superior compared to state-of-the-art methods on both state and image based environments. More importantly, AEAIL shows much better robustness when the expert demonstrations are noisy.Comment: 15 page