| dc.description.abstract | With the trend of economic globalization and the innovative technology, corporations and customers can obtain tremendous opportunities and selections in the market. However, they are also facing some problems, such as a lack of labor force, rising cost of materials, shorter product life cycle, and the needs of mass customized products. Therefore, stabilizing and improving the operational efficiency of the supply chain is one of the most important conditions for these enterprises to maintain competitiveness in the overall supply chain ecosystem. To make the factories become smart-manufacturing industries, we need Artificial Intelligence (AI) to achieve more flexible management and production, and even to create a new industrial revolution. In addition to achieving the efficiency of process automation and improving the quality and consistency of customer service, AI can also change the original management mode. Furthermore, AI can apply to all aspects of supply chain management (SCM) and can make the operation of enterprises faster, smarter, and more streamlined. The purpose of this research is to explore that how the enterprises can utilize AI in the SCM to efficiently integrate and manage suppliers and manufacturers.
In the trading market, the crude oil price and the industry news may affect each other. When the positive news appears, the oil price usually rises. On the contrary, the price decreases when the negative news occurs. In this research, we propose a novel Reinforcement Learning (RL) algorithm, which utilizes a multi-learning network to predict oil prices and classify emotional news, and then fuses two networks into the RL model to investigate market trends. While interacting with the environment, the RL model can learn the market transactions by itself and employs positive or negative industry news to react to the buying, selling, or holding trading behaviors. By using this model to predict market trends, the professional purchasers can avoid purchasing risks when making purchasing operations. So, this research hopes to effectively present the potential supply risks and provide a fundamental of evaluating the suppliers’ shortage or financial risks before ordering. | en_US |