No abstract

With the intensification of market competition and the development of market globalization, the efficiency of supply chain management orders has become an important part of enterprise competition resources. The competition among enterprises is fierce. To achieve effective customer response quickly, the time for supply chain order management is minimized, and refine the order processing process. This article introduces the strategy research of supply chain management order based on a reinforcement learning algorithm. This article first combines the reinforcement learning algorithm and deep learning algorithm, using the optimal decision-making ability of reinforcement learning algorithm and deep learning algorithm. The combination of data perception and the optimal ability to analyze examine the data of the order process, order cycle, and order delivery process of the supply chain order management and give the optimal decision. The supply chain order management process conducts questionnaire surveys and seminars to understand the current process of supply chain order management and the problems derived from the analysis of data based on the deep learning algorithm. Finally, through the output of the optimal strategy of the reinforcement learning algorithm, the supply chain order management process was improved, and the satisfaction survey was conducted again. The survey showed that the satisfaction was improved, and the satisfaction reached more than 90%.

The COVID-19 pandemic has had a serious impact on firms’ sourcing strategies. Since COVID-19 disrupted the supply chain, firms have had to make emergency purchases from other suppliers. In addition, emergency ordering is one of the most effective strategies to achieve sustainable operations because such a strategy can save inventory costs. We aim to address a retailer’s emergency procurement strategies during the COVID-19 pandemic. We use prospect theory and the newsvendor model to uncover the retailer’s inventory decisions. In our study, we find that retailers have the choice to order items before the selling period at the normal purchase price, and, if available, they can order them before the end of the selling period at the urgent purchase price. We perform a comparison of the optimal ordering policy and margins in this case with the conventional and loss aversion models. The influence of emergency procurement on the optimal order policy and margins is investigated as well. This paper contributes in theory that we innovatively capture the uncertainty of emergency sourcing, which is a feature that has never been considered in current research.