## 1 Introduction  
An established efficient and reliable inventory management system guarantees the reliable supply of cross-border e-commerce commodities, provides supply chain members with a flow of products, and meets the ever-changing requirement of fulfilling various customer demands [1]. Additionally, precise and robust inventory forecasting is essential for replenishment automation and e-commerce service automation. [PERSON] and [PERSON] [2] proposed that inventory management is one of the key decisions in supply chain management in response to globalization. The five fundamental supply chain management sectors are planning, purchasing, manufacturing, distribution, and return. Supply chain management refers to the scientific management method of product manufacturing, transportation, distribution, and sales that effectively organize suppliers, manufacturers, warehouses, distributors, and retailers to minimize the entire supply chain systemwide cost and maximize customer service level. However, in the supply chain planning process, the traditional demand forecasting method is limited by scientific and technological factors as well as data volume and variety. Machine learning models can be used to classify various data based on product past sales volume in the supply chain. Through mining, integrating, and analyzing all kinds of necessary data of products, they can extract key data features and find complex dependency relationships in data. Nevertheless, these data are usually very intensive and difficult to handle. The advancement of technological development, empowered consumers, e-commerce, and globalization have facilitated the growth of electronic supply chain management in the 21 st century. As such, electronic supply chain management and logistics 4.0 will be the new trend of supply chain development in the coming years. The outbreak of the COVID-19 pandemic induced governments to adopt 'lockdown' policies, tight social distancing measures, quarantine, and rigorous anti-infection measures (i.e., human-to-human; human-to-cargo). Clearly, the enhancement of electronic supply chain management is the only way to mitigate the risks to the supply chain and sustain the competitiveness of supply chain management due to COVID-19 [3]. As expected, high-speed internet access would integrate enterprises, customers, and suppliers into the electronic supply chain and exchange vital information and real-time data promptly. The rapid, transparent, and integrated information flow can respond to real customer demand in time through the Internet and other new, innovative technologies. To this end, firms would achieve prompt responses to final customer requirements, reduce inventory costs, improve efficiency, promote traceability, and enhance technical sustainability for building resilient supply chains [4]. With the continuous development of networks, artificial intelligence (AI), machine learning, blockchain technology, and database technology, research has started to use complicated computer modelling and systematic analysis tools for forecasting and inventory prediction applications. AI has been widely adopted in the real applications of many disciplines such as logistics, healthcare [5; 6], water quality [7], hydro informatics problems [8], etc. In logistics services, [PERSON] et al. [9] proposed the use of the AI model to forecast economic recession through logistics industries. Recently, blockchain technologies [10] were applied to the inventory management of a large-scale company in the aviation industry. To enhance supply chain quality management, various critical factors were considered by [PERSON] et al. [11], and the fuzzy approach was adopted in [PERSON] et al. [12] for inventory classification in order to enable enhanced logistics management and decision-making. A relevant integration model was established through supply chain big data collection and analysis to encourage enterprises to use quantitative analysis and forecast future supply chain situations.