Due to the impact of coronavirus disease 2019 (COVID-19), automation and artificial intelligence (AI) have attracted renewed interest in multiple industrial fields. Global manufacturing bases were affected strongly by workforce shortages associated with the spread of COVID-19, and are working to increase productivity by embracing digital manufacturing technologies that take advantage of artificial intelligence and the Internet of Things (IoT) that offer the promise of improved connectivity among supply chains. This trend can increase and smooth the flow of social capital, which is a potential resource in supply chains and can affect supply chain performance in healthcare industry. However, such an issue has not been properly recognized as the best practice in healthcare industry. Thus, this study investigates empirically the relationship between digitalization and supply chain performance in healthcare manufacturing companies based on previous research that proposed a role for social capital. We surveyed the staff of domestic small and medium-sized healthcare manufacturing companies in South Korea currently operating or planning to deploy digital manufacturing technologies. Online and email surveys were utilized to collect the data. Invalid responses were excluded and the remaining 130 responses were analyzed using a structural equation model in SPSS with the AMOS module. We found that digitalization has a positive effect on the formation of social capital, which in turn has a positive effect on supply chain performance. The direct effect of digitalization on supply chain performance is small, and relatively large portions are mediated and influenced by social capital. The establishment of strategic relationships in the healthcare manufacturing industry is significant, as supply chain networks and production processes can influence the intended use of factory output. Companies should, therefore, secure timely and accurate information to manage the flow of products and services. The formation of social capital in the supply chain can help visualize entire supply chains and has a positive effect on real-time information-sharing among key elements of those chains.

The air cargo market is growing due to the spread of information technology (IT) products, the expansion of e-commerce, and high value-added products. Weather deterioration is one factor with a substantial impact on cargo transportation. If the arrival of cargo is delayed, supply chain delays occur. Because delays are directly linked to costs, companies need precise predictions of cargo transportation. This study develops a forecasting model to predict delay times and costs caused by the delayed arrival of cargo due to severe weather in the air cargo service environment. A seasonal autoregressive integrated moving average (SARIMA) model is developed and analyzed to address delay reductions in cargo transportation. Necessary data are identified and collected using time series data provided by Incheon International Airport Corporation, with an emphasis on monthly data on cargo throughput at Incheon International Airport from January 2009 to December 2016. The model makes forecasts for further analysis. The model stands to provide decision makers with strategic and sustainable insights for cargo transportation planning and other similar applications.