On assessing input congestion in container terminals

Abstract Given the key role ports play in the trade and economic growth of countries, port managers are looking for novel ways to reduce inefficiencies and improve port performance. Input congestion (defined below) is one of the inefficiency factors in ports and calculating and identifying it is one of the most important keys to improving port performance. In this study, we are addressing input congestion via Data envelopment analysis (DEA). As a case study, the one-stage DEA model is used to calculate the efficiency scores and input congestion of Adriatic ports in the period 2020–2023. The model is proposed as a tool that container terminal managers can use dynamically to calculate and plan the optimal allocation of resources. Inputs include terminal area, length of quays, and two other inputs introduced in this study for the first time, namely the degree of connectivity and the level of terminal equipment. Financial and labor data were not available for all ports and were thus not included in the analysis. Output is represented by port throughput. In this paper, inefficiency and input congestion are assessed simultaneously. The results identify ports that are relatively inefficient compared to their competitors due to input congestion. The results are then compared with 12 Mediterranean ports to contextualize our findings.