Blockchain technology is now considered a next generation information technology tool for sustainable growth in supply chain (SC) management. However, its study is relatively rare in the literature on SC collaboration and sustainability management research, despite its advantages in sustaining connectivity and reliability among SC partners. This study investigates how the use of blockchain in SC activities can influence (increase or decrease) SC partnership efficiency and growth, thereby affecting SC performance outcomes. Specifically, this study empirically validates a measurement and structural equation model with 306 SC experts from various industries. The findings show that the blockchain technology characteristics (information transparency, information immutability, and smart contracts) have significant positive effects on partnership growth and marginal effects on partnership efficiency. Though partnership growth has a positive effect on firm performance, partnership efficiency shows a negative effect.

We study inventory pooling in systems with symmetric costs where supply lead times are endogenously generated by a finite-capacity production system. We investigate the sensitivity of the cost advantage of inventory pooling to various system parameters, including loading, service levels, demand and production time variability, and structure of the production system. The analysis reveals differences in how various parameters affect the cost reduction from pooling and suggests that these differences stem from the manner in which the parameters influence the induced correlation between lead-time demands of the demand streams. We compare these results with those obtained for pure inventory systems, where lead times are exogenous. We also compare inventory pooling with several forms of capacity pooling.

We principally focus on evaluating the local and entire network performance of railway stations for sustainable logistics management in South Korea. Specifically, we aim to address the issue of dealing with vulnerability in logistics dependent on the degree of connectivity. To resolve this issue, we investigate (i) the current level of local railway station sustainability performance from the perspectives of the value of the station (node) and the geographical location (place), and (ii) how railway station network management can prepare for imminent internal and external risks. Integrating node–place analysis and social network analysis approaches, we demonstrate a means of assessing (i) local railway station performance by comparing how one station’s value differs from that of other stations, and (ii) overall railway network performance by measuring the degree of connectivity based on the centrality characteristics. Consequently, we recommend improvement in planning orders considering the degree of local performance and network vulnerability for railway station network sustainability.