Disaggregating Uncertainties in Operations Analysis of Intermodal Logistics Systems

Abstract

The data collected on second-to-second operations of large-scale freight and logistics systems have increased in recent years. Data analytics can provide valuable insight and improve efficiency and reduce waste of resources. Understanding sources of uncertainty, including emergent and future conditions, is critical to enterprise resilience, recognizing regimes of operations, and to decision-making for capacity expansions, etc. This paper demonstrates analyses of operations data at a marine container terminal and disaggregates layers of uncertainty and discusses implications for operations decision-making and capacity expansion. The layers arise from various sources and perspectives such as level of detail in data collection and compatibilities of data sources, missing entries in databases, natural and human-induced disruptions, and competing stakeholder views of what should be the performance metrics. Among the resulting observations is that long truck turn times are correlated with high traffic volume which is distributed across most states of operations. Furthermore, data quality and presentation of performance metrics should be considered when interpreting results from data analyses. The potential influences of emergent and future conditions of technologies, markets, commerce, environment, behaviors, regulations, organizations, environment, and others on the regimes of terminal operations are examined.