The Cost of Weather: Modeling Weather Delay in Bulk Shipping

Weather is an ever-present factor influencing shipping operations at every stage, including port operations. This paper examines the determi nants of weather-induced delays in port operations, the probability and duration of such delays, the predictive capability of various statistical models, and the potential for improving upon standard industry methods for estimating port margins. A wide range of models are investigated, including Generalized Linear (GLM) models, Cox Propotional Hazard models, and Autoregressive Conditional Duration (ACD) models. The findings reveal that a GLM with gamma distributed dependent variables provides the best fit for data on delay duration, while a linear multiple regression offers the highest predictive accuracy for delay duration. Similarly, probit and logit models are found to perform comparably well for both predicting delay probabilities and data fit. Moreover, the analysis demonstrates that there are significant potential cost savings when using a linear regression model with a probit model to predict delays compared to a common industry rule-of-thumb of half a day delay. These results underscore the potential for improving operational efficiency and accuracy in port margin estimation through statistical modeling techniques.