Indiana University-Purdue University Indianapolis (IUPUI) / On-time delivery of supplies to industry is essential because delays can disrupt production schedules. The aim of the proposed application is to predict transit times for outbound logistics thereby allowing suppliers to plan for timely mitigation of risks during shipment planning. The predictive model consists of a classifier that is trained for each specific source-destination pair using historical shipment, weather, and social media data. The model estimates the transit times for future shipments using Support Vector Machine (SVM). These estimates were validated using four case study routes of varying distances in the United States. A predictive model is trained for each route. The results show that the contribution of each input feature to the predictive ability of the model varies for each route. The mean average error (MAE) values of the model vary for each route due to the availability of testing and training historical shipment data as well as the availability of weather and social media data. In addition, it was found that the inclusion of the historical traffic data provided by INRIXTM improves the accuracy of the model. Sample INRIXTM data was available for one of the routes. One of the main limitations of the proposed approach is the availability of historical shipment data and the quality of social media data. However, if the data is available, the proposed methodology can be applied to any supplier with high volume shipments in order to develop a predictive model for outbound transit time delays over any land route.

On-time delivery of supplies to industry is essential because delays can disrupt production schedules. The aim of the proposed application is to predict transit times for outbound logistics thereby allowing suppliers to plan for timely mitigation of risks during shipment planning. The predictive model consists of a classifier that is trained for each specific source-destination pair using historical shipment, weather, and social media data. The model estimates the transit times for future shipments using Support Vector Machine (SVM). These estimates were validated using four case study routes of varying distances in the United States. A predictive model is trained for each route. The results show that the contribution of each input feature to the predictive ability of the model varies for each route. The mean average error (MAE) values of the model vary for each route due to the availability of testing and training historical shipment data as well as the availability of weather and social media data. In addition, it was found that the inclusion of the historical traffic data provided by INRIX™ improves the accuracy of the model. Sample INRIX™ data was available for one of the routes. One of the main limitations of the proposed approach is the availability of historical shipment data and the quality of social media data. However, if the data is available, the proposed methodology can be applied to any supplier with high volume shipments in order to develop a predictive model for outbound transit time delays over any land route.