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.
| Identifer | oai:union.ndltd.org:purdue.edu/oai:figshare.com:article/12515444 |
| Date | 23 June 2020 |
| Creators | Brooke Renee Cochenour (8996768) |
| Source Sets | Purdue University |
| Detected Language | English |
| Type | Text, Thesis |
| Rights | CC BY 4.0 |
| Relation | https://figshare.com/articles/Predicting_Transit_Times_For_Outbound_Logistics/12515444 |
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