Predicting Carrier Load Cancellation
Truckload cancellations by carriers are causing disruptions in the trucking industry operations. By extrapolating the findings from the 3PL’s data studied in this research to the whole trucking industry, it is estimated that 32 million cancellations occur every year. These cancellations result in ar...
| Main Authors: | , |
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| Language: | en_US |
| Published: |
2018
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| Online Access: | http://hdl.handle.net/1721.1/118108 |
| _version_ | 1826196183471620096 |
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| author | Al-Habib, Ali Favier Gonzalez, Nicolas |
| author_facet | Al-Habib, Ali Favier Gonzalez, Nicolas |
| author_sort | Al-Habib, Ali |
| collection | MIT |
| description | Truckload cancellations by carriers are causing disruptions in the trucking industry operations. By extrapolating the findings from the 3PL’s data studied in this research to the whole trucking industry, it is estimated that 32 million cancellations occur every year. These cancellations result in around $4.6 billion extra cost. If these cancellations can be predicted, shippers and transportation brokers can avoid loss of money and resources caused by the required rebooking process. This research explores the key drivers of loads’ cancellation using historical cancellation patterns. It evaluates the applicability of different predictive models that were built using three-year data from a third-party logistics provider. These models include logistic regression, random forest, neural networks and k-nearest neighbors. However, the research focuses mostly on logistic regression, as it provides more insights of the main drivers of the cancellations. The resulted models were capable of correctly predicting only 16% of the cancelled loads. In effort to improve the accuracy of the logistic regression model, tradeoff analysis was developed to study the impact of adjusting the threshold. The analysis showed that using lower threshold can improve the correctly predicted cancellations to 42%. However, for every additional cancelled load predicted correctly, around 3 uncancelled loads are predicted as cancelled. As all models gave comparable results, the research concludes that the available load information and historical cancellation behaviors are not enough to predict future cancellations. The research concludes by recommending business solutions to be implemented in order to reduce the probability of cancellations. These solutions include educating carriers on the impact of cancellation and encouraging them to cancel with longer timeframe when cancellation in inevitable. Moreover, further research might focus on surveying carriers to identify the root causes of cancellations and capture details related to these causes. |
| first_indexed | 2024-09-23T10:22:50Z |
| id | mit-1721.1/118108 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T10:22:50Z |
| publishDate | 2018 |
| record_format | dspace |
| spelling | mit-1721.1/1181082019-04-11T10:20:32Z Predicting Carrier Load Cancellation Al-Habib, Ali Favier Gonzalez, Nicolas Truckload cancellations by carriers are causing disruptions in the trucking industry operations. By extrapolating the findings from the 3PL’s data studied in this research to the whole trucking industry, it is estimated that 32 million cancellations occur every year. These cancellations result in around $4.6 billion extra cost. If these cancellations can be predicted, shippers and transportation brokers can avoid loss of money and resources caused by the required rebooking process. This research explores the key drivers of loads’ cancellation using historical cancellation patterns. It evaluates the applicability of different predictive models that were built using three-year data from a third-party logistics provider. These models include logistic regression, random forest, neural networks and k-nearest neighbors. However, the research focuses mostly on logistic regression, as it provides more insights of the main drivers of the cancellations. The resulted models were capable of correctly predicting only 16% of the cancelled loads. In effort to improve the accuracy of the logistic regression model, tradeoff analysis was developed to study the impact of adjusting the threshold. The analysis showed that using lower threshold can improve the correctly predicted cancellations to 42%. However, for every additional cancelled load predicted correctly, around 3 uncancelled loads are predicted as cancelled. As all models gave comparable results, the research concludes that the available load information and historical cancellation behaviors are not enough to predict future cancellations. The research concludes by recommending business solutions to be implemented in order to reduce the probability of cancellations. These solutions include educating carriers on the impact of cancellation and encouraging them to cancel with longer timeframe when cancellation in inevitable. Moreover, further research might focus on surveying carriers to identify the root causes of cancellations and capture details related to these causes. 2018-09-17T15:58:09Z 2018-09-17T15:58:09Z 2018 http://hdl.handle.net/1721.1/118108 en_US application/pdf |
| spellingShingle | Al-Habib, Ali Favier Gonzalez, Nicolas Predicting Carrier Load Cancellation |
| title | Predicting Carrier Load Cancellation |
| title_full | Predicting Carrier Load Cancellation |
| title_fullStr | Predicting Carrier Load Cancellation |
| title_full_unstemmed | Predicting Carrier Load Cancellation |
| title_short | Predicting Carrier Load Cancellation |
| title_sort | predicting carrier load cancellation |
| url | http://hdl.handle.net/1721.1/118108 |
| work_keys_str_mv | AT alhabibali predictingcarrierloadcancellation AT faviergonzaleznicolas predictingcarrierloadcancellation |