2018DSpace@MIT (Massachusetts Institute of Technology)Open access

A Study of Shipper Performance in the Less-Than-Truckload Market

Ben Yin, Christos W. Rallis

Open full text 0 citations

Abstract

When it comes to LTL shipping, it can be tough for shippers to get the performance that they expect due to the makeup of LTL networks. On-time performance is dependent on many more factors than in full truckload shipping. Performance often comes down to attributes of the shipment such as size and weight and also attributes of the geographical shipment volume. It is critical for shippers to understand these attributes and how they contribute to on-time performance of their own shipments. Through quantitative and qualitative analysis, this capstone details the shipment, shipper, and geographical characteristics that impact on-time performance of LTL shipments. Data from 33 shippers over a period of nearly two years was provided by C.H. Robinson and TMC (a division of CHR). This data was evaluated through a mix of regression and segmentation methods, as well as through qualitative understanding of the industry and economic landscape. The modeling and analysis here within describe the attributes of high performing shipments and provides guidance for shippers as to how to strive for the best performance. We found that shipment size, transit length, and destination shipment volume are among the largest drivers of on-time performance. Although on-time pick up and on-time delivery share some common significant drivers, significant drivers are not all the same for both. This report dives into further detail to help shippers understand the drivers what they can do to manage expectations and performance of their LTL shipments.

Open-access reader

About this research paper

What this paper is about

When it comes to LTL shipping, it can be tough for shippers to get the performance that they expect due to the makeup of LTL networks. On-time performance is dependent on many more factors than in full truckload shipping. Performance often comes down to attributes of the shipment such as size and weight and also attributes of the geographical shipment volume. It is critical for shippers to understand these attributes and how they contribute to on-time performance of their own shipments. Through quantitative and qualitative analysis, this capstone details the shipment, shipper, and geographical characteristics that impact on-time performance of LTL shipments. Data from 33 shippers over a period of nearly two years was provided by C.H. Robinson and TMC (a division of CHR). This data was evaluated through a mix of regression and segmentation methods, as well as through qualitative understanding of the industry and economic landscape. The modeling and analysis here within describe the attributes of high performing shipments and provides guidance for shippers as to how to strive for the best performance. We found that shipment size, transit length, and destination shipment volume are among the largest drivers of on-time performance. Although on-time pick up and on-time delivery share some common significant drivers, significant drivers are not all the same for both. This report dives into further detail to help shippers understand the drivers what they can do to manage expectations and performance of their LTL shipments.

Why it matters

A significance statement is not available in the OpenAlex record.

Key contribution

A contribution statement is not available in the OpenAlex record.

Method / approach

Method details are not available in the OpenAlex metadata.

Main findings

Findings are not separately available in the OpenAlex metadata.

Limitations

Limitations are not available in the OpenAlex metadata.

Applications

Application details are not available in the OpenAlex metadata.

Available abstract

When it comes to LTL shipping, it can be tough for shippers to get the performance that they expect due to the makeup of LTL networks. On-time performance is dependent on many more factors than in full truckload shipping. Performance often comes down to attributes of the shipment such as size and weight and also attributes of the geographical shipment volume. It is critical for shippers to understand these attributes and how they contribute to on-time performance of their own shipments. Through quantitative and qualitative analysis, this capstone details the shipment, shipper, and geographical characteristics that impact on-time performance of LTL shipments. Data from 33 shippers over a period of nearly two years was provided by C.H. Robinson and TMC (a division of CHR). This data was evaluated through a mix of regression and segmentation methods, as well as through qualitative understanding of the industry and economic landscape. The modeling and analysis here within describe the attributes of high performing shipments and provides guidance for shippers as to how to strive for the best performance. We found that shipment size, transit length, and destination shipment volume are among the largest drivers of on-time performance. Although on-time pick up and on-time delivery share some common significant drivers, significant drivers are not all the same for both. This report dives into further detail to help shippers understand the drivers what they can do to manage expectations and performance of their LTL shipments.

Key concepts: Operations management, Business, Economics

Related papers

Back to paper searchBrowse research topicsOriginal source
A Study of Shipper Performance in the Less-Than-Truckload Market — Research Paper | ScholarLens