MetroFreight

Improving Goods-Movement Efficiency with Load-Matching Technology

Project Number

2.1e

Project Summary

Project Summary: Load-matching technology for truckers and shippers helps an inefficient and often fragmented local trucking market by eliminating non-revenue-generating trips. There is reason to believe that the market for this kind of service will only grow; however expansion will depend upon a combination of economic and political factors. The roll-out of load-matching services in Los Angeles will provide useful lessons for their adoption in other locations.

Project Status

Complete

Year

2015

Topic Area

Sustainable Urban Freight

P.I. Name & Address

Director, METRANS CSULB Programs
California State University Long Beach
6300 State University Drive
Suite 255
Long Beach, CA 90815
United States
Thomas.Obrien@csulb.edu
Associate Professor, Department of Economics; College of Liberal Arts
California State University Long Beach
1250 Bellflower Blvd.
S/SPA 361A
Long Beach, CA 90840
United States
Seiji.Steimetz@csulb.edu

Project Summary: Load-matching technology for truckers and shippers helps an inefficient and often fragmented local trucking market by eliminating non-revenue-generating trips. There is reason to believe that the market for this kind of service will only grow; however expansion will depend upon a combination of economic and political factors. The roll-out of load-matching services in Los Angeles will provide useful lessons for their adoption in other locations.

Project Description: One way to improve goods-movement efficiency is to improve the way information is exchanged between shippers and carriers.  For instance, less-than-truckload (LTL) motor carriers find it difficult to market the extra space they have on their trucks.  Independent owner-operators serving the drayage and full-truckload (FTL) markets have trouble coordinating directly with manufacturers, third-party logistics (3PL) providers, and freight forwarders.  What results is wasted capacity and an excess of truck trips (including “deadhead” trips, i.e. trucks that consume highway space while empty).  And every move is typically orchestrated through a time-consuming series of phone calls, emails, and faxes.

Recently, however, “load matching” technology has emerged to help reduce the information and coordination costs of good movement.  The basic idea is to provide a real-time, online, GPS-based connection between shippers and carriers, somewhat similar to how Uber and Lyft connect drivers and passengers.  One key innovator in Southern California uses load matching technology to connect over 400 businesses with more than 700 owner-operators in New York, Los Angeles, and the San Francisco Bay Area.

The general benefits of load-matching services are somewhat obvious.  Full trucks mean fewer trucks, resulting in less highway congestion and reduced truck pollution.   However, load-matching is still a nascent technology, and there is still much to learn about how it can be improved to further reap its efficiency gains.  Moreover, the fact that load-matching services are already in operation provides an unprecedented opportunity to cultivate and analyze the data required to realize those improvements

The purpose of this project, simply stated, is to investigate the role that data-driven analytics can play in improving goods-movement efficiency through load-matching technology.  A corresponding goal is to reveal any economic, political, or institutional barriers to wider implementation that cannot be empirically addressed.  With the cooperation of a load matching firm based in the greater Los Angeles area, the project’s key research activities include:

  1. Examining the availability of data on load-matching movements, including information on pricing, transit times, service reliability, lead and turnaround times, and characteristics of shippers and operators
  2. Compiling and constructing a comprehensive database that can be used for analytics
  3. Developing a feasible set of analytic possibilities
  4. Performing a select set of feasible analyses
  5. Providing direction on steps needed and data required to perform further analysis
  6. Identifying barriers to adoption that cannot be addressed through data or analytics

Those activities are described in fairly general terms because, frankly speaking, little is known about the availability and condition of the data generated by load-matching operations.  As such, this project might generally be described as a “feasibility study”.  But its potential gains are substantial relative to its cost.  For example, analyzing the tradeoffs that shippers make between prices and delivery times might reveal pricing strategies that improve adoption.  As a result, fewer truck trips would be generated, thereby reducing highway congestion and pollutant emissions.  Furthermore, it may be possible to determine the number of truck trips removed, thereby making it possible to estimate the magnitudes of those congestion and emissions reductions.

While this project focuses on load-matching operations in Los Angeles, we anticipate that its findings will readily generalize.   Moreover, they will provide a unique, analytical perspective on how to implement load-matching technology in untapped markets, thereby expanding the potential for that technology to improve the efficiency of goods movement nationwide. 

 

Initial Workflow
We have secured access to one company’s data for analyzing factors that influence the supply of load-matched carriers in short-haul trucking operations.  To put this analysis in context, consider for example a shipper who needs to transport a load from its warehouse to a destination 100 miles away.  In our company’s load-matching facility, a request will be broadcast to participating carriers with the load size, destination, and price.  An accepting carrier will be paid that price.  If no carrier accepts, the company will assume responsibility for delivery using its own for-hire fleet.

 

The load matching company is especially interested in understanding how factors such as pricing, load size, lead time, distance, inter alia, affect the likelihood that a shipment will be accepted.  The policy appeal of this analysis is that greater acceptance rates ultimately imply fewer truck trips and attendant reductions in congestion and emissions.

 

To complete this analysis, our faculty and student research team will:

  1. Obtain and export data on acceptance rates and shipment characteristics (including prices).
  2. Prepare data for statistical analysis.
  3. Develop an econometric (discrete-choice) model relating acceptance rates to shipment characteristics.
  4. Determine how those characteristics influence acceptance rates.  For example, determine how a one-percent increase in price affects the probability of acceptance.
  5. Develop a report summarizing findings, including institutional and political barriers to adoption, and providing recommendations for improving acceptance rates.

As the results of this analysis are likely to garner considerable interest from the academic community, we will also develop a manuscript of its findings for submission to a well-regarded, peer-reviewed journal.

This particular workflow will also set the stage for ongoing analysis, as described above.