Estimating Carbon Emissions from Less-than-Truckload (LTL) Shipments Source: http://www.slh.ca/en/about_slh_green_initiatives.aspx Authors: Guilherme Aguiar and Mark Woolard Advisor: Dr. Edgar Blanco Sponsors: C. H. Robinson & Estes Express Lines MIT SCM Research Fest May 22, 2014
Agenda Key Question Background Information Data and Methodology Results Conclusions May 22, 2014 MIT SCM ResearchFest
1. Key Question How to estimate carbon emissions of individual LTL shipments given the complexities of a typical LTL network? May 22, 2014 MIT SCM ResearchFest
2. Background Information May 22, 2014 MIT SCM ResearchFest
Existing Guidance on Emissions Examples: Greenhouse Gas (GHG) Protocol EPA SmartWay Focused on corporate-level accounting Current methods do not capture the dynamics of LTL operations May 22, 2014 MIT SCM ResearchFest
LTL Overview $32-billion industry (2012) Shipment weight < 10,000 pounds Multiple levels of freight consolidation Multiple customers being served simultaneously Pick-up and Delivery (P&D) and Line Haul May 22, 2014 MIT SCM ResearchFest
LTL Overview D O Customer End-of-Line terminal (EOL) Hub/Breakbulk May 22, 2014 MIT SCM ResearchFest
3. Data and Methodology May 22, 2014 MIT SCM ResearchFest
Data Profile Partner companies: C. H. Robinson/TMC Estes Express Lines Over three million shipments moved in a period of three months May 22, 2014 MIT SCM ResearchFest
Carrier Terminals Network May 22, 2014 MIT SCM ResearchFest
Methodology Overview Analyze historic shipment, fuel consumption and mileage data Develop regression models to predict line haul miles Develop an approach for Pick-up and Delivery miles Determine fuel economy (MPG) factors Calculate total emissions using GHG Protocol factors Allocate emissions to single shipments based on Estes’ load factors May 22, 2014 MIT SCM ResearchFest
Methodology Overview Two separate calculation tools: Detailed model for Estes Express Lines’ network Low-precision model for unknown carrier (generic model) Three basic model inputs: Origin ZIP code Destination ZIP code Shipment weight (pounds) May 22, 2014 MIT SCM ResearchFest
4. Results May 22, 2014 MIT SCM ResearchFest
Summary of Results Sample of 2,700 shipments: May 22, 2014 MIT SCM ResearchFest
Comparison of Total Emissions May 22, 2014 MIT SCM ResearchFest
Comparison of Total Emissions – Short Haul (<300 miles) May 22, 2014 MIT SCM ResearchFest
The Impact of Pick-up and Delivery (P&D) May 22, 2014 MIT SCM ResearchFest
The Value of Detailed Information Comparison of emissions from low-precision and detailed models for all 2,700 shipments: May 22, 2014 MIT SCM ResearchFest
The Value of Detailed Information Comparison of emissions from low-precision and detailed models for individual shipments: May 22, 2014 MIT SCM ResearchFest
5. Conclusions May 22, 2014 MIT SCM ResearchFest
Conclusions In order to optimize, we must first measure Our low-precision model was a good approximation for aggregate estimates of multiple shipments A detailed approach is still preferred for estimates at the shipment level May 22, 2014 MIT SCM ResearchFest
Conclusions Comparison methods do not capture the dynamics of LTL networks, generating flawed estimations P&D operations have a high impact on emissions (30%) and operational costs, especially for: Short distances Light shipments May 22, 2014 MIT SCM ResearchFest
Future Research Analyze load factors by lane or by region Further investigate the pick-up and delivery operations Take volume/class/density information into account for the allocation procedures May 22, 2014 MIT SCM ResearchFest
Estimating Carbon Emissions from Less-than-Truckload (LTL) Shipments Q&A Estimating Carbon Emissions from Less-than-Truckload (LTL) Shipments
Appendix May 22, 2014 MIT SCM ResearchFest
Appendix May 22, 2014 MIT SCM ResearchFest
Appendix May 22, 2014 MIT SCM ResearchFest
Appendix May 22, 2014 MIT SCM ResearchFest
Appendix May 22, 2014 MIT SCM ResearchFest
Appendix May 22, 2014 MIT SCM ResearchFest
Appendix May 22, 2014 MIT SCM ResearchFest
Appendix May 22, 2014 MIT SCM ResearchFest