1. Estimating Carbon Emissions from Less-than-Truckload (LTL) Shipments
Source:
Authors: Guilherme Aguiar and Mark Woolard
Advisor: Dr. Edgar Blanco
Sponsors: C. H. Robinson & Estes Express Lines
MIT SCM Research Fest
May 22, 2014

2. Agenda Key Question Background Information Data and Methodology
Results
Conclusions
May 22, 2014
MIT SCM ResearchFest

3. 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

4. 2. Background Information
May 22, 2014
MIT SCM ResearchFest

5. 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

6. 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

7. LTL Overview D O Customer End-of-Line terminal (EOL) Hub/Breakbulk
May 22, 2014
MIT SCM ResearchFest

8. 3. Data and Methodology
May 22, 2014
MIT SCM ResearchFest

9. 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

10. Carrier Terminals Network
May 22, 2014
MIT SCM ResearchFest

11. 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

12. 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

13. 4. Results
May 22, 2014
MIT SCM ResearchFest

14. Summary of Results Sample of 2,700 shipments: May 22, 2014
MIT SCM ResearchFest

15. Comparison of Total Emissions
May 22, 2014
MIT SCM ResearchFest

16. Comparison of Total Emissions – Short Haul (<300 miles)
May 22, 2014
MIT SCM ResearchFest

17. The Impact of Pick-up and Delivery (P&D)
May 22, 2014
MIT SCM ResearchFest

18. 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

19. The Value of Detailed Information
Comparison of emissions from low-precision and detailed models for individual shipments:
May 22, 2014
MIT SCM ResearchFest

20. 5. Conclusions
May 22, 2014
MIT SCM ResearchFest

21. 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

22. 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

23. 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

24. Estimating Carbon Emissions from Less-than-Truckload (LTL) Shipments
Q&A
Estimating Carbon Emissions from Less-than-Truckload (LTL) Shipments

25. Appendix
May 22, 2014
MIT SCM ResearchFest

26. Appendix
May 22, 2014
MIT SCM ResearchFest

27. Appendix
May 22, 2014
MIT SCM ResearchFest

28. Appendix
May 22, 2014
MIT SCM ResearchFest

29. Appendix
May 22, 2014
MIT SCM ResearchFest

30. Appendix
May 22, 2014
MIT SCM ResearchFest

31. Appendix
May 22, 2014
MIT SCM ResearchFest

32. Appendix
May 22, 2014
MIT SCM ResearchFest