1. Data-driven Risk Assessment for Truckload Service Providers
Authors: Kishore Chittella and Marcos Teixeira
Advisors: Dr. Daniel Steeneck and Dr. Chris Caplice
Sponsor: Coyote Logistics
MIT SCM Research Fest 2015

2. MIT Center for Transportation & Logistics
Agenda
Introduction
Methodology
Data Analysis
Results
Volatility Factors
Distribution Fitting
Lane Characterization
Risk Profiles
Management Implementation
Insights
Recommendations
Future Research
MIT Center for Transportation & Logistics

3. Third party logistics business
Shipper
3PL
Carrier
Annual contract
Spot Market
Connect shippers (loads) with carriers (trucks)
Profit = [Price paid by shipper] – [cost paid to carrier]
Price is determined through annual contract
Cost is determined through spot market
MIT Center for Transportation & Logistics

4. MIT Center for Transportation & Logistics
Linehaul spot market
GDP
Origin
Industry
Destination
HOS
Distance
Capacity
Volume
Agriculture
Backhaul
Specificity
Season
MIT Center for Transportation & Logistics

5. MIT Center for Transportation & Logistics
Thesis focus
Research Questions:
How can volatility be measured and understood?
What factors influence spot market volatility?
How do volatility patterns vary (geography, season, etc.)?
How can this research inform pricing?
Sponsor Company:
Large North American non-asset based third party logistics service provider
MIT Center for Transportation & Logistics

6. Methodology PROBLEM DEFINITION Data Collection
Spot market transactions
Truck capacity
External factors (GDP growth rates, agricultural patterns)
Data Analysis
Data cleaning & aggregation
Sample selection
Distribution fitting
Volatility calculations
Linear regression
Results
Lane characterization
Risk profiles
Volatility factors
MANAGEMENT IMPLEMENTATION
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7. Data analysis – cleaning and sampling
Over 1.5M shipment loads
7 regions
90K 3digit origin/destination lanes
674 3digit lanes with 1+ load/month in 2012 and 2013
Cost per mile computation
MIT Center for Transportation & Logistics

8. Data analysis – volatility
Coefficient of variation
Beta
Average M/M % ∆
HOW?
𝛽= 𝐶𝑜𝑣(𝑥,𝑦 𝑉𝑎𝑟(𝑦
𝐶𝑉= 𝜎 𝜇
𝐴𝑣𝑔= n=0 𝑁 𝑀 𝑛 𝑀 𝑛−1 ∗100% 𝑛
Degree of variation in linehaul CPM of a lane in the context of the mean.
Relative risk of a lane in comparison to an index e.g. national, CASS etc.
The extent to which the linehaul CPM varies (i.e. frequent oscillation vs. abrupt drop)
WHAT?
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9. Results – national and regional indices
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10. Results – volatility factors
Volume
Distance
Season
GDP index
# trucks (O)
Agriculture
Central Atlantic
Gulf Coast
Lower Atlantic
Mid West
New England
Rocky Mountain
West Coast √
Individual lanes
MIT Center for Transportation & Logistics

11. Results – distribution fitting
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12. Results – lane characterization
Top 10 lanes
Avg M/M % ∆ CV
Beta
Risky
Long haul
Short haul
Safe
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13. Results – risk profile – coefficient of variation
Mean: 0.16
Median: 0.14
Range: 1.35
Standard Dev: 0.10
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14. Results – risk profile - beta
Mean: 0.54
Median: 0.53
Range: 18.78
Standard Dev: 1.09
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15. Results – risk profile – average M/M % ∆
Mean: 3%
Median: 2%
Range: 138%
Standard Dev: 8%
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16. Management Implementation
Percentile
Beta
Coefficient of Variation
Average M/M % Change
100th
x>1.25 or x<-1.25
x>19%
x>3%
75th
1<x≤1.25 or
-1.25≤x≤-1
14%<x≤19%
1.77%<x≤3%
50th
1≤=x≤=-1
x≤14%
x≤1.77%
Lane
Zip
Region
Distance
Volume
Laredo TX to Houston TX
Gulf Coast
350 mi
1977 loads
Phoenix AZ to Inglewood CA
West Coast
285 mi
1420 loads
MIT Center for Transportation & Logistics

17. MIT Center for Transportation & Logistics
Insights
Rocky Mountain and New England are the riskiest regions
Mid West and Lower Atlantic are most stable
Lower Atlantic consists of most high risk long haul lanes
Mid West consists of most high risk short haul lanes
Volatility factors vary geographically
Certain volatility factors only apply to certain geographies (regions and lanes)
E.g. effect of GDP index is present in certain lanes e.g. City of Industry, CA
MIT Center for Transportation & Logistics

18. MIT Center for Transportation & Logistics
Recommendations
View volatility and risk holistically
Beta, coefficient of variation and average m/m % change
Take a geographical approach to identify volatility patterns
Regions, sub-regions and individual lanes
Apply granularity to volatility factors
MIT Center for Transportation & Logistics

19. MIT Center for Transportation & Logistics
Future Research
Include other factors that influence spot market volatility
Investigate other potential segmentation approaches (distance, corridors, time periods etc.)
Draw parallels between financial markets and line haul spot market
MIT Center for Transportation & Logistics

20. MIT Center for Transportation & Logistics
Thank you
Questions?
MIT Center for Transportation & Logistics