The document discusses the evolution and potential revolutions in the trucking industry and how they relate to analytics. It notes that prescriptive analytics have evolved to provide excellent tactical planning tools for most carriers. However, the next generation needs to support real-time decision making, which remains challenging. Ongoing and potential revolutions in areas like mobile computing, commerce, autonomous vehicles, and collaborative logistics will create new opportunities for analytics systems to help with challenges like dynamic planning and complex system design.

1. Evolution and Revolution in Trucking
Analytics and Automation
Alan Erera
Coca-Cola Professor and Associate Chair for Graduate Studies
School of Industrial and Systems Engineering, Georgia Tech
September 2017

2. What to remember
1. Prescriptive analytics deployed in the trucking industry has
evolved to a state where most carriers can take advantage of
excellent tactical planning tools
2. Next generation prescriptive analytics systems must support real-
time decision support in trucking, but there are many challenges
3. Ongoing and potential revolutions in industry will create additional
opportunities for trucking analytics systems

3. My Lens
• Academic role
• 16 years as Georgia Tech faculty member
• Research program
• Optimizing design and control of logistics
systems
• Industry research sponsors
• Industry experience
• Software developer and consultant for
freight industry
• Decision support for planning systems
• ALK Technologies

4. Evolution Versus Revolution in Industry
Evolutionary change
• Steady progress
• Natural next steps
• Improvements can still be dramatic over time
Revolutionary change
• Entirely new business models
• Completely new industries

5. Evolution of analytics
ecosystem

6. What is Current State of Prescriptive
Analytics Evolution?
• Computing in the cloud
• Key features
• Centralized massive-scale data management
• On-demand resource spin-up
• Virtually limitless computing power
• Software maintenance

7. • Data acquisition
• Old sources
• Detailed transactional data
• New sources
• Real-time feeds
• Sensors
• Internet of Things (IoT)
• Image, voice, video
What is Current State of Prescriptive
Analytics Evolution?

8. • Making predictions with data
• Machine learning
• “Peak of inflated expectations”
• As a technology, not a revolution
• Evolution of computational statistics, AI,
and optimization
• Better predictive modeling available
• Learning-based updating
SAP view of analytics maturity
What is Current State of Prescriptive
Analytics Evolution?

9. • Solvability of large-scale mixed-integer linear optimization models
• Example advanced solver software speed-up from Gurobi
Hardware speedup during
these 8 years is NOT
accounted for here!
What is Current State of Prescriptive
Analytics Evolution?

10. • We have outstanding capability for:
• Solving exactly medium-scale MIP models
• Estimating high-quality bounds for many large-scale MIP models to
benchmark heuristic approaches
• Solving approximately many large-scale MIP models using heuristic search,
primarily via (large) neighborhood search
• Parallelism, metaheuristic kicks
• We have emerging capability for:
• Solving approximately medium-scale or large-scale MIPs in “real-time”
• Solving extended models that add stochastic (expectation) or robust
optimization formulations
What is Current State of Prescriptive
Analytics Evolution?

11. Trucking analytics
evolution

12. State-of-art in truckload analytics
• Truckload trucking and 3PL / freight
brokerage
• Yield optimization, including pricing
• Dispatch optimization, matching resources to
loads
• What’s newest here?
• Improved revenue management methods,
including those based on machine learning
• Better approaches for heuristic solutions of large-
scale dynamic programming formulations

13. State-of-art in LTL analytics
• Less-than-truckload
• Yield optimization, including pricing
• Service network planning problems, including
flow planning, load planning, and scheduling
• P&D route management
• What’s new here?
• Better approaches for solving massive mixed-
integer programming models
• Local search using small MIPs
• Weekly replanning using day-of-week, service-
constrained time-space network models
MAC
ATH
CLT
ATL
PHI
BIR
PITCHI
CIN
MEM
CLE

14. Next evolution for truckload analytics
• Improved dynamic freight matching and resource
management
• Tools for both large asset-based carriers, best-in-class
brokerage/3PL
• Perfecting the online load board
• Better predictive features for drivers, including trip planning
• Super-fast data management, real-time optimization
• Convoy, Amazon, Uber Freight
• Which of these companies can compete with large 3PLs in this
space?
• Improved yield optimization
• Implementation of best-in-class pricing tools
• Machine learning and discrete-choice models

15. Real-time resource allocation remains
challenging
• Approaches still needed:
• Stochastic models with look-ahead,
as necessary
• Accurate scenario predictors
• Super-fast optimization
• Decentralized matching when
resources may reject matches
today’s orders begin
time
today’s orders end
current time
unassigned order
All drivers Upcoming orders partitioned
value of driver – order
assignment

16. Next evolution in LTL Analytics
• Dynamic service network design
• Adapt flow plans, load plans, and schedules to
real-time operational conditions
• Data requirements challenging to manage
• Blend of known information, updated in real-
time, with unknown information
• Many carrier data systems not ready for real-
time data
• Optimization algorithms must handle data
problems gracefully
• Rapid operationalization of updated plans may
require new processes
• E.g., scan-to-sort electronic cross dock

17. Revolutions impacting
trucking analytics and
automation

18. Revolution in Mobile Computing
Major ongoing revolution
• Deep Blue (1997): 12 GFLOPS
• Galaxy 5S (2014) processor: 142 GFLOPS
• Computing and communication capabilities
enabled by portable electronics are astounding
Human trust in automated decisions
• Do you still own a map or an atlas?
• Millennial workforce ready and trusting
Real-time decision support needs
• Trust, ubiquity, scalability, reliability, security

19. Revolution in Consumer
Commerce
• Major ongoing revolution
• Diminishing role of retail stores
• Product delivered directly to consumers
• On-demand delivery (same day, 4 hours, 2 hours)
• New dominant retailers
• Supply chain restructuring
• On-demand consumers may create more
variation in product flows
• Small package carriers experiencing most
difficult disruptions

20. Revolution in Autonomous Freight Vehicles
• Potentially major revolution
• Reduce manual labor
• Possible increases in utilization,
productivity, and safety
• Types
• Automated lift trucks
• Aerial drones
• Sidewalk delivery vehicles
• Self-driving long-haul truck

21. Self-driving Trucks
• Labor transformation
• Reduces driving, not other human tasks
• Sleeper team from 2 to 1!
• Customer transactions, freight security
• Creates an “engineer” role
• Operate vehicle(s), but do not drive
• Highway train/convoy: platooning
• Fuel cost savings
• Dynamic carrier collaboration
• Platoon form-up
• One operator, many vehicles

22. Revolution in Collaborative Logistics: the
Physical Internet
• Potentially major revolution
• Freight mobility as a utility
• Internet for physical goods
• Almost completely analogous
• Features
• Hyperconnected and global
• Open and seamless asset sharing
enabling flows consolidation
• Modularized and encapsulated
• Standards and protocols

23. Revolutions and Analytics
• Revolutions create challenges and
opportunities
• Freight transport system complexity unlikely
to diminish
• Analytics for interpreting and measuring
• Analytics for system design, planning, and control
• Convergence
• Unprecedented opportunity for optimization-based
decision support in control of complex logistics
systems. Enabled by emerging data and computing
platform capabilities.

24. What to remember
1. Prescriptive analytics deployed in the trucking industry has
evolved to a state where most carriers can take advantage of
excellent tactical planning tools
2. Next generation prescriptive analytics systems must support real-
time decision support in trucking, but there are many challenges
3. Ongoing and potential revolutions in industry will create additional
opportunities for trucking analytics systems