#PAWNYCDAY2

1. Realtime Pricing
in B2B Truck Logistics
PAW 2017, New York
Alwin Haensel, PhD
Managing Director & Founder

2. € 250 bn
share of truck
transportation
Logistics market grows twice
as fast as the economy
(3% CAGR over next 5 years)
Transportation sector is
extremely fragmented
(53% of companies
own 1-3 trucks)
54.000
thereof in
Germany
High truck density increasingly
problematic (ø26 trucks per kilometer on
highways)
€ 960 bn
Logistics market volume (EU)
575.000
Trucking companies (EU)
500.000
thereof in
Germany
2 millions
Truck tours per day (EU)
The trucking market is huge …

3. … and highly fragmented
84% of trucking
companies
in the EU own
less then 11 trucks
Trucking company structure
(by number of trucks)

4. Fixed lines
Long-term contracts

5. Consequence:
High amounts of empty
capacities

6. The Cargonexx Service
Freight forwarders - agents Algorithm-based platform
Instant market price
Pricing engine powered by
Vetted freight carriers
place orders
place orders place orders
place orders

7. Pricing Problem
Underpricing: price >
prediction is the problem!
Agent A: want to ship a freight (tour)
P (A accept price x) is decreasing in x
Carrier C: has capacity to transport
(tour)
P (C accept price x) is increasing in x
ye
s
ye
s
no
A submits
shipment request
and sees price
x+Δ
price x is
proposed to Cs
no

8. Market price?
What is the market price?
Say, the
willingness-to-pay of A
is 420 and the
minimum-expected-price of C
is 350.
=> any x ϵ [350,420] is a good price

9. Shipment Request 2nd of March 12pm
 Origin: Bocholt, 46395, mid-west Germany, pickup: 5th of March 5-7pm
 Destination 1: Karlsruhe, 76189, south Germany, delivery: 6th of March 5-7am
 Destination 2:Teningen, 79331, south Germany, delivery: 6th of March 5-8am
 Route length: approx. 400 miles
 Cargos:
1. Euro pallets, 7.0 loading meter, 8.5 t, good type ‘construction material’
2. Euro pallets, 7.0 loading meter, 8.5 t, good type ‘construction material’
 Vehicle requirements: ramp loading & discharge, tautliner truck
Request id = "8319dc14-077a-48d2-8b20-1f04d4550eee"

10. Example Vehicle Requirements
 max passage height
 side loading and/or discharge
 ramp loading and/or discharge
 crane loading and/or discharge
 tautliner
 hydraulic ramp
 dangerous good possible
 …

11. Tour Distance vs. Price

12. Origins & Destinations

13. O&D per Weekday

14. Pricing Steps for a Tour Request
Base price level
Acceptance risk hedging
Learns with current data
“Stable Prices” !
Base Model1
Analyst Control2
Temporary human
adjustment based on
certain criteria, e.g.
increases for special
periods such as local
holidays.
Live Adjustments3
Reactions to current
price responses in the
market
Acceptance &
Margin Optimizer
Price $

15. Base Model (1)
Clustering1

16. Base Model (2)
Random Forest with e.g. custom scoring functions,
etc.
2
Source https://helloacm.com/a-short-introduction-bagging-and-random-forest/

17. Base Model (3)
Re-computation of the Random Forest3

18. Base Model (3)
Re-computation of the Random Forest3
Identify the worst
performing trees.

19. Base Model (3)
Re-computation of the Random Forest3
Replace with new
trees.

20. Base Model (4)
Smart Combination of Tree Results4
Request

21. Estimated Price Acceptance Curve
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
530 550 570 590 610 630 650 670 690
proposed price
price - acceptance estimation curve
From the error distribution we can derive an estimated price acceptance curve for the
carrier, i.e. predicted price + error distribution -> price distribution

22. Estimated Price Acceptance Curve
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
530 550 570 590 610 630 650 670 690
proposed price
price - acceptance estimation curve
risk hedging price, which
was proposed -> 649
EUR
=> accepted !!!mean expected
price 611 EUR

23. Stochastic modeling idea
So far the prediction was based on learning patters from observations and to
explain them best as possible.
origin destination weekday distance
loading
meter
weight
historic
price
error for
prediction
340
HamburgBerlin Mon 280 12.4 22000 310 30
HamburgBerlin Tue 295 10.0 24000 340 0
HamburgBerlin Mon 310 13.6 22000 295 45
HamburgBerlin Wed 260 11.8 18000 330 10
HamburgBerlin Tue 300 12.0 21500 350 -10

24. Stochastic modeling idea
BUT we don’t want to explain the past.
-> We want to give a price such that the acceptance probability is maximized.
origin destination weekday distance
loading
meter
weight
historic
price
error for
prediction
340
error for
acceptance
with price 340
HamburgBerlin Mon 280 12.4 22000 310 30 0%
HamburgBerlin Tue 295 10.0 24000 340 0 0%
HamburgBerlin Mon 310 13.6 22000 295 45 0%
HamburgBerlin Wed 260 11.8 18000 330 10 0%
HamburgBerlin Tue 300 12.0 21500 350 -10 100%

25. Current Development
x price of the tour,
𝛼 min bounding probability of
tour acceptance
t tour features,
𝑈 𝐶 utility of the tour for carrier,
𝑈𝐴 utility of the tour for agent
𝐦𝐚𝐱
𝒙,𝜶
𝜶
𝑠. 𝑡.
𝑃 𝑥 ≥ 𝑈 𝐶 𝑡 ≥ 𝛼
𝑃 𝑥 ≤ 𝑈𝐴 𝑡 ≥ 𝛼
𝛼 ∈ 0,1 , 𝑥 > 0 Price acceptance
0%
prices
100%
Get utilities and price-acceptance curves directly
from the raw data without error distributions
from the normal ML framework.

26. IT structure on AWS
pricing response time is < 15ms
Request
LoadBalancer
Pricing Version A
Target Group
Feedback
Target Group
Service Instance
Service Instance
Cloud storage for
model versions
Instances for model
computations and
updates
Data
Warehouse
Replica Set DB
on instances
Spot
Instance
Amazon EC2
Application
Load Balancer
S3

27. Thanks for your attention!
Haensel AMS GmbH
Alwin Haensel, PhD
alwin@haensel-ams.com
Berlin, Germany
US office ìs coming in 2018 ;-)
www.haensel-ams.com