The complete logistics process from planning to delivery consists of many small decisions and many of those decisions are still made by humans. This session presents how better predicted data and automation with the Simacan platform allows shippers and carriers to be more efficient. Speaker: Silvie Spreeuwenberg, Business Controller at Simacan. *Intelligent Mobility 2021: Virtual Conference.

1. The added value of predictions
for transport delivery
A step towards continuous transport planning and 5-star delivery experience.
Silvie Spreeuwenberg

2. ■ challenges in transport logistics
■ introduction to the Simacan platform
■ benefits - related to role of predictions
■ future of transport logistics
● autonomous shipments
● continuous planning
Agenda

3. People on this session
Business Controlling & Strategy
Business controller and program
manager for the scale-up
program of Simacan. Passionate
about using AI to improve and
optimize human decision making.
Silvie
Spreeuwenberg

4. Legenda
Supplier
Inbound function depot
Outbound function depot
Receiver
Journey Supplier Inbound depot
Journey Inbound Outbound depot
Journey Outbound Receiver
strictly confidential - sharing this without Simacan consent is prohibited
CONNECTING THE DOTS IN SUPPLY CHAIN
Road transport by commercial (electric) vehicles

5. MANAGE THE PARADOX IN SUPPLY CHAIN!
CUSTOMER INTIMACY
OPERATIONAL EXCELLENCE
BUSINESS
NATURE
CUSTOMER
EXPECTATION

6. YOUR PLAN
REALITY

7. The Simacan smart collaboration platform
allows stakeholders in time critical delivery networks
to provide customers with a 5-star delivery experience
against minimal waste

8. Working as one!
Some examples to start with

9. Working as one!
DC - shop - driver - planner alignment

10. Working as one!
Customer notification and proof of delivery

11. Working as one!
eCMR paperless process, monitoring and alerting seamlessly integrated

12. Working as one!
Departure and arrival specific routing instructions brought to existing onboard computers

13. Working as one!
Business analist Order & Transport
admin
All data available for analytics & reporting

14. Working as one!
Automated
exception
alerts and
management

15. Shop manager
Consumer
Hub manager
9,9
5-STAR DELIVERY EXPERIENCE

16. Personal devices
Personal devices Planning screen In & outbound monitor Onboard devices
Platform intelligence enriches data for smarter exchange
Intelligent algorithms Up-to-date maps Planning & routing
Traffic information Intelligent traffic systems Master data
Two-way communication Real-time insights Last-mile assistance Spot-on notification
Home delivery supply chain

17. Benefits our clients report
Role of predictions in
managing the delivery
experience

18. “Brilliant in its simplicity and reducing turnaround
times with 15% on one hand and reducing the
drivers time spent in traffic jams with 7%”

19. Current time & state ETA next stops Exception
11:00 Departing 12:00 - 13:30 none

20. Current time & state ETA next stops Exception
11:00 Departing 12:00 - 13:30 none
11:05 Driving 12:01 - 13:30 none

21. Current time & state ETA next stops Exception
11:00 Departing 12:00 - 13:30 none
11:05 Driving 12:01 - 13:30 none
11: 20 Driving slow traffic 12:05 - 13:35 minor delay

22. Current time & state ETA next stops Exception
11:00 Departing 12:00 - 13:30 none
11:05 Driving 12:01 - 13:30 none
11: 20 Driving slow traffic 12:05 - 13:35 minor delay
11:30 Not driving 13:30 - 15:30 major delays with
consequences for
subsequent deliveries

23. Current time & state ETA Exception
13:30 Onloading 15:30 none
13:40 Departing 15:30 none
13:50 Driving slow traffic 15:30 none predicted
15:30 Arriving 15:30 none predicted

24. Trajectory method
We calculate the travel time from a starting
date/time for one link in the route and use the
resulting end time as the input for the next link.
The travel time is based on speed profiles,
actual free-flow speed, truck factors, region and
date factors.
Instantaneous method
For each link in the route, we calculate the travel
time using speed profiles, actual free-flow
speed, truck factors and date factors. The travel
times of each link are combined into one travel
time.
Truck factors
The vehicle type influences the actual travel
time in the case of certain roads or certain road
elements such as roundabouts.
Regional factors
There may be regional differences between
actual speeds per road type.
Date factors
The time of day, day of the week, season and
holidays all influence actual travel speeds.
Why are our ETAs better?
Tapering method
ETAs are often unreliable because by the time
the vehicle arrives at the spot of an identified
traffic jam or incident, the traffic is often flowing
again. Our tapering method combines the
instantaneous method for ETAs close by with the
trajectory method for ETAs further ahead.

25. All scheduled
deliveries,
including
expected delay in
planned arrival.
Store display
Next delivery
based on latest
ETA calculation
Store location
Planned route
and actual
position transport

26. Remember two things:
1. Less turnaround time because the unloading
team knows when the truck arrives.
2. Less time in traffic jams because the routing
and planning system understands daily traffic
routines.

27. Half of our late deliveries have been resolved. This
creates savings, but more importantly happy
customers

28. own fleet
carriers

29. Remember one thing:
1. A branded track and trace portal increases the
customer experience and allows you to choose
the best carrier for the occasion.

30. Step by step improving our supply chain to
improve the information to the store managers
having greater insight into the delivery and
having increased the delivery performance by
50%

31. 1. Inform about subsequent
delays
2. Change order sequence
3. Swap orders between assets
Planning the home delivery

32. Remember two things:
1. Groceries delivery may start from a shop
location or a hub location.
2. When multiple vehicles operate in a small area
the planning may be easily adjusted to
accommodate for exceptions.

33. PRE-TRIP
ON-TRIP
POST-TRIP
Impact
Continuous
learning
planning
algorithms
Continuous
optimisation
of order
sequence
within a trip
Reallocation
of orders
between
trips
Continuous
reallocation
of assets
Continuous
planning:
No more
cut-off
moment
Complexity
Steps to full continuous planning

34. PRE-TRIP
ON-TRIP
POST-TRIP
Impact
Continuous
learning
planning
algorithms
Continuous
optimisation
of order
sequence
within a trip
Reallocation
of orders
between
trips
Continuous
reallocation
of assets
Continuous
planning:
No more
cut-off
moment
Complexity
Steps to full continuous planning
Reinforcement learning
We are using reinforcement learning to
create a model that suggests intertrip
mutations based on vehicle locations, trip
information and ETA updates.

35. Our vision and trajectory

36. Thank you