This document discusses how AI can add value to logistics operations. It outlines Softec International, a leader in fleet management and intelligent transportation solutions. Traditional fleet management provides cost reduction and optimization, but more value is needed. AI like evolutionary computing and machine learning can be applied to injection "intelligence" into operational workflows. This helps optimize routes and schedules while accounting for uncertainties. The approach focuses on user-friendly interfaces and value over technical aspects. Key challenges include data quality and unpredictability during execution.

1. AI FOR LOGISTICS
A PRACTICAL APPRAOCH TO SOLVING PROBLEMS
NOUR SHAWKY
CEO, Softec International

2. SOFTEC HIGHLIGHTS
 Globally recognized leader in fleet management and intelligent transportation solutions
 Offices in Dubai, Abu Dhabi and Cairo
 Cairo-based AI Innovation Lab
 Serving 2,500+ organizations worldwide
 60,000+ managed vehicles
 Microsoft GOLD Partner and Global Partner of the Year Finalist
 High-impact Endeavor Entrepreneurship Company since 2016
Fleet Management Command & Control Artificial Intelligence

3. Fleet
Tracking
Maintenance
Management
Resource
Management
Analytics
and Reporting
Work Order
Management
What we have been doing the past years

4. TELEMATICS – TRACK & TRACE
Fleet Panel
with live data
Find nearest
vehicle
Driver Details, Fuel
Details, Sensor Details,
Assigned Work Order,
Speed Chart
Map Panel
Zoom into
vehicle
Vehicle Condition,
Odometer/mileage, real-time
sensor data, RPM/fuel
level/temperature/…
Geo-fencing,
Landmarks

5. COMPLETE FLEET MOVEMENT LOG AND HISTORY
Video Playback
Stop Location
Details
Speed and temperature charts
Sensor Details
Vehicle Dashboard Items
Speed Chart
Trip Summary,
Sensor Data Engine Status (Count + Time)

6. ALTAIR
Business Intelligence
Heat Maps
Variety of Charts
Interactive Charts
Export to JPG, Excel, CSV, ..

7. HQ Operation
CRM
ERP
INTEGRATIONAPIs
Branch A Operation Region B Operation
WORKSHOPS
Internal and External DRIVERS VEHICLES
Business
Management
OperationManagement
On Customer Servers
Enterprise Implementations

8. Value proposition of traditional fleet management systems
 Cost Reduction – Fuel & Maintenance Management
 Optimization of utilization
 Optimization of predefined routes (TSP)
 Streamline through integration with ERP / Accounting software
 Driving Behavior, Green Driving, Safety, Anti-theft
 Asset & Resource Management

9. Focus to add more value
School Bus Food Delivery
Limousine
Services
Public
Transport
Car Rentals
Airport
Ground
Services
Smart
Ambulance
Couriers Oil & Gas Field Services Logistics
Waste
Management
Van Sales
Governments

10. Key challenges of logistics operations remain
X Unpredictable service times
X Driver shortages
X Anomalies during execution
How can we achieve more?
X Shorter time windows &
tighter regulations

11. Injection of Intelligence into the Workflow
Inject “intelligence” into the operational workflow
Applied AI Disciplines:
(1) Evolutionary Computing
(2) Machine Learning

12. The “Technology Trap”
“Yet many companies fail to deliver exceptional value because they
are obsessed by the novelty of their product or service, especially if
new technology plays a part in it.
Unless the technology makes buyer’s lives dramatically simpler,
more convenient, more productive, less risky, or more fun and
fashionable, it will not attract the m asses no matter how many
awards it wins.
Value innovation is NOT the same as
technology innovation”
Blue Ocean Strategy by W. C. Kim and R. Mauborgne

13. Approach to applying AI to solve problems
(1) Innovation must be combined with
value to end users
(2) Avoid technical “jargons”
(3) Wrap technology behind user friendly
interface

14. Evolutionary Computing
1
Order Queue
Time Windows
Constraints
Traffic
Capacity
2
Batch Processing
“Genetic Algorithm”
3
Best-Case Plan
What-if Analysis

15. The Genetic Algorithm
Intelligent and Fast
Inspired by nature
Finds near-optimal solutions very
fast
Follows principles of
recombination and mutation
Parallel capabilities

16. End User Interface

18. Combine with Machine Learning
1
Input consults
“trained model”
2
Predictive input
3
“Non-greedy”
results
Execution
Machine Learning
LSTM

19. New Value Proposition
 Increase tons per mile
 Decrease costs per mile
 Optimize and scale-out
 Cope with demand/capacity trends

20. Key Challenges: Quality of Input DATA

21. First ResponseInfrastructure Mobile Data Terminals
Other Use Cases – Emergency Management
Command & Control
Fixed
Assets
Surveillance
Radars
Fire Alarm
Fire Alarm
Mobile
Assets
Traffic
Management
Law
Enforcement
Fire Fighting

22. Police Response Time Target – 8 mins

23. Safe City in the UAE
Intelligent Command & Control

24. Thank You