The document summarizes a hackathon project called "Cognitive Drivers" that aims to optimize underground loader operations for a mining company called Newcrest. The team, consisting of students and professionals from various fields, developed a prototype that uses real-time data to help loading drivers perform at their optimum speed and output, which could save the company over $300 per hour in maintenance and fuel costs based on their 30 million tonne annual output. The prototype and approach may also be applicable to improving other mining processes and challenges.

1. Hackathon 2017
Newcrest Challenge #3:
Underground Loader Optimisation
Chilumba Machona
John Vial
Jon Morgan
Jamie Gough
Tung Van Truong
Business IT Student
Data Scientist
Mining Engineer
Mining Engineer
User Experience Designer
TEAM: COGNITIVE DRIVERS

2. Problem Statement:
What is the difference between good
driving vs inefficient driving?

3. What it means to Newcrest
~ $ 300 hr cost of maintenance
~ $ 75 hr cost of driver
~ $ 50 fuel burn
Cost assumptions
Annual output
30 Million tonne

4. The key data points
we need to track.
Drawpoint Optimum speed and volume output

5. PROCESS
Hypothesis, validation and rapid iteration
version 1.2version 1.1

6. How might we use real-time data
to help loading drivers perform
at their optimum?

10. Prototype

11. autonomous vehicles
application to other processes
and challenges within society
ScalabilityReapplication
What’s next

12. COGNITIVE
DRIVERS
Chilumba Machona
Business IT Student
Jon Morgan
Mining Engineer
Tung Van Truong
User Experience Designer
Jamie Gough
Mining Engineer
John Vial
Data Scientist

13. Appendix

14. Appendix

15. Prototype demo

16. Prototype