This project will analyze large existing data sets concerning the usage and maintenance of equipment at the South Carolina Department of Transportation. The effort will assess SCDOT repair shop capability needs, capacity, skills, and tools/equipment. The analysis is expected to yield correlations and insights about the equipment, so that it can be maintained more effectively and at lower cost.

1. SOUTH CAROLINA
DOT EQUIPMENT
INSIGHT SYSTEM
Zhonghao Liu, University of South Carolina
Jianjun Hu, Ph.D. , University of South Carolina
John White, Director of Supply & Equipment,
South Carolina DOT
SCDOT INSIGHT

2. SCDOT INSIGHT

3. OUTLINE
Part I : Insight Into SCDOT Data
Part II: Future Work
SCDOT INSIGHT

4. INSIGHT INTO SCDOT DATA
Part I
SCDOT INSIGHT

5. WHAT WE HAVE?
SCDOT INSIGHT

6. WHAT WE WANT?
SCDOT INSIGHT
‘…I don’t know’

7. DATA THE UNSEEN GOLD MINE
SCDOT INSIGHT
Smaug is here

8. WHAT WE DID
SCDOT INSIGHT
SCDOT Equipment Insight System
(EIS)

9. EQUIPMENT INSIGHT SYSTEM
(EIS)
SCDOT INSIGHT
MySQL Database
Service Tickets Equipments
Repair
crews
Django based Web Server
Web Browser
Javascript
HTML
CSS
Machine learning and
Statistical analysis

10. FEATURES
 Big data business insights
 Visualization data to trend/insights/knowledge
 Data mining uncover expected or unexpected patterns
 Predictive analytics generate actionable decisions to optimize
business process (reduce cost or improve productivity)
 E.g. how to balance the work loading levels across repair shops
 How to assign repair tasks to most proficient shops
SCDOT INSIGHT

11. SYSTEM DISPLAY
SCDOT INSIGHT

12. EQUIPMENT
DETAIL ANALYSIS
SCDOT INSIGHT

13. SCDOT INSIGHT

14. EQUIPMENT DETAIL

15. SERVICE TASKS

16. USAGE VS MAINTENANCE

17. MAINTENANCE
ANALYSIS
SCDOT INSIGHT

18. SCDOT INSIGHT

19. SCDOT INSIGHT

20. SCDOT INSIGHT

21. SCDOT INSIGHT

22. OUTLIER
DETECTION
SCDOT INSIGHT

23. SCDOT INSIGHT

24. SCDOT INSIGHT

25. SCDOT INSIGHT

26. SCDOT INSIGHT

27. FUTURE WORK
SCDOT INSIGHT

28. FUTURE WORK
 Maintenance task scheduling optimization
 Repair shop efficiency prediction
 Prediction of equipment to retire

29. OUR NEXT MOVE: CLOUD-BASED EQUIPMENT
MANAGEMENT SOFTWARE AS A SERVICE
 Transport Equipment Management
 Work to your own schedule
 Maintain a clear eye on all DOT assets
 Cloud computing
 Keep in control, anywhere
 Success example:
 http://www.medixir.com/ for medical equipment management