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Michael N. Ferrara, Jr.Michael N. Ferrara, Jr.Sr. VP Field OperationsSr. VP Field OperationsFebruary 17, 2009February 17, 2009
Predictive Analytics for Fleet Safety
Over fifty years of combined experience in the transportation industry.
Industry leader in predictive modeling for transportation safety.
Very broad client base.
Intelligent Intervention Systems.
Minimize Effort, Maximize Results
FleetRisk Advisors
A leveled approach
Calibration, Planning and Customizing are the keysCalibration, Planning and Customizing are the keys
What is it…
Innovative and sophisticated use of statistical and quantitative data to build predictive models that drive human decisions, and fully automated decisions, to predict future events and dramatically improve performance.
Predictive Analytics
Some perspective…..
In business, as in baseball, the question In business, as in baseball, the question isn’t whether or not you’ll jump into isn’t whether or not you’ll jump into analytics; the question is when! Do you analytics; the question is when! Do you want to ride the analytics horse to want to ride the analytics horse to success…..or follow it with a shovel?success…..or follow it with a shovel?
Rob Neyer, ESPNRob Neyer, ESPN
Analytics will be the gold rush of the next Analytics will be the gold rush of the next several years, and many companies will be several years, and many companies will be assiduously panning their data for assiduously panning their data for treasure.treasure.
Michael Treacy, cofounder, GEN3 PartnersMichael Treacy, cofounder, GEN3 Partners
Every 30-90 days you knew which drivers were most likely to have an event, or type of event, with a 65-85% accuracy rate before they happen?
What if I told you that the driver does not have to go online, fill out any forms, take any tests or submit any personal data that would violate any company policy or privacy regulations?
What if you knew whether it was a personal, professional or skill issue that was impacting the driver.
What if there was an intelligent intervention system that could effectively manage the issue?
What if……
Modeling PlatformModeling Platform
Analytics Engine
Utilize your data for a 2nd generation ROI!
HR
OBC
MVRTelematics
Maintenance
Claims Data
Vehicle Type
Traffic
PopulationOther
Weather
Fatigue
© 2008 QUALCOMM Incorporated. External presentation to (audience), prepared by Qualcomm’s (presenters name) – Month Day, 2008.
8
7 8 9 10 4 5 6 1 2 3
Parking & BackingModel 3: Parking/Backing/Collision
Incidents per Year Assigned
-
0.02
0.04
0.06
0.08
0.10
0.12
Nu
mb
er
of
Inc
ide
nts
1 2 3 4 5 6 7 8 9 10
Over The Road CarrierTrip Distance variability
Net Payroll amount
Age of Vehicles
% of training done on time
Gasoline HaulerFatigue Score
Hard Brakes
Idle Time
Monthly average speed
Commercial FleetYears Driving Experience
Vehicle age
Sales performance
Zip code
Predictive Data at Work
RetentionService Failures
Monthly payroll amount
Take home percentage of pay
Total miles prior 6 months
Recruiting Avg. number prior employers
Longest prior tenure
Count of W/C claims
Real ResultsReal Results
We love gain share opportunities!
A Predictive Model’s Capabilities
Accidents Severity Turnover Productivity
23%
81%
49% 12
%
The Target -
Likelihood of drivers to have a worker comp claim in January Based on Decembers data.
The Result –
3 out of the 8 worker comp claims were made by drivers identified in the worst 1%.
A model built on 2 years of historical data:
Workers Comp Summary
Change from:
What happened?How many and where?What do we think the problem is?What do you think we should do about it?What’s the worst that can happen?
Change to:
Why is this happening?What does our data really say?What will happen next?How do we stop it from happening?What’s the best that can happen?
Change Management
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