How fleet advantage analytics uses predic engine and iot with machine learning

1. How I helped Fleet Advantage analytics= Using Predix data lake engine and IoT for analytics,
preventive maintenance and tracking visualization software ATLAAS.
As part of turnkey asset management solutions, Fleet Advantage monitors the fleet by individual vehicle
and groups of vehicles to determine the most cost-effective lifecycle and fleet optimization plan.
ATLAAS (ADVANCED TRUCK LIFECYCLE ADMINISTRATIVE ANALYTICS SOFTWARE)
and its easy-to-use interface gives fleet executives all of their pertinent fleet information with their data
analytics and visualizations they need to manage their fleet with a few keystrokes, on one platform,
without the need for a support team of analysts or data scientists.
Disruptions happening in the commercial vehicle business=
a) Telematics=large source of data that we collect. Drivers to provide electronic logs of when they are
driving
b) Autonomous driving
c) Electrification of vehicles
d) Automating the inspection process of vehicles and preventive maintenance
Key KPIs and using Hadoop and Cloud including challenges encountered and lessons learned
a) Sources of data
b) Consumers of data
c) Improve the digital supply chain
d) Vehicle servicing for any commercial vehicle on the road-Data collection devices installed on
vehicles but no method to process this data
e) Birth of big data platform and then migrating it to Cloud= Use agile-fail fast methodology for trying
out innovative solutions
f) ATLAAS connection=preventive maintenance, towtruck, what is required. Partnered with 27
telematics data service providers to get the data points, normalize that data and generate a health
report. Now we have ATLAAS. ATLAAS connection is a dashboard which captures all data points
across vehicles=2 PB of data in a month and generates health reports of the vehicle. Now there are
475,000 vehicles sending data regularly to our own telematics device ATLAAS. Collecting data
every 11 seconds and now targeting 4 secs using IoT.
Results=
a) Down days=28% reduction in down days
b) 31% reduction in repairs for preventive maintenance and remote diagnostics with the health report
c) Maintenance costs=reduced to 4 cents per mile well below industry average of 15 cents per mile
d) By converting OnDemand maintenance to predictive maintenance, maintenance budget reduced by
30%
e) Reduce roadside breakdown, towing and other costs and better uptime-so now roadside breakdowns
reduced to less than one per month.