acerta deck
TRANSCRIPT
Real time analysis of vehicle data to diagnose and predict failure
Complexity of vehicles is growing exponentially
Existing diagnostic tools cannot keep up
Issue Impact
Cyber security vulnerabilities
Recall ~1.4 Million Cars
Software incompatibility between Electric Vehicle control unit and battery control module may cause propulsion system to shut down
Recall ~5,600 Electric Cars
Software flaw may cause the hybrid system to shut down while driving Recall ~1.9 Million Hybrid Cars
Flaw in the continuously variable automatic transmission software may subject the drive pulley shaft to high stress
Recall 143,000 Cars in the U.S.
RECALLS
Source: Various news outlets and manufacturer�s websites, McKinsey & Company
SOLUTION
A platform that uses machine learning and statistical analysis to detect anomalies and
predict failures in real time for automotive vehicles
Manufacturing Advanced analytics that detect problems in vehicles before they get on the road
Fleet Managers Real-time monitoring of the vehicles on the road to reduce warranties and maintenance costs
SOLUTION
Growing complexity More sensors and electronics
Connected vehicles
Advanced and autonomous software features
DTC (Diagnostic Trouble Codes) that set off the Check Engine Light
Trouble codes are becoming less informative and less reliable
More data
More vehicle data is being collected, both during manufacturing and on the road, than ever before
WHY NOW
*source:IHS;Autofacts;Frost&Sullivan;KPMG;HBR;Bain;McKinsey;NHTSA;Technavio;NaBonalAutomobileDealersAssociaBon;OEMreports;Capgemini;ThomsonReuters;Gartner;OxfordEconomics;Strategy&analysis
THE GLOBAL AUTOMOTIVE MARKET $150B
90M New cars
produced per year
=
$1,665 Software supplier revenue
per car manufactured
Vehicle sales $2.45 Trillion
Insurance, financing, aftermarket $1.7 Trillion
Supplier $0.85 Trillion
Software $150 Billion
Hardware $700 Billion
Collaboration
Structured POC to demonstrate diagnostics and prognostics capability.
POC (Proof of Concept)
Develop a prototype to integrate the analytics and monitor select vehicles.
PILOT EXPAND (SaaS)
Expand prototype into a full deployment by increasing the number of monitored vehicles.
Validation Integration Deployment
BUSINESS MODEL
Geared for automotive systems (focused and validated with vehicle components)
Works with any vehicle model (not exclusive to single manufacturer or product)
Scales with vehicle complexity (support thousands of signals from growing number of sensors and components)
Support any OBDII hardware & data (analysis adapts to data from a variety of data collection hardware)
Rich data (more than your typical OBDII data)
Leverage manufacturing data (data from newly manufactured vehicles and their malfunctions)
Manufacturers Telematics General AI
COMPETITION
Greta Cutulenco, CEO § Software Engineer, University of Waterloo
§ 2 years of research experience in embedded and real time systems as part of her master�s
§ 3 years of experience working with software systems in automotive, aerospace, and nuclear fields
§ She has worked at Magna, AECL, and Qualcomm
Sebastian Fischmeister, Chief Scientist § PhD, PEng, Computer Science
§ Associate Professor, University of Waterloo
§ Associate Director of WatCAR
§ Over 16 years of research experience
§ Extensive experience managing a team of over 15 people and working with million dollar budgets
Jean-Christophe Petkovich, CTO § Master of Computer Science and PhD candidate in
Computer Engineering, University of Waterloo
§ 5 years of research experience in the fields of statistics and machine learning
§ Significant contributions in the open sourced software community
§ He has worked at QNX and Bombardier
Gonen Hollander, COO § MBA, Rotman School of Management, University
of Toronto
§ 7 years of navy service, as Missile Ship Tactical Officer and Basic Training Team Leader
§ Over 6 years of management and leadership development
§ Experience with operations, business strategy and business development
Prashant Raghav, Chief Data Officer § Master of Computer Science, University of Waterloo
§ He has worked at Amazon, SAS, and Lenovo
§ Extensive experience in scaling analysis for BigData
§ Over 4 years experience with distributed systems including Hadoop and Spark
Himesh Patel, Marketing & Business Dev. § Management Engineer, University of Waterloo
§ Experience in managing product development and optimizing internal company processes
§ He has worked with Whitehat Security, Broadcom, and D2L
TEAM
Mike Donoughe § 25 years VP at Chrysler overseeing
manufacturing and engineering § EVP at Tesla Motors overseeing vehicle
engineering, manufacturing, quality, and supply chain
§ C-Level executive with extensive experience leading companies in all phases of execution
Chris Kondogiani § 15 years of leadership experience with
Fortune organizations, startups, and new ventures
§ Over 10 years experience with vehicle engineering at Chrysler
§ MBA with Finance and Marketing focus
ADVISORS
