Cisco PM Case Competition

1. Cisco PM Case Competition Team: The Sparkly Destroyers

2. Our Team Lynette Chen Raphael Rangel Ukemeobong Owoh Elisa Zhao

3. Executive Summary The objective is to increase access and information across vehicles sharing the roads to increase safety and decrease emissions across the US. Our proposed solution is a to create a platform that uses Cisco’s technology to connect all the stakeholders in and out of the road. Device manufacturers, vehicle manufacturers, cities, partners and drivers will each be connected to a centralized Saas and IaaS system, and able to add-on services. That will enable Cisco to reap the benefits of network effects that makes the platform increasingly useful, precise and efficient.

4. User Personas Ukeme - Head of Strategy of Honda • Looking for strategic partnership to help Honda to enter the semi-autonomous market before the deadline • Wants to find the best strategy for continued growth of Honda. • Wants a solution that will set Honda apart from competitors. Raphael - Fleet Manager for Amazon • Oversees truck purchases and the efficiency of the company’s fleet. • Is frustrated with his existing fleet management system which does not integrate with new technology well. • Compensation is tied to fleet efficiency metrics such as fleet availability rate and on-time completion rates. • Wants to minimize operating costs and optimize company revenues. Lynette - Chief Engineer of Honda • Is looking for a software solution that will aid in the move to semi autonomous. • Wants to find a partner that will be easy to integrate with and reliable. Elisa - Chief Engineer1 of Daimler • In charge of transitioning all existing truck models to meet the semi-autonomous requirements. • Is looking for a solution that will ensure that the company’s trucks do not become obsolete without having to invest heavily in R&D. She is frustrated by the short timeline 1- Chief engineer in the automobile industry is similar to a product manager within tech. He works with engineers, designers, finance and senior management and has end-to-end ownership of the car

5. Use Cases Trucking • As the fleet manager, Raphael wants a system that is able to help optimize fleet logistics by minimizing downtime, maintaining vehicles, and increasing fuel efficiency in order to cut costs and maximize profits for the company • As the Chief Engineer, Elisa wants a method to quickly ensure that the existing truck models are semi-autonomous and safe so that the company meets the legal requirements Consumer Vehicles • As the chief engineer, Lynette wants to build a next generation car with a software AI component so that she can delight customers • As the head of strategy, Ukemeobong wants to invest in a winning strategy so that Honda continues to grow in size and in profit

6. Problem Statement Sources: 1 - https://www.cbinsights.com/research/autonomous-driverless-vehicles-corporations-list/ 2 - https://ecapital.com/trucking-company-profit-margins/ 3 - https://www.trucks.com/2018/10/04/large-truck-fatalities-29-year-high/ 4- https://www.ft.com/content/39c01b56-9be5-11e9-9c06-a4640c9feebb, 5 - https://www.fastcompany.com/90374083/for-years-automakers-wildly-overpromised-on-self-driving-cars-and-electric-vehicles-what-now 5 - see appendix slide 40+ - Over 40+ corporations are working on self driving vehicles. This can, potentially, make for redundancies in backend server infrastructure and waste of resources at an industry level1 80% - Large commercial vehicles and consumer individuals vehicles are contributing to 80% of transportation emissions5 1,800 hours - The average long haul idles about 1,800 hours per year, using about 1,500 gallons of diesel2 A lot of automakers are forming partnerships on Semi AVs, yet these partnerships often lack a IoT specialist within the play4 Fatalities from big truck crashes continue to rise (9%) even though the overall traffic fatality rate declined (2%)3 Traditional car manufacturers are playing catch up on the self-driving cars race. Many are struggling technologically5

7. Proposed Solutions (1/2) Our solution will be to expand on existing Cisco Kinetic capabilities, bundling the system with partner devices and city data access to allow for a seamless and efficient transition to equip all vehicles for truck and car manufacturers with the software and infrastructure to meet semi-autonomous legal requirements. Solution Logic Flow (Numbers reference diagram on the left): 1. Provide smart edge software designed for semi-autonomous vehicle automation to car/truck manufacturers e.g. Daimler 2. Manufacturers in turn share data with Cisco and contribute to industry data lake 3. Provide smart edge software designed for semi-autonomous critical devices to device makers e.g. Quanergy 4. Device Manufacturers in turn share data with Cisco and contribute to industry data lake 5. Collect city data (e.g. traffic light data) from partner cities 6. Provide cities with anonymized traffic and semi AV data 7. Offer vehicle benchmarking services to fleet manager and car owners who are already on Cisco software for analytics and real-time usage on the go Sources: https://www.cisco.com/c/en/us/solutions/internet-of-things/iot-kinetic.html

8. Proposed Solutions (2/2) Why we’re proposing this: We want to increase access As cities are already partnering with Cisco through the City Infrastructure Financing Acceleration Program, Cisco has the unique advantage to be able to easily connect the city sensors to the infrastructure for manufacturers to use. Shared information between the city and manufacturers will be mutually beneficial for traffic congestion. Partner devices crucial to customer needs such as Quanergy’s LiDar sensors and ContiConnect’s tire sensors will come integrated with Cisco’s platform and additional devices can be seamlessly added to the gateway. A unique benefit of being on Cisco’s system once we have the majority of manufacturers on board will be the ability to compare your fleet or vehicle’s data with those of other players. Such sharing of information for benchmarking purposes will help to move the industry forward as a whole. Our solution will be able to move the industry forward

9. Business Model Viability • Cisco is able to make $2.5billion. The cost of developing IaaS and SaaS solutions are capitalised to the balance sheet • Giant car manufacturers (e.g. Daimler, VW) will pay on average $25million/year for access to all Cisco’s Software and data centre facilities. Cisco will not charge a fee for smaller car manufacturers/startups with revenues <$500 million/year • Ancillary device makers (e.g. Lidar manufacturers) will pay a highly discounted $10million/year for the ability to use Cisco’s software and IoT facilities. • CIsco will offer the platform to cities for free as an incentive to seed the platform A 5-year financial forecast is shown in the appendix “Business Viability”

10. Assumptions & Risks Assumptions • All manufacturers are responsible for implementing their own hardware solutions • Sensor technology is reliable and durable1 • 5G network is secure and widespread2 • Network and database are able to handle the velocity and amount of data volume being sent for ingestion3 • Network effect is significant once major manufacturers are on the system • Assume growth in revenue in line with inflation6 Risks • Lack of connectivity in certain situations and thus solution should also leverage Cisco’s fog computing solutions4 • Amount of data will explode exponentially - Cisco will need to continue to scale infrastructure to mitigate risks • Increased security threats from hackers - Cisco will need to continue to develop its IOT Threat Defense solutions5 Sources: 1- https://arstechnica.com/cars/2018/01/driving-around-without-a-driver-lidar-technology-explained/ 2- https://www.zdnet.com/article/how-5g-can-help-unlock-iots-potential/ 3-https://blogs.cisco.com/sp/connected-car-all-that-data-cost-and-impact-on-the-network 4- https://www.cisco.com/c/dam/en_us/solutions/trends/iot/docs/computing-overview.pdf 5- https://www.cisco.com/c/en/us/solutions/internet-of-things/iot-security.html#~stickynav=1 6 - https://www.bls.gov/Cpi/

11. Appendix

12. Requirements - MVP Ability to connect seamlessly with: • Advanced LiDAR sensors for collision-avoidance and safety • City traffic sensors for current and predicted future conditions to inform routing • Weather conditions data to inform safety precautionary measures and routing • Car diagnostics to minimize breakdowns • Tire pressure sensors to increase fuel efficiency Display up to date, real time map and traffic conditions by leveraging partnership with Google (Alphabet) • Ability to use existing mapping technology • Ability to use existing autonomous driving data to power machine learning Ability to integrate consumer feedback into the machine learning cycle immediately • For events that happen as a driver is operating a vehicle, integrate that data point into the algorithm so that it can start processing immediately. (Ex. accidents on the street) • Ability to integrate voice processing input into system

13. Emissions Sources: https://www.epa.gov/greenvehicles/fast-facts-transportation-greenhouse-gas-emissions

14. Solutions Brainstorm Low ease-High BV High ease-High BV Fuel efficiency and emissions Weather conditions Health devices Planning routes and outcomes Car physical conditions Traffic conditions Smart home ecosystem Business Value (BV) Entertainment` Low ease-Low-BV High ease-Low BV Ease of implementation

15. Business Viability Sources: Cisco, Bureau of Labor Statistics