CapMetro
MISA Spring Case Competition
Placed FirstIn this case competition, as a team of four, we determined recommendation to resolve CapMetro’s issues of decreased ridership and disatisfied consumers.
This is a brief of look at our recommendation.
The Case
We started off by analyzing the provided case materials and researching the transportation industry. Through finding patterns in the data, we were able to determine that the issue lay within the specific segment of the suburban commuter.Looking at the provided emotional index of the suburban commuter, we could see their painpoints lied within home ︎︎︎ station, transit, and transit ︎︎︎ destination. Thus, we’d have to craft our solution to target these areas.
The Solution
For our recommendation, we decided to create a partnership with cruise, an autonomous, fully electric, self-driving car company. By stationing these in suburban neighborhoods to bring commuters to bus stops, our solution can mitigate the pain point of commuting from home ︎︎︎ station. Along with this, we decided to pair it with a transformed app to allow users to easily schedule a ride to their house.
With the app, users can both schedule and automate their rides ahead-of-time to improve the user experience with predictability and convenience. Along with this, they’ll stay updated on throughout their entire journey to make riding with CapMetro seamless and stress-free. On the other side, CapMetro can use the the app can collect data on its consumers to gain a better understanding of them and improve the quality of its service. These implementations can be applied especially to the transit portion of the ride.
With this soultion, we developed a framework to encapsulate each impact it creates. Our recommendation tackles the CORE - CapMetro’s customers, operations, revenue, and eco-friendliness.
As mentioned before, the parternship with cruise and the app work together to resolve consumer pain points, establishing the C in CORE. For Operations, we recommended CapMetro integrate all of their platforms on one cloud-based software in order to easily access their data. They can then apply AI algorithms to track patterns in order to increase efficiency and improve operations. As for Revenue, our team member studying finance calculated the return on investment. Lastly for Sustainability, we calculated a rough estimate of how much CO2 emissions could be saved through implementing this solution.
After establishing these values, we then created an implementation plan.
Lastly to end our recommendation, we addressed risks, and also included mitigants for those risks.
Outcome
Overall with our recommendation, our team, WAM, ended up taking first in the case competition.