Runner ups included a team making the airport taxi system more efficient and team trying to figure out why delays happen
SEER Analytics, a Singaporean data analytics startup, won the Rolls Royce Data Innovation Challenge held in Singapore today by pitching a machine-learning API designed to help make the airline industry more efficient.
As part of the victory, they will be taking home a S$50,000 (US$37,000) grant from Enterprise Singapore and a US$10,000 cash prize from Rolls Royce.
The runner-ups were ZestIoT technologies and Coffeeshop Teens, who walked away with US$2,000.
The competition was not a Demo Day but rather a test of the teams’ ability to complete a brief. They were given Rolls Royce data from the airline industry and were asked to solve problems like, “How can we optimise flight schedules?”, “How can we better manage engine maintenance?” and “How can we help pilots and flight planners make better decisions?”.
SEER won by presenting what amounted to a Google Analytics product for the airline industry. It had a full dashboard of information and analytics that could be used to drive decision-making.
“SEER Analytics had a very innovative approach to this challenge, brought a very different answer the question than some of the other competitors. We saw the potential for their idea, a machine-learning API, had an opportunity to empower the industry,” said Caroline Gorski, the Group Director of Rolls Royce’s data innovation initiative.
While the first innovation challenge in Singapore, it is not the first ever for Rolls Royce. Gorski said that over time they have realised that a core part of what makes a successful project is the quality of data that is provided by Rolls Royce.
“It is hard to do an innovation activity that actually drills down into the actual challenge problem. And the thing that makes the difference is the quality of the data you are able to make available and the support of the mentors that you can bring to help your startups understand the domain. Those two factors really make the difference.” she said.
Also Read: How can my startup participate in TOP100?
So let’s have a look at all nine companies that participated!
1. Innovative Binaries
Innovative Binaries built a tool to help airlines and pilots find the most efficient times for takeoff and departure in a given trip.
The platform gets into the specific flight details and uses information like weather patterns and airport statistic to perfect push-back times. The theory is these moments of hyper-efficiency can save significan fuel burn on the landing by making it less likely a plan runs into airports dealing with delays.
The company claims its platform has a 95 per cent accuracy rate.
2. Coffeeshop Teens
One of our runner-up teams has built a product “4”. Instead of trying to avoid delays, this product was trying to figure out “why” they happen.
By consolidating data from airlines, they can disseminate information the provides insights and help airports more accurately predict future delays and make decisions accordingly.
Actually, in this pitch, it wasn’t talking about airlines generally, but specifically the people behind operations at Changi Airport in Singapore.
An interesting tidbit about this team is that they are not an actual company as all four of them currently hold down corporate jobs.
Navigem is using data to help airports make extremely quick decisions, citing the statistic that delays cost the airline industry US$30 billion a year (and will keep rising),
It brought up the example of a large storm front coming into Singapore and relaying this data to ground support who can act accordingly.
It also helps with congestion blocking and queue optimisation.
4. Zero Build Stop
Based in China, this group represented the Rolls Royce employees in the competition.
It built a product for forecasting flight schedules to get the best plan and reduce uncertainty. Zero Build Stop is working towards a “one button solver” product because it want make the product intuitive for every day employees.
One of its clever features is a simple UI decision. By using a scatter plot, it helps users ignore flights that are performing at standard and focus on the high or low performers.
They stand out visually and it prompts the user to ask, “I wonder why this flight is so late? (or ahead of time)”. This helps operators pinpoint similarities in flights that perform beyond the mean.
drootoo is going after the engines themselves, helping companies pinpoint maintenance problems using data. The team built an output system that over time can optimise and predict maintenance scheduling.
The goal is to help improve planning, maximise utilisation and minimise risk.
It gathers weather data, flight data and where the aircraft is flying to try and create hyper specific information about the engine’s life.
6. Octad Lab
Octad Lab is the brainchild of a group of university students who built a scheduler model to avoid bad weather. The X – Y graph shows weather analysis of rain hours and when the user chooses the date, it shows the atmospheric conditions.
Engrav has really built solid aviation fleet management company. It boasts 35 corporate customers across 5 countries and is essentially an input-output database that can answer a ton of hyper specific questions about a company’s fleet.
The example shown during the pitch produced a 70-page report answering all sorts of questions, like crew availability, delays, cancellation and maintenance planning.
8. SEER Analytics
Our winner built a real-time machine-learning API that can be sold to airports to help them jumpstart their AI capabilities. With the SEER API, airports wouldn’t have to spend the money or time to build out the necessary data sets for a decent AI product.
The platform itself looked like a Google Analytics for Airplanes, allowing people to go into the data set and search for all sorts of information on an ad-hoc basis.
Our other runner-up, ZestIoT, wants to use big data to help improve the efficiency of the ground crew. It takes in various data over time and tries to work out ways that the taxi process can be sped up.
ZestIoT tries to accurately predict landing times so that the ground crew can kick into action, and quickly turn around the plane or takeoff.
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