Creating Value<\/h2>\n\n\n\n
Rubicon helps cities and waste management collection companies create value by using AI, machine learning, and a vast array of sensors. To create this value, Rubicon has sensors mounted on various parts of the trash truck (including the breaks, steering wheel, and mechanical trash compacting parts). There are also cameras mounted on the truck that take in video feed. The tape is analyzed by AI-enabled image-recognition software to identify things on the road. Rubicon offers haulers an app that helps communicate the insights to drivers in real time and also a dashboard offering to help company management plan based on the insights.<\/p>\n\n\n\n
They take this data and use AI-enabled analytics to reduce costs for cities by strategically picking up garbage only when needed which reduces fuel consumption, increasing collection rates which reduces redundant trash runs to pick up \u201cmissed\u201d garbage collection, and also provides information directly to the city about the real-time status of the road conditions, graffiti, tree branches, and trash patterns among other data points. This service also helps municipalities better plan for reoccurring events such as parades, concerts, and protests to estimate the types of clean up they will need in advance instead of having to react to the clean-up requirements after the fact. They are also piloting a service to help identify when recycling has been mis-sorted to correctly inform and eventually fine households that continue to put excessive trash in the recycling streams.<\/p>\n\n\n\n
The service also creates value for trucking companies that are part of \/ contract with the waste management ecosystem by reducing wear and tear on the equipment through a preventative maintenance program. The software helps trucks avoid areas with major road damage, further protecting equipment as well as provides safety updates and fuel metrics. By using AI and long-term data analytics, the company can better predict when a truck is going to be full and preemptively send out an additional truck to take over the route. This saves time, optimizes the process, reduces labor usage, and leads to higher customer satisfaction. Additionally, having more insight into what is in the incoming recycling stream allows MRFs (Materials Recovery Facilities) and other members of the value chain to also operate more efficiently by reorganizing their sorting lines, re-deploying labor to different sorting streams faster, and rejecting loads with too much garbage before sorting. All these improvements help improve margins for members across the value chain \u2013 making recycling more economical. This value creation also does social good in that by making recycling more economical, more companies and cities are likely to invest and use the recycling systems which is overall environmentally positive.<\/p>\n\n\n\n \u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Rubicon charges haulers (waste management trucking companies) for the services and app at $190 per truck after a 3-month free trial. Most haulers actually use the app and companies (including their municipal stakeholders) find immense value in the app over those 3 months and typically convert into paying customers. When there are enough haulers in a city that sign up with Rubicon, municipalities are then more incentivized to buy a license for their whole city and their city\u2019s fleet. Contracts are often multi-year, insulating the company from competitors and building customer loyalty while also using the data to further develop and refine the AI technology.<\/p>\n\n\n\n Municipalities have strict contract rules and guidelines they need to follow. Getting in and securing contracts with governments can be a lengthy process and often requires a highly defendable ROI to secure the contract. Thankfully for Rubicon, the company has many contracts that can gives new customers confidence in their product and its value creation potentials. \u00a0<\/p>\n\n\n\n Challenges related to the AI include identifying trash from recycling as there is so much variance in what is coming through the stream, managing technology costs as higher accuracy AI involves faster frame capture which is more expensive even if it leads to higher accuracy, and pushback from incumbents who fear job losses\/disruption in their industry if the city \/ company adopts Rubicon.<\/p>\n\n\n\n The company is likely going to expand their range to other municipality-based services such as snow plowing, road salting, street cleaning, water metering, identifying illegal dumping locations, and storm clean-up. They are able to leverage their on the ground route data collection with AI-enabled visual interpretation to provide on-the ground information for cities. Municipalities have limited resources and personnel to execute on all the city needs \u2013 often requiring some city services to be delayed for long periods of time leading to unhappy and inconvenienced constituents. Using technology and quickly identifying issues will help municipalities better serve their constituents and more effectively use their limited resources.<\/p>\n\n\n\n The company is doing incredibly well and has both the funds and the technological capabilities to execute on many of their opportunities. As well, they have a well proven and tested ROI that makes customers trust and contract with them. They have very limited customer churn and are a leader in the space. All this leads me to believe that the sky is the limit for this company. They could partner with more advance technology and AI processers to make their products more efficient and better at recognizing objects\/distinguishing objects from each other. They could also more aggressively pursue the complimentary opportunities outside of the recycling and waste management to get into \u201csmart city\u201d opportunities and become the go-to \u201csmart city\u201d software provider. Or\u2026. they can move into outer space clean-up!<\/p>\n\n\n\n Waste and recycling get’s smart – moving towards smarter cities!<\/p>\n","protected":false},"author":19333,"featured_media":17555,"comment_status":"open","ping_status":"closed","template":"","categories":[],"class_list":["post-17552","hck-submission","type-hck-submission","status-publish","has-post-thumbnail","hentry"],"connected_submission_link":"https:\/\/d3.harvard.edu\/platform-digit\/assignment\/machine-learning-3\/","yoast_head":"\n![\"\"](\"https:\/\/d3.harvard.edu\/platform-digit\/wp-content\/uploads\/sites\/2\/2022\/11\/rubicon-truck.jpg\")
<\/a><\/figure>\n\n\n\nMaking money<\/h2>\n\n\n\n
Challenges<\/h2>\n\n\n\n
Opportunities <\/h2>\n\n\n\n
What’s next?<\/h2>\n\n\n\n
Sources<\/h2>\n\n\n\n