Fredrik Kekäläinen (FI)
Fredrik Kekäläinen is the founder and CEO of Enevo, a Espoo, Finland based startup working on revolutionizing the global waste and recycling industry by using wireless fill‐level sensors, advanced analytics and routing algorithms to generate the most economical waste collection plans for entire cities. Enevo’s smart city waste management solution is currently used in over 35 countries and in some of the world’s most recognizable cities such as Amsterdam, Barcelona and London. The company has regional offices in UK, USA, Germany and Japan.
Enevo – How Technology Can Break Silos in the Public Waste Management Sector?
Today, the majority of public waste management operations in cities and municipalities are outsourced to third party private companies or run through public private partnerships. These organizations typically serve the city or municipality with long term multiyear contracts; servicing public areas such as parks, streets and buildings but also private residences from single family homes to large multi‐dwelling apartment complexes.
Typically, these organizations are working in closely protected silos with little or no transparency on their operations to the city or municipality they are serving. This causes business models and incentives in many ways to be backwards. Waste is collected on a static schedule with a fixed collection price, regardless of waste quantities generated and collected. This model is in many ways counter productive ‐ waste trucks end up driving around in public areas regardless if there is an actual need to service that certain area. The incentive for these organizations to increase efficiency in their operations does not exist, as revenues and quantity of services offered are set in fixed long term contracts.
Enevo is a Finland based startup company that is bringing technology and new processes to break the silos of traditional waste collection operations. We strive to provide full transparency to the way waste collection operations are run in cities and municipalities across the globe, while bringing the most efficient logistics operations into use; changing from a habit‐based operational model to a demand‐based.
Our method uses small wireless sensors to gather waste quantity buildup data from all waste bins in a city and utilizes predictive analytics to build trends that are used to schedule and route the most efficient collection operations possible. It provides direct economical, social and environmental impact by significantly cutting down operational costs, providing citizens with better service and hygiene (no overfull waste bins), generating lower levels of emissions and traffic congestion by reducing unnecessary driving.