Sensoneo Smart Analytics: Revolutionizing Waste Management
Summary
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Details
Deep dive
Software overview
"Discover just how efficient is your waste collection today"
Source: sensoneo.com
Sensoneo Smart Analytics is an advanced data-driven solution designed to optimize waste management by providing deep insights into waste collection operations. This tool is part of Sensoneo’s broader smart waste management platform. It utilises data from its sensors and software to offer detailed analysis and performance metrics that help cities, waste collection companies, and businesses improve efficiency and sustainability.
Key Features
Smart Analytics gathers and analyzes data from Sensoneo’s smart sensors installed in waste bins, providing real-time insights into fill levels, collection frequencies, and operational efficiency. This allows waste managers to make data-driven decisions that optimize collection routes and schedules.
The platform tracks key performance indicators (KPIs) such as bin fill rates, collection accuracy, and service efficiency. This helps identify underperforming areas and opportunities for improvement, enabling waste managers to refine their operations continually.
Sensoneo Smart Analytics uses historical data to forecast future waste generation trends and predict bin fill patterns. This predictive capability helps plan ahead, avoid overflows, and adjust collection schedules dynamically to meet demand.
The platform measures the environmental impact of waste collection activities, including CO2 emissions and fuel consumption, allowing companies to monitor their sustainability goals and make necessary adjustments to reduce their carbon footprint.
Users can generate customized reports that provide detailed insights into their waste management operations. These reports can be tailored to specific needs, helping stakeholders understand the impact of their decisions and plan improvements accordingly.
Smart Analytics identifies inefficiencies in waste collection operations, such as unnecessary pickups or missed bins, and provides recommendations to streamline processes, reduce operational costs, and enhance service quality.
Benefits
Cost Reduction: By optimizing routes and collection schedules, Smart Analytics helps reduce fuel consumption, labour costs, and vehicle wear and tear.
Improved Service Quality: Provides actionable insights that help enhance waste collection reliability and responsiveness.
Sustainability: By minimizing unnecessary collections and reducing emissions, Smart Analytics supports more sustainable waste management practices.
Enhanced Decision-Making: Empowers waste managers with data and analytics, turning complex data sets into easy-to-understand insights for strategic planning.
Sensoneo Smart Analytics is a powerful tool that helps transform waste management into a more efficient, sustainable, and data-driven process, supporting the broader goal of smarter cities and greener communities.
Technology
Sensoneo uses advanced technology, primarily IoT (Internet of Things), smart sensors, and data analytics, to optimize waste management. Their sensors, installed in bins, use ultrasonic technology to measure fill levels and send data in real time to a cloud-based platform. The platform uses this data to generate insights and optimize collection routes, schedules, and frequencies.
Unlike traditional waste management solutions, Sensoneo’s ultrasonic sensors are highly durable and provide precise data on waste fill levels, temperature, and movement. Sensoneo’s integration capabilities with other city systems and their focus on sustainability further differentiate them from others in the market.
Case study: Prague, Chech Republic
The City of Prague implemented Sensoneo's smart waste management system as part of the Smart Prague 2030 plan. Starting in 2018, the project installed sensors in underground waste bins to monitor fill levels, optimizing waste collection routes and improving efficiency. By 2023, over 1,900 sensors were deployed, integrating data into Prague’s official platform for predictive analytics. The project reduced waste collection frequency and costs while enhancing environmental sustainability.
