Up to 2050, the world’s urban population is expected to double, according to the European Commission. As a result, globally the sheer volume of waste produced is estimated to double over the next ten years. The insurmountable increasing waste flow resulting from this requires sustainable waste collection and processing. To create liveable cities as populations grow asks for municipals and waste management companies to create a sustainable environment and infrastructure.
In today’s circular world waste is treated as a resource. Leading waste management companies in the municipal waste industry are actively developing new and more sustainable solutions for dealing with household waste. This is supported with state-of-the-art technologies enabling organizations to be agile, reduce environmental impact and at the same time provide the best possible customer service
Most importantly these innovative technologies contribute to ‘smart city concepts. For instance, sensor-based containers and wheelie bins equipped with RFID tags provide visibility into waste collections enlightening how to improve processes to meet both sustainable, operational, and customer demands.
Innovative technology contributes to reducing mileages and CO2 emissions
An example of this contemporary waste collection is Omrin who with innovative technology reduced its environmental impact by lowering mileage and cutting back CO2 emissions. The household waste collector and recycler in The Netherlands has achieved that result with intelligent optimization which combines super fast and smart algorithms with IoT (Internet of Things) to optimise routes. Omrin deploys intelligent optimization for the so-called reversed waste collection.
Collection routes are optimized based on the fill level data of the in-built sensors exchanged with the centralized system. As a result, due to a more efficient collection, the number of times underground containers are emptied has fallen significantly. There also has been a sharp increase in the fill level, from 60% to 80%. The average content of waste in the containers when they were emptied went up from 260 kilos to 300 kilos. Therefore, on balance, dynamic planning saves a considerable number of trips, so vehicles can be used elsewhere. Moreover, the planning process can be speeded up, leaving planners more time for other work.
Tackle increasingly complex planning demands
Intelligent optimization also enables household waste collectors to tackle increasingly complex planning demands. For instance, Germany’s second-largest recycling company can automate the optimization of the routes and digitize both the paperwork and the communication between the planners and drivers. With this, the interaction has become faster and more direct while at the same time also being able to meet the highest quality demands. With route and time as the main parameters, the route planning software helps the German recycling company to achieve ideal planning results which can then be implemented reliably on routes.
Because of the precise and super-fast algorithms, the planning is also of a high accuracy which considerably reduces planning time. Additionally, for the leading German company, it is also is the answer to the challenge of being short-staffed in drivers. Due to optimal route planning, the number of drivers available can be deployed effectively and efficiently on the available vehicles. The solution also provides greater convenience and support for drivers with less stress and fewer risks of accidents thanks to additional aids of guided navigation which leads drivers through their routes and guides them to any waste collection point.
AMCS Platform for Municipal and Residential Waste
AMCS’ Platform for Municipal and Residential Waste includes state-of-the-art technology solutions for household waste and recycling collection, route planning, vehicle technology, card management, and customer service support. The AMCS Platform also provides full support for municipalities that subcontract collections with service-level monitoring, dynamic reporting, and analytics.
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