{"id":312,"date":"2026-02-24T09:18:14","date_gmt":"2026-02-24T10:18:14","guid":{"rendered":"https:\/\/gokupara.net\/?p=312"},"modified":"2026-03-02T10:18:49","modified_gmt":"2026-03-02T10:18:49","slug":"smart-waste-smart-decisions-how-data-is-transforming-waste-operations","status":"publish","type":"post","link":"https:\/\/gokupara.net\/index.php\/2026\/02\/24\/smart-waste-smart-decisions-how-data-is-transforming-waste-operations\/","title":{"rendered":"Smart waste, smart decisions: How data is transforming waste operations"},"content":{"rendered":"
\u00a0<\/div>\n

\"Smart<\/h4>\n

Jessica Bradley speaks to industry leaders about how data is transforming waste operations.<\/h4>\n

AI technologies are transforming the way waste is processed. From AI-assisted route optimisation and sensor-led collections to digital waste tracking and automated facilities, data-driven technologies are reshaping waste management operations.<\/p>\n

Many local authorities and waste management companies are taking the leap and investing in data-driven technologies, with the aim of achieving improved efficiency and stronger compliance as a result.\u00a0<\/p>\n

However, some limitations and costs remain, as well as the lingering question of what the future holds for humans working in waste management.<\/p>\n

How AI is changing the waste industry\u2019s daily operations<\/h2>\n

There are many ways real-time data is changing daily decision-making on the ground. For local authorities, this looks like improved insight into waste generation patterns and behaviour.\u00a0<\/p>\n

\u201cThe biggest task we use data for as a local authority is optimising waste collection regimes,\u201d says Dave Atkinson, director of environmental and regulatory services at City of York council, a unitary authority with waste collection and disposal services.\u00a0<\/p>\n

Optimising its waste collection regime can bring numerous benefits for councils, including reduced operational costs, carbon footprint and traffic congestion, and increased recycling rates and improved service levels. But doing this without data-driven technologies can be hugely challenging.<\/p>\n

\u201cThere\u2019s so much data involved with waste because you have, say, 90,000 properties and hundreds of thousands of segments of road, and you\u2019re trying to work out the best way of getting a [refuse collection] vehicle around them all,\u201d Atkinson says.\u00a0<\/p>\n

AI is also helping businesses with Digital Waste Tracking (DWT), which requires huge operational shifts. Progress has been made since the government first announced mandatory DWT ambitions in 2018 as part of its Resources and Waste Strategy, which aimed to provide a \u2018comprehensive way\u2019 to see what\u2019s happening to the UK\u2019s waste.<\/p>\n

The legislation focused on commercial and regulated waste activities, with operators expected to record information for each movement into permitted sites.<\/p>\n

In February this year, Defra confirmed the mandatory rollout for phase two of DWT will take place in October 2027<\/a>. This means reporting through DWT will become compulsory for all required operators across the waste supply chain.\u00a0<\/p>\n

The Rail Safety and Standards Board (RSSB) has been working with engineering and environmental consultancy Ricardo to help the industry transition. It has developed metrics to help Britain\u2019s rail industry measure performance across the circular economy, waste management, and resource management.\u00a0<\/p>\n

Both organisations are now working with rail firms to pilot the implementation of data collection plans and metrics<\/a> for the industry\u2019s waste management. The RSSB says the information gathered using these metrics would help to build a data-led understanding of rail\u2019s sustainability credentials, by enabling consistent monitoring and reporting of circular performance across its assets, infrastructure and operations.<\/p>\n

How data helps decision-making and safety<\/h2>\n

Better data can enable better managerial and operational decisions within the waste management sector, according to researchers.<\/p>\n

In a paper published in 2024, researchers outlined in the journal Cleaner Waste Systems how they used machine learning models \u2013 a subset of AI \u2013 to analyse and forecast waste generation trends<\/a>. They also used it to assess the viability of numerous waste management methods and develop optimisation models for resource allocation and operational efficiency.<\/p>\n

Better data can enable better managerial and operational decisions within the waste management sector, according to researchers.<\/p>\n

They achieved 85% accuracy on predictive analytics models for forecasting waste generation trends (they attribute this to the integration of more diverse data sets) and a 15% increase in operational efficiency. They said their findings prove that machine learning models can lead to more sustainable and cost-effective practices.<\/p>\n

Tom Harrison, sales manager at Recycleye, has seen firsthand how data-driven technology sharpens decision-making. Recycleye\u2019s machines \u2013 a robot arm and an optical sorter \u2013 can be implemented to give operators a better understanding of preventative maintenance, he says.\u00a0<\/p>\n

\u201cThey see minute-by-minute data to get an instant understanding of what\u2019s going on, including when something is going to break. Having the data is really useful for them to carry out active, rather than planned, maintenance,\u201d he says.<\/p>\n

Data also helps with financial planning, Harrison says.<\/h2>\n

\u201cThey can see if plastic content is higher one month, so they can see the long-term view as to whether it\u2019s worth investing in different equipment.\u201d<\/p>\n

One downside of AI, Harrison explains, is that it can\u2019t weigh material. When it comes to sampling waste, however, it\u2019s possible to know how much items roughly weigh and extrapolate this to understand a waste stream and make informed decisions about it, he says.<\/p>\n

However, there are many instances where human judgment is still crucial, and can complement data-driven technologies.<\/p>\n

UK startup LitterCam uses CCTV footage to detect low-lying litter to help reprofile road sweeper and larger refuse collection vehicles\u2019 routes. Its technology can also detect littering from vehicles.\u00a0<\/p>\n

\u201cData-driven approach enables insight-driven decision-making and ability to make decisions more quickly instead of relying on gut or doing things in the way they\u2019ve always been done,\u201d says Andrew Kemp, the company\u2019s founder and chief executive. \u201cThe ability to make data-driven decisions is absolutely key.\u201d<\/p>\n

However, Kemp adds, local authority offices still have to validate suspected offence footage and look at appeals.<\/p>\n

\u201cThey need to see the littering offence themselves,\u201d he says.\u00a0<\/p>\n

Atkinson continues that the enduring need for human judgement in waste management manifests in several ways, but that the role humans play will shift.\u00a0<\/p>\n

\u201cThere is always going to be a need for mechanical type maintenance,\u201d he says. \u201cParticularly with complex machinery and robotics.\u201d<\/p>\n

\u201cIt\u2019s not a stretch of the imagination to think that, in the next 15 to 20 years, we\u2019ll have autonomous vehicles doing waste collection. But there\u2019s a care element where members of the public would want human interaction between the council and members of the public. There\u2019s also a safety element. We would want an overview, whether remotely or on-site.\u201d\u00a0<\/p>\n

Harrison says there\u2019s a safety element to having human input for Recycleye\u2019s customer base, too.\u00a0<\/p>\n

Recycleye machines would, he says, never go at the pre-treatment front end of a plant, where waste is pre-sorted to remove any items that are dangerous or too big for the automated part of the process.\u00a0<\/p>\n

\u201cI don\u2019t think we will ever replace that side of it with machinery because you need that human understanding of what\u2019s dangerous,\u201d he says. \u201cBut once that\u2019s all been treated, there\u2019s nothing to say that, after that, you couldn\u2019t have a fully automated production line.\u201d<\/p>\n

Challenges and barriers of data-driven technology\u00a0<\/h2>\n

One of the limitations with AI is that it will never be 100% accurate, says Harrison.\u00a0<\/p>\n

\u201cYou train AI to see all the different products that come through the sorting line, but if it\u2019s never seen something before, it has to make a prediction, which may be incorrect,\u201d he says.\u00a0<\/p>\n

Danielle Stephens is the founder of Recycle Lab, a recycling start-up that collects and recycles plastic waste from science labs. She says cost is the biggest barrier for start-ups wanting to implement data-driven technologies.<\/p>\n

Stephens says cost is the biggest barrier for start-ups wanting to implement data-driven technologies.<\/p>\n

The company picks up waste from larger customers, while smaller customers\u2019 waste is picked up by a courier. Stephens says that as the company grows, she hopes to look into AI-assisted route optimising \u2013 particularly because customers are using Recycle Lab as a way to be more sustainable.\u00a0<\/p>\n

\u201cI started the business because of a lack of sustainable options within the industry, so we try to be as sustainable as possible,\u201d Stephens continues.\u00a0<\/p>\n

\u201cRoute optimisation is a sales aid to show customers that we\u2019re using this technology to help reduce our carbon impact, which in turn helps them to reduce their carbon emissions.\u201d<\/p>\n

Currently, Recycle Lab manually tracks collections and sends customers data about their waste, but says this will become increasingly difficult as the company grows, and it will need to embed DWT.<\/p>\n

Cost is the biggest reason Stephens hasn\u2019t invested in these technologies yet.\u00a0<\/p>\n

\u201cIn the next five years, we\u2019ll also be looking at how we can improve efficiency through automation and machinery, but first we need to have more capital to warrant the investment,\u201d she says.<\/p>\n

Another challenge for waste management within the science industry, Stephens says, is legislation.\u00a0<\/p>\n

Because plastic waste and recycling are quite new in the science industry, Stephens explains that legislation is yet to catch up \u2013 and this means there aren\u2019t yet enough standards or specific legislation around recycling.<\/p>\n

The future of data-driven technologies\u00a0<\/h2>\n

Atkinson would like York City Council to start automating the brokerage of materials to markets.\u00a0<\/p>\n

\u201cThe materials we collect have value, and it\u2019s in the local authority\u2019s interest to limit the contamination of recyclable materials,\u201d he says.<\/p>\n

He\u2019d also like to introduce a large language model to start collecting data from social media showing customer feedback regarding any missed bin collections, for example, that could feed info through the periodic optimisation processes.<\/p>\n

\u201cThis could transform council services to be more suited to what residents want,\u201d he says.<\/p>\n

On a wider scale, Harrison says data-driven technologies will continue to become smarter in the waste management sector \u2013 but that this is dependent on people.\u00a0<\/p>\n

\u201cThe model is going to get better, as is the nature of AI, which is constantly learning,\u201d he says.\u00a0<\/p>\n

\u201cIt doesn\u2019t learn by itself, though \u2013 we teach it what individual items are. Behind all these models are people.\u201d<\/p>\n

\u00a0<\/p>\n

\u00a0<\/p>\n

\u00a0<\/p>\n

The post Smart waste, smart decisions: How data is transforming waste operations<\/a> appeared first on Circular Online<\/a>.<\/p>\n","protected":false},"excerpt":{"rendered":"

\u00a0 Jessica Bradley speaks to industry leaders about how data is transforming waste operations. AI technologies are transforming the way waste is processed. From AI-assisted … <\/p>\n","protected":false},"author":1,"featured_media":314,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":[],"categories":[13],"tags":[],"_links":{"self":[{"href":"https:\/\/gokupara.net\/index.php\/wp-json\/wp\/v2\/posts\/312"}],"collection":[{"href":"https:\/\/gokupara.net\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/gokupara.net\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/gokupara.net\/index.php\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/gokupara.net\/index.php\/wp-json\/wp\/v2\/comments?post=312"}],"version-history":[{"count":1,"href":"https:\/\/gokupara.net\/index.php\/wp-json\/wp\/v2\/posts\/312\/revisions"}],"predecessor-version":[{"id":313,"href":"https:\/\/gokupara.net\/index.php\/wp-json\/wp\/v2\/posts\/312\/revisions\/313"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/gokupara.net\/index.php\/wp-json\/wp\/v2\/media\/314"}],"wp:attachment":[{"href":"https:\/\/gokupara.net\/index.php\/wp-json\/wp\/v2\/media?parent=312"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/gokupara.net\/index.php\/wp-json\/wp\/v2\/categories?post=312"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/gokupara.net\/index.php\/wp-json\/wp\/v2\/tags?post=312"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}