Cheshire West Recycling (CWR) chose the Greyparrot Analyzer system to monitor operations at its materials recovery facility, and started gathering actionable data just six days later.
CWR is a data-driven local authority trading company (LATCO) committed to smarter, more efficient resource management. It manages both collection and sorting for over 160,000 households on behalf of Cheshire West and Chester Council.
After adopting a data-driven approach to managing its collections, fleet and staff, CWR turned its focus to its newly refurbished MRF by deploying Greyparrot Analyzer units throughout the facility.
“We operate like a logistics business – our team takes a ‘plan, deliver, review’ approach, using data to optimise everything we do.
This has worked very well for our frontline operations, driving huge efficiencies alongside improvements in safety and performance.
For us, the missing piece was the MRF, leaving us to question how we bring that level of data, intelligence and optimisation to an environment where it’s traditionally been challenging.”– Jody Sherratt, Operations Director
CWR adopted AI waste analytics technology with the goal of improving sorting efficiency, reducing downtime, and improving material quality.
As a smaller enterprise prioritising agile decision-making, Cheshire West Recycling was able to engage our team and install our Analyzer system in just six days — one of the fastest deployments of AI waste analytics technology to date.
Filling an MRF data gap with AI waste analytics
The Analyzer units use cameras to capture images of waste items on conveyor belts, with Greyparrot’s AI identifying each object and displaying live composition data on a web-based Analyzer portal.
Sherratt explained why continuous AI monitoring helps CWR address a lack of operational insight that was standing in the way of targeted improvements:
“Before this, we were just looking at the material leaving the facility – how much waste came out of the plant and what was returned by reprocessors. We could tell when quality dropped or fewer bales were produced, but we couldn’t pinpoint why.”
– Jody Sherratt, Operations Director
How CWR are acting on new operational insights
The CWR team has already used AI waste analysis to identify and resolve operational inefficiencies:
“Reviewing our data, we discovered 40 minutes where a key product line was empty, leaving pickers underutilised. Investigating further, we found that a delayed haulier had disrupted operations.
Although we outsource that process now, we’re reviewing the need to either increase yard staff or expand our own haulage services to ensure greater control and consistency throughout.”– Jody Sherratt, Operations Director
CWR also uses shift-based data to determine the optimal number of pickers based on the quality of infeed stock. Analysing post-sorting material quality by shift allows for performance measurement at a granular level. In one instance, they discovered that two pickers could often outperform a group of three, guiding future resource allocation and training priorities.
Data from the Analyzer also revealed that pickers currently spent significant time removing plastic film, a non-target item. The Analyzer system allows CWR to review feedstock by both mass and item count, underlining just how much strain plastic film is placing on the picking line. This insight will help support a business case for robotic arms to target film.
How data is shaping the business case for sorting machinery investment
CWR also sells baled resources to reprocessors. The company plans to use AI waste analytics to measure, certify and improve bale quality – an opportunity they estimate could generate significant income.
By installing Analyzer units on infeed and sorting lines, the team aims to reduce contaminants in the fibre line and certify bale quality to meet purity standards. Sherratt believes detailed data on bale composition will help CWR command higher prices for higher-quality material:
“Currently, a reprocessor checks purity by breaking down one bale or applying an average. Eventually, we’d like to certify each bale’s compliance with the agreed-upon threshold before it leaves the MRF.
This will allow us to generate significantly more income by accounting for materials such as aluminium within plastic bales.
Importantly, it will also enable CWR to meet new MRF sampling requirements safely and efficiently.”– Jody Sherratt, Operations Director
Sherratt describes operational efficiency as the “first stage” in CWR’s data-driven optimisation journey. In the future, the organisation plans to use AI waste analytics to link infeed composition with collections from specific areas in Cheshire West and Chester.
With a better understanding of local consumption patterns, CWR hope to boost recycling rates and improve feedstock with targeted consumer education campaigns.
Learn how we helped another UK MRF improve product quality here.
