UK-based AI waste analytics firm Greyparrot has introduced a new live dashboard designed to measure Overall Equipment Effectiveness (OEE) in Material Recovery Facilities (MRFs). The tool integrates real-time computer vision data to help operators continuously monitor how sorting machinery performs relative to its potential.
Traditionally, MRFs calculate OEE using manual audits or retrospective data, which often results in delayed responses to equipment blockages, mechanical slowdowns, or drops in sorting purity. By utilising AI-driven cameras placed at key points along the sorting line, the new system allows facility managers to instantly identify how downtime, throughput speeds, and material composition impact overall performance.
For UK waste management companies and packaging converters, this technology offers a direct route to improving the purity of sorted polymer fractions. Higher sorting accuracy at the MRF level reduces contamination in baled plastics, which is increasingly critical as export regulations tighten. For Turkish recyclers, who rely heavily on importing high-quality UK plastic feedstock, more efficient UK sorting facilities mean more consistent bale quality, fewer rejected shipments at customs, and lower pre-processing costs upon arrival in Türkiye.
As the plastics recycling supply chain faces stricter quality standards under international waste shipment regulations, the practical takeaway for waste operators is clear: transitioning from manual, retrospective sampling to automated, real-time purity and throughput tracking is becoming essential to safeguard material value and maintain access to export markets.
