A recent article in Environmental Business Outlook describes how AI is being applied on the production floor to address one of the more persistent problems in plastics recycling: maintaining consistent color quality in Post-Consumer Recycled (PCR) and Post-Industrial Recycled (PIR) materials.

The piece is worth reading for anyone involved in recycled resin procurement. It also prompted us to share where Regenport fits into the same conversation.

The production-side problem

Color inconsistency in recycled resin streams is a known challenge. Because the composition of incoming waste varies, the color of the output material shifts from batch to batch. Compounders respond by adjusting pigment manually — a process that is time-consuming, dependent on operator availability, and prone to variation across shifts.

"The outcome is higher prices for PCR-based materials, which makes it poor economic sense to add more recycled content to consumer products."

— Environmental Business Outlook, April 2026

The article describes closed-loop AI systems that use fiber optic in-line color spectrophotometers to measure color continuously during extrusion and adjust pigment dispensing automatically, without operator input. This removes a manual step that currently contributes to both inconsistency and downtime.

The sourcing-side problem — where buyers spend most of their time

Consistent production quality is one part of the equation. For buyers of recycled resin, a separate but equally real problem is finding the right material in the first place.

Sourcing recycled resin is more complex than sourcing virgin material. The available supply varies by resin type, MFI range, color, contamination level, and geography. Supplier reliability and output volume fluctuate with waste generation. Most buyers currently manage this through direct relationships and manual outreach — a process that works, but doesn't scale well and limits the pool of suppliers they can realistically evaluate.

How Regenport approaches this

Regenport is an AI-powered platform for recycled resin supply-demand matching. When a buyer specifies what they need — resin type, MFI range, color tolerance, volume, location — our matching engine identifies suppliers whose output fits those requirements.

The goal is to reduce the time buyers spend identifying and vetting suppliers — and to surface options they wouldn't find through existing networks alone.

Two different applications, the same underlying shift

Production-side AI and supply-chain-side AI are addressing different problems. What connects them is that both are trying to reduce the friction that keeps recycled resin from being a more straightforward choice for buyers.

If you source recycled resin and want to see how Regenport's matching works in practice, contact us directly.

Source: "AI Technology is making plastics more sustainable and enhancing economics," Environmental Business Outlook, April 2026. Read the original article →