Breaking Through the Low-Speed, Low-Yield Bottleneck in MAP Lines for Chilled Fresh Meat

On the production line of a chilled fresh meat cutting and packaging workshop, minced meat, shredded meat, pork collar, pork belly, rib strips, and spare ribs are continuously fed into the modified atmosphere packaging (MAP) machine—a critical step for locking in freshness. However, the rapid growth of new retail formats and increasing consumer demand for high-quality fresh pork are placing greater pressure on traditional operational models. This leads to more instability, affecting supply chain operations, store sales, and forward warehouse fulfillment.

In traditional chilled meat cutting and packaging workshops, the precision cutting line, MAP machine, and weighing/labeling machines often operate in isolation. The connections between these stages—such as sorting, transfer, and labeling—rely on manual labor, resulting in core operational challenges:

1 ️⃣ Low production efficiency

2 ️⃣ High labor costs

The Solution: Giving Production Lines “Intelligent Eyes”

In traditional settings, production lines are staffed with numerous workers straining to distinguish between similar-looking packages of different products. Any lapse in attention can lead to mislabeling. Manual counting of SKU quantities is prone to errors and frequent slowdowns… Even expensive MAP lines often operate far below their designed capacity.

This is not an isolated case. Driven by consumer upgrading, demand for small-portion MAP chilled meat is surging, making multi-category, small-batch, high-quality production the new norm. Traditional production models, heavily reliant on manual labor, have become an invisible constraint hindering the efficiency of MAP packaging lines.

The[Desigco] AI Visual Recognition Systemis designed to break this bottleneck. This solution integrates machine vision and deep learning to empower packaging lines with new capabilities:

As MAP packages pass inspection points, high-sensitivity industrial cameras capture biological characteristics such as meat texture, fat distribution, and bone structure from multiple angles—instantly identifying the marbling patterns of pork collar or the bone structure of ribs. A built-in adaptive recognition engine analyzes these visual fingerprints, determines the SKU within milliseconds, and synchronizes the results with downstream equipment.

Restructuring Production Line Rhythm: From “Human-Machine Waiting” to “Seamless Collaboration”

The core value of the visual labeling system lies in creating an intelligent closed loop for the MAP packaging line.

Outdated, slow MAP machines can be replaced on demand with high-speed models, allowing entire line capacity to jump from 2,000 packages/hour to 4,000 packages/hour—significantly boosting production efficiency while reducing auxiliary labor by 50%.

All nodes—from receiving production orders and issuing tasks to aggregating production data and uploading reports—will be system-controlled. This is not just an efficiency tool but a data hub. The system continuously gathers data on SKU distribution, order types, and equipment status, transforming it into actionable insights through analytics platforms. Previously vague experiential issues now receive clear, data-driven answers.

Enabling every MAP line to realize its full designed potential is the core mission of the[Desigco] AI Visual Recognition System. If you too are struggling with production bottlenecks, we welcome you to explore the next generation of intelligent production solutions for chilled meat with us.