Commentary: PMMI
Achieve smarter processing through data optimization
High-quality data collection is essential to manufacturing success, but to improve line performance, skilled operators must be trained to access and act on that data.

Today, meat and poultry processors are prioritizing intuitive controls, reduced manual intervention, and systems that require less internal engineering oversight. Due to workforce constraints and integration demands, processors need to embrace advanced technologies. But true manufacturing efficiency also depends on access to reliable and actionable plant-floor data.
However, the current shortage of skilled data technicians often leads to inaccuracies, and there are many challenges in the transition from manual to automated data capture. For some companies, there is a lack of — or a disparate — automation infrastructure, according to Data Acquisition, Sharing and Utilization, a report from PMMI, The Association for Packaging and Processing Technologies.
The collection of real-time data can inform predictive maintenance, as well as yield management and efficiency improvements. PMMI report participants said that quality data is a key aspect of overall equipment effectiveness (OEE). However, legacy equipment, cost constraints, and variable data sources often complicate scaling, making collaboration with original equipment manufacturers (OEMs) essential for seamless data capture and interoperability.
“There is a need for gradual implementation, ensuring projects have management buy-in and are based on capturing quality data that genuinely impacts performance,” states Jorge Izquierdo, PMMI’s vice president, market development. “Stakeholder buy-in ensures the most effective data utilization.”
Study participants viewed measuring output as the most effective data-capture process, since it yields information that can be extracted from most machines, even older models. But to make the most of this information, all stakeholders must understand the significance of the data they receive and record.
Plus, operators should be able to assess data and alert managers to any issues. For instance, using statistical process control (SPC) to analyze a data set creates an opportunity for tactical leadership and for management to identify issues and enhance maintenance and efficiency.
Establishing a common protocol to enable widespread digital connectivity is just as important as training employees to record data accurately. Study participants agreed that OEE was the most accessible metric for delivering efficient tracking systems and more widespread data acquisition and utilization.
Addressing quality and transparency with AI
Traceability and audit tracking remain a challenge for meat and poultry processors as they switch to digital quality management solutions. Connected packaging tools, including QR codes and digital labeling, are helping to improve traceability, compliance, and consumer engagement while supporting transparency initiatives.
In addition to plant-floor improvements, sustainability transparency remains a major focus amid increased consumer demand for recyclable, compostable, and lightweight packaging solutions. Consequently, processors are continuing to prioritize designs that support their circular economy goals while maintaining product shelf appeal and protection.
At the same time, demand for turnkey, fully integrated, and transparent solutions is rising, and companies are seeking suppliers capable of delivering coordinated, ready-for-production systems, according to a new research study, Processing State of the Industry 2026, developed through a collaboration between PMMI and the Food Production Solutions Association.
“Artificial intelligence [AI] adoption in processing remains gradual and risk weighted, with current use focused on data monitoring, inspection enhancement, and decision support rather than autonomous control,” Izquierdo says.
Processing State of the Industry 2026 interviewees noted that while AI-supported quality and machine-adjustment tools are emerging, processors must ensure consistent and predictable outcomes before allowing systems to affect product quality or process parameters. In addition, successful AI deployment depends on stable, knowledgeable technical teams who can maintain, fine-tune, or retrain AI models over time.
Even though interest in AI is growing, and data-driven tools offer promise, processors remain pragmatic. High-cost tools require convincing justification before they can be adopted.
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