Packaging compliance becomes an industrial data test

A packaging regulation does not usually sound like a technology story. For food and beverage manufacturers, however, the EU Packaging and Packaging Waste Regulation is turning a familiar compliance duty into a test of how well data moves across suppliers, production, quality and logistics. The issue is no longer only whether a product uses the right material, but whether the manufacturer can prove what sits inside the product, the packaging and the evidence file behind both.

Neil Smith, CPG president at Schneider Electric, says the regulation matters because it changes the nature of proof. “The scope of the regulation is starting to get very granular,” he says. “Moving from a simple statement to say I’m compliant to having to essentially provide evidence. That burden reaches beyond EU-based manufacturers because any business shipping into the European market has to deal with the same expectations.”

The pressure lands on an already expensive part of the business. Schneider Electric’s 2026 Industrial AI in CPG study found that European food and beverage manufacturers estimate regulatory and compliance costs at 13.7 per cent of a product’s final price, higher than labor costs at 12.2 per cent and manufacturer margin at 8.0 per cent. Across the UK and Ireland, compliance costs were estimated at 12.8 per cent of final price, ahead of labor at 11.5 per cent and margin at 9.5 per cent.

Compliance moves beyond factory walls

The immediate trigger is PPWR, which has now moved from looming deadline to live compliance pressure. Its requirements include tighter evidence around packaging conformity and restrictions on PFAS in food-contact packaging above defined thresholds, but the harder practical problem is not understanding the regulation. It is connecting declarations, material composition, packaging changes and batch-level evidence in a way that can survive audit, supplier disruption and future rule changes.

A manual process may be enough to get through an initial filing, but it leaves the business exposed when the next packaging change, supplier substitution or regulatory update asks for a different view of the same fragmented data. For manufacturers, the post-deadline challenge is now operational: making compliance evidence part of the way production and supply-chain data are managed every day, rather than treating it as a separate reporting exercise.

Smith is clear that manufacturers can treat the requirement as paperwork, but doing so will add cost and friction. “I actually think this is a data management problem to be able to do it efficiently,” he says. “We talk 13, 14 per cent cost of compliance today. If you do it manually, this is only going to get higher for you.”

The challenge is compounded by wider changes in food and beverage production. Smaller batches, shorter product lives, more SKUs, more frequent changeovers and volatile raw material costs already make operations harder to manage. Packaging compliance adds another layer because it requires manufacturers to connect supplier declarations and material data with what actually happened on the line, including where materials were consumed, which batch they were attached to and how exceptions were handled.

“Manufacturers have good visibility. Data is often in silos,” Smith says. “What this is really starting to do is require the data transparency beyond the walls of the factory. This starts to take a full supply chain view.” In his view, much of the necessary information already exists, but it may sit in an email, a file, a shipping note or a supplier submission rather than inside a connected operating system.

The data already exists in fragments

That distinction is important because compliance is becoming less about producing a static document and more about maintaining a live chain of evidence. Material composition, supplier declarations, production records, quality data, energy information and product traceability can no longer remain disconnected if manufacturers want to avoid repeated manual exercises. A change in packaging format, film, tray or supplier can create a new evidence requirement every time the product changes.

Smith describes the task as connecting external supplier data to the systems that already support production. “How do you integrate that into your production data?” he says. “How do you consume these raw materials into your production? Where have they been consumed? What product is it being attached to? How do you also manage change?”

AI has a role, but only when it is grounded in reliable industrial data. Smith sees the most immediate applications in practical checks: finding gaps in supplier documentation, identifying omissions, spotting traceability breaks and reconciling evidence before it becomes an audit problem. The constraint is that supplier information may arrive in different formats, at different levels of detail and with different standards of quality. Unless that information is connected to production, purchasing and batch data, AI will be working around the gaps rather than solving them.

That makes data contextualisation the real foundation. Manufacturers need to know which packaging material was used, which supplier provided it, which batch it was attached to, and whether exceptions such as packaging jams, substitutions or manual changes were properly recorded. Once that thread is reliable, AI can start to reconcile product information with compliance evidence and help compile the audit file.

Regulation should not become another one-off project

The risk for manufacturers is that PPWR becomes a standalone compliance project, solved by temporary reporting effort and disconnected from the systems used to run the business. That would miss the wider opportunity. The same data foundation needed to prove packaging conformity can also support faster investigations, recall readiness, waste reduction, energy reporting and supply chain resilience.

“I think the manufacturers who view this not as an obligation but as an opportunity are going to fare better,” Smith says. “This is not the only regulation that’s going to come in place with similar type of obligations.” He points again to the rising complexity of manufacturing operations, where line agility and product refresh rates are increasing while supply and material costs remain volatile.

Kellogg’s Valls plant in Spain shows how the same data foundation can deliver operational and compliance benefits together. The site, which produces 12 cereal brands, moved from manual line-weight data collection twice per shift to real-time samples every 20 seconds, equivalent to 1,440 samples per shift. The plant nearly eliminated nonconformities from human input, reduced critical control point incidents by 64 per cent and cut cereal cases scrapped due to failures by 73 per cent.

The plant also used asset framework data to create digital twins of packing-line equipment, putting machine metrics on dashboards so operators could manage line efficiency in real time and see maintenance alerts before problems developed. By 2018, it had reached the consumer-packaged goods best-in-class OEE benchmark of 80 per cent, increased mean time between failures by 180 per cent and reduced minor stops per hour by 67 per cent.

The improvement did not come from creating a separate compliance system. It came from connecting production data, contextualising it against assets and process conditions, and making it available to the people managing quality, maintenance and line performance. The relevance to PPWR is not that the project was designed for packaging regulation, but that connected plant data can become evidence, control and improvement infrastructure at the same time. That is why PPWR should not be seen only as a packaging rule, but as another signal that manufacturers need data systems capable of proving what happened, when it happened and which decisions followed.

Building on existing systems

Smith says many manufacturers already have the backbone for this work, even if the data is not yet connected across the full enterprise. Within Schneider Electric’s customer base, AVEVA PI infrastructure often handles time-based operational data inside the facility. The next step is extending that view into suppliers, logistics, distribution centers and cloud-based collaboration environments such as AVEVA Connect, where data can be shared, controlled and enriched for analytics.

“The disclosures and compliance could be an area where AI could quite readily sit today, as long as you’ve got good, complete data,” he says. “The actual filing, the record creation, could be automated. That turns compliance from a purely defensive activity into a potential cost-efficiency lever, particularly for manufacturers dealing with multiple markets and recurring evidence requests.”

The investment case is not based on replacing everything old. Smith says manufacturers often operate a spectrum of technology, including machines from the 1950s or 1960s that still perform useful work and will not be ripped out for the sake of digitization. The more realistic route is integration, secondary sensing, controlled manual inputs and a transparent data layer that lets old and new equipment contribute to the same evidence chain.

“It’s a question of how you integrate all of this and address that obsolescence,” he says. “Creating that data transparency does many things for you.” For Smith, PPWR is one more domain added to a wider industrial architecture, joining energy, process information, traceability, enterprise systems and operational intelligence in a single view of manufacturing performance.

Compliance becomes competitive proof

The commercial significance extends beyond the cost of filing documents. Food and beverage brands depend on trust, and failures in safety, contamination or recall management carry high financial and reputational costs. The ability to prove what is in a product and its packaging is becoming part of how manufacturers protect market access and customer confidence.

Smith links that directly to brand value. “The reputable brands are very keen on safety,” he says. “Where there are supply chain failings, there have been some well-documented cases, for example, in baby formula around the world in terms of contamination and how they manage their recalls. They’re all high costs.”

The manufacturers best placed for PPWR will not be those that build a compliance spreadsheet fastest. They will be the ones that connect supplier information, production reality and audit evidence into a system that can adapt as regulations tighten. Packaging compliance is becoming a useful warning about the next phase of manufacturing data: it must reach beyond the factory, withstand scrutiny and still help the business move faster.