For many companies, packaging data does not really exist in one place.
It exists in spreadsheets maintained by packaging engineers. In supplier specifications sent as PDFs. In ERP systems designed primarily for procurement and finance. In sustainability databases. In emails. In test reports. In artwork systems. And sometimes in the knowledge of the people who have worked with a particular packaging format for years.
For a long time, this fragmentation was manageable.
Today, it is becoming a business risk.
Packaging is increasingly connected to regulation, sustainability, procurement, logistics, product protection, cost and corporate reporting. As those decisions become more interconnected, companies are discovering that the quality of the decision depends on something much more fundamental:
Can they trust the packaging data behind it?
Packaging complexity has outgrown the spreadsheet
Spreadsheets remain one of the most useful tools in business. They are flexible, familiar and remarkably effective for solving local problems.
The problem begins when hundreds or thousands of packaging components are managed across multiple markets, suppliers, factories and product portfolios.
One team may record the weight of a tray. Another records its material composition. Procurement maintains the supplier reference. Sustainability calculates its environmental impact. Regulatory teams assess compliance. Logistics works from dimensions and pallet configurations.
Each dataset may be correct within its own context.
But together, they may not describe exactly the same packaging.
A component name changes. A supplier updates a specification. A material structure is simplified in one database but not another. A packaging weight is measured in one file and estimated in another. One market uses grams and millimetres; another uses ounces and inches.
Individually, these inconsistencies can look insignificant.
Across a global portfolio, they accumulate.
And eventually, the organization starts making decisions using different versions of the same reality.
Fragmentation creates invisible costs
The most obvious cost of fragmented packaging data is time.
Packaging engineers search for specifications. Sustainability teams request information already held elsewhere. Suppliers are repeatedly asked for the same data. Regulatory teams rebuild datasets for individual assessments.
But the larger cost is not administrative.
It is decision uncertainty.
Consider a seemingly simple question:
How much plastic does this packaging portfolio actually contain?
Answering it reliably may require knowing the exact component structure, material composition, weight, supplier, market configuration and current specification version for hundreds or thousands of SKUs.
If those facts are distributed across disconnected sources, the organization first has to reconstruct the packaging portfolio before it can even begin the analysis.
The same problem appears when asking:
Which packaging components could be affected by a regulatory change?
Where could material reduction generate the greatest value?
Which specifications are duplicated across the portfolio?
Which suppliers have provided sufficient evidence?
What would happen to cost if a material were replaced?
Which packaging formats are ready for a particular market?
The analytical question may be sophisticated.
But very often, the bottleneck is much more basic:
the underlying data is not decision-ready.
The cost is no longer confined to packaging teams
Historically, poor packaging data was primarily an operational inconvenience.
That is changing.
Packaging information is increasingly feeding decisions made across the organization:
Regulatory teams need reliable material and component information to assess compliance;
Sustainability teams need accurate packaging structures, weights and evidence to calculate environmental performance;
Procurement teams need visibility across suppliers, specifications and materials to identify sourcing opportunities;
Operations and logistics teams depend on accurate dimensions, weights and configurations;
Finance teams increasingly need packaging information to understand fees, material exposure and transformation costs;
And executives need to understand the consequences of packaging decisions across markets.
A data inconsistency that once resulted in an engineer spending an afternoon checking a spreadsheet can therefore propagate into regulatory assessments, sustainability calculations, sourcing decisions and investment priorities.
Fragmented packaging data is no longer simply a packaging problem.
It is an enterprise information problem.
More data does not necessarily mean better decisions
Most organizations are collecting more packaging information than ever before.
That does not automatically mean they understand their packaging portfolios better.
The critical distinction is between having data and having trusted data.
A spreadsheet containing 50,000 packaging records may appear comprehensive. But how many values can be traced back to their original source? Which specifications are current? Which values were measured, supplied, calculated or estimated? Which records conflict with another source? Which evidence is still valid?
Without that context, data volume can create an illusion of certainty.
A number without provenance is simply a number.
For packaging information to support important decisions, companies increasingly need to understand not only what the value is, but also:
Where did it come from? When was it collected? Who owns it? Which version does it represent? What evidence supports it? And how confident should we be in using it?
That is a fundamentally different approach to packaging information.
Regulation is accelerating the problem
The regulatory environment is making packaging data quality increasingly important.
In Europe, the Packaging and Packaging Waste Regulation is pushing companies toward more detailed understanding of packaging composition, recyclability, recycled content, minimization and supporting evidence.
At the same time, Extended Producer Responsibility frameworks are expanding and evolving across markets.
In the United States, packaging requirements remain more fragmented, with federal requirements interacting with rapidly developing state-level rules.
For multinational companies, this creates a difficult equation:
one packaging portfolio may need to support multiple regulatory interpretations and reporting requirements.
The temptation is to solve each new requirement by creating another spreadsheet, another questionnaire or another reporting workflow.
That may solve the immediate compliance request.
It also creates another layer of fragmentation.
Over time, organizations risk building parallel versions of the same packaging portfolio — one for engineering, one for sustainability, one for compliance, one for procurement and another for reporting.
The strategic challenge is therefore not simply collecting more regulatory data.
It is establishing a packaging information foundation capable of supporting multiple decisions.
AI makes trusted packaging data even more important
Artificial intelligence is creating significant opportunities for packaging.
AI can help identify patterns across portfolios, compare alternatives, detect anomalies, accelerate regulatory analysis and support increasingly sophisticated decision-making.
But AI does not eliminate the data problem.
It amplifies it.
If two systems contain different weights for the same packaging component, an AI model still needs to know which value should be trusted.
If material composition is incomplete, AI cannot transform an assumption into verified evidence.
If the relationship between a product, its packaging components and its suppliers is unclear, sophisticated analysis may simply produce sophisticated uncertainty.
This leads to an important principle:
Before companies can use AI to make better packaging decisions, they first need packaging data they can trust.
The competitive advantage will not come simply from applying AI to more information.
It will come from combining AI with structured, governed and traceable packaging knowledge.
From fragmented files to packaging intelligence
Solving the problem does not necessarily mean replacing every existing corporate system.
ERP, PLM, sustainability platforms, supplier systems and spreadsheets all serve legitimate purposes.
The challenge is creating a coherent layer of packaging knowledge across them.
That requires several capabilities.
Packaging information needs to be structured so that products, SKUs, packaging systems, components, materials, suppliers and markets can be connected:
It needs to be normalized so that terminology, units and classifications can be compared consistently;
It needs to be reconciled when multiple sources describe the same fact differently;
It needs to be traceable so that important values can be linked to their source and supporting evidence;
And it needs to be decision-ready, meaning the organization can understand whether the available information is sufficiently complete and reliable for the analysis it wants to perform.
That last distinction matters.
Data can be perfectly adequate for one decision and insufficient for another.
A company may have enough information to calculate packaging weight across a portfolio but not enough evidence to substantiate a regulatory claim.
The objective should therefore not be perfect data.
It should be trusted data fit for the decision being made.
The hidden opportunity
Fragmented packaging data is a liability.
But resolving it creates something valuable.
Once packaging information becomes structured and connected, companies can begin asking questions that are difficult to answer today:
Which components create the greatest regulatory exposure?
Where are specifications unnecessarily duplicated?
Which packaging formats offer the strongest opportunities for lightweighting?
Where are supplier dependencies concentrated?
Which changes could simultaneously reduce cost and environmental impact?
Which products should be prioritized for redesign?
Instead of repeatedly rebuilding datasets for individual projects, the organization begins creating a reusable packaging knowledge base.
That changes the economics of packaging analysis.
Every new project can build on what the company already knows.
Over time, packaging data stops being a collection of files and becomes an organizational asset.
A new packaging capability
The companies that manage packaging best over the next decade will not necessarily be those with the largest packaging teams.
They will be those capable of connecting engineering expertise, regulatory intelligence, sustainability knowledge and trusted data into better decisions.
This requires a shift in perspective:
Packaging specifications are no longer simply technical documents;
Supplier information is no longer simply procurement data;
Material composition is no longer simply an engineering parameter.
Together, they form part of the information infrastructure behind increasingly strategic business decisions.
And as packaging becomes more regulated, more measurable and more connected to enterprise performance, the cost of fragmented information will continue to rise.
The question for executives is therefore becoming less:
“Do we have packaging data?”
And increasingly:
“Do we have packaging data we can actually trust and use?”
AUDREN Perspective
At AUDREN, we believe the next generation of packaging strategy will be built at the intersection of engineering, regulation, sustainability and data.
For companies operating between Europe and the United States, this becomes particularly important: the same packaging portfolio increasingly needs to respond to different regulatory frameworks, market expectations and business priorities.
Turning fragmented packaging information into trusted, decision-ready knowledge is therefore not simply a data-management exercise.
It is becoming a prerequisite for better packaging decisions.