Whether it’s current legislation, or whatever comes next, modern packaging regulations demand the same thing: verified, SKU-level data on material, weight, and recycled content, checked against real plant and vendor records rather than an estimate someone made a few years ago. Building that data set properly requires time and effort. It involves working directly with plants, vendors, and internal systems to confirm what’s actually true, not what a spreadsheet has always assumed.
Once that data exists, though, it does more than satisfy a regulator.
Oversized cases. Excess void space. Materials specified years ago for reasons nobody currently at the business remembers. None of this shows up until someone looks closely enough at the packaging to report on it accurately, which is exactly what compliance now demands.
In practice, that means the audit businesses are required to do for regulatory reasons is very similar to the audit they’d want to do anyway to control cost. Compliance forces us to look. The savings are just sitting there once you do.
Building good compliance data takes effort but will yield benefits if done right. So, rather than the minimum needed to hit a reporting deadline, the savings identified, in material, weight, and often freight, can significantly contribute toward covering the costs of the program, and more. Compliance then stops being pure overhead and starts paying its own way.
This isn’t automatic. Two businesses can gather the same data and get very different results, depending on whether the exercise is scoped narrowly to clear a compliance bar, or genuinely used to ask why the packaging is built the way it is in the first place. The businesses that benefit most treat the regulatory deadline as the occasion to ask the right questions, not just the reason to file a report.
With SB54 already live in the US, and PPWR taking effect in Europe, most companies selling into these markets are already doing the hard part of this work: gathering the data. The real opportunity is making sure that work is asked to do double duty, satisfying the regulator and paying for itself at the same time.
If you’re already building this data set, it’s worth a conversation about what else it can do for you.
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