What is DPP? Why are more and more people beginning to pay attention to the trend of digital passports for goods

To start with the conclusion: DPP is not something overly complex. At its core, it is simply addressing an increasingly real problem — whether products can leave behind data that can be viewed during their circulation.

When many people hear this term for the first time, they tend to think of it as a new technology or some kind of “standard system.”

But if you look at it from another angle, it becomes much easier to understand:

👉 In the past, we could only “look at the product”; now we are starting to need to “see what the product has been through”

This difference is exactly why DPP is gaining attention.

What is DPP?

Simply put, DPP (Digital Product Passport) can be understood as creating a “data entry point” for every product.

Through this entry point, users can view information related to the product. More importantly, this information is no longer just static display — it can be continuously updated and recorded.

In other words, it doesn’t just tell you “what this product is,” but gradually starts to answer:

👉 Where the product came from, what it has gone through, and what its current status is

Once you understand this level, you will realize that the focus is no longer a single result, but the entire process.

Why do people misunderstand it at first?

Because for many years, people have been used to understanding all scan-related behaviors through the lens of “anti-counterfeiting.”

Scan a code, get a result:

It’s genuine, or it’s not.

This logic works fine in simple scenarios, but once products enter more complex circulation environments, it becomes insufficient.

For example:

• The same product is transferred multiple times
• It appears in different regions
• Disputes arise during after-sales stages

At this point, a simple “true or false” result cannot really explain the situation.

The real question becomes:

👉 What happened in between?

Product data is essentially evolving

If we break down product-related data, it can roughly be divided into three categories:

The first category is the most familiar — basic information such as name, specifications, and materials.

The second category is result-based judgments, such as whether it has passed verification.

The category undergoing the biggest change now is the third:

👉 Behavioral records

When actions like scanning, viewing, and verifying are recorded, they are no longer just a one-time “query,” but gradually form a trace.

This trace becomes the basis for future judgment.

What DPP really changes is not the form, but the logic

On the surface, it may not look very different from traditional methods — it still involves scanning and pages.

But the underlying logic is completely different:

👉 Previously it was “viewing information”
👉 Now it is becoming “viewing records”

One is static, the other is continuously evolving.

One solves “display,” the other starts to participate in “judgment.”

When products are involved in circulation, after-sales, and distribution channels, this difference becomes increasingly evident.

Why is this trend becoming hard to ignore?

The reason is simple: the environment has changed.

Products are no longer traded within a single channel — they are continuously moving across different platforms and regions.

Users are also no longer satisfied with just “descriptions,” but care more about:

👉 Whether there is evidence 👉 Whether it can be verified

In such a context, without any records, relying solely on descriptions makes it difficult to sustain trust over time.

On the other hand, once there are even basic records, it becomes much easier to gain acceptance.

This is why more and more people are starting to pay attention to DPP —

Not because it is “new,” but because it is something that will have to be addressed sooner or later.

What are the current practical approaches?

Some merchants have already started trying to combine product identity with verification records.

For example, using labels or QR codes so that every scan leaves a record, rather than just displaying information.

Approaches like GEXYRAL are moving in this direction — placing more emphasis on “records” rather than just “results.”

Of course, this does not have to be implemented all at once. A more practical approach is to start with small-scale testing.

Conclusion

As product data gradually becomes a fundamental element
Simply displaying information is no longer enough

What matters more is whether this information can be verified and recorded

When products can leave real verification records during circulation
They truly gain long-term reference value

Platforms providing such capabilities already exist, and it is worth starting with small-scale trials