This article was originally published in our PLM Report 2023 – the definitive instalment in fashion’s longest-running dedicated PLM market analysis. To read other opinion pieces, exclusive editorials, and detailed profiles and interviews with key vendors, download the full PLM Report 2023 completely free of charge and ungated.


Key Takeaways:

  • Embedding data within 3D assets can streamline the apparel product creation process.
  • Accurate 2D patterns, sizing information, and graded patterns can all be included in a 3D asset.
  • Understanding actual vs. average fabric utilization and costs can be possible via the data embedded in a 3D asset.
  • Accurate costing and manufacturing instructions can be unlocked by treating the 3D asset as the product definition.
  • Progressing to a stage of fully manufacturable digital twins would bring the “creator” and “manufacturer” closer together.

If a picture is worth a thousand words, then what is a rotatable, accurate 3D asset that fully represents the physical product worth? A million? A billion?

I am writing this piece to inspire a vision of what I believe is possible for digital product creation (DPC) in apparel and footwear – not just a visual output, but a true representation of a given product that anyone can extract value from.

“A picture is worth a thousand words” is an adage across languages meaning that complex and sometimes multiple ideas can be conveyed by a single still image, which translates its meaning or essence more effectively than a mere verbal description.

We often use pictures in PLM tech packs as the audience (the factory) don’t usually speak English as their primary language, therefore pictures are less open to interpretation than text.

Pictures, however, are two-dimensional and interpretation does still exist, hence the apparel product creation process requiring multiple prototypes prior to approval. Even where brands have switched to 3D design and collaboration, there is often still a gap between the output of those processes and the final physical result.

Or, to put it another way, condensing a 3D workflow down to a 2D render and some accompanying text provides a much weaker decision-making tool than the full 3D asset itself.

I use the term data-embedded when I’m talking about an ideal 3D garment because, instead of a picture of a jacket with all the accompanying text detailing the fabric data (mill, item number, weight, composition, cuttable width, price, etc) – the data that is usually documented and/or referenced via the BOM (bill of materials) – this data should be embedded within the 3D asset if fashion is going to move to truly digital-native workflows.

As an example: to create a realistic-looking 3D asset, fabric and trim libraries are required. Ideally these fabric libraries are linked to the actual physical fabric properties, not only for a realistic visual and drape, but also for a realistic transfer of accompanying data.

Then, instead of sending a BOM to a factory to advise them where to purchase the fabrics and how to detail care and composition on the care and content labels, this information is available once the material/s are selected during the design process. The data is anchored to the asset, and vice versa.

This way, if the fabrics change in the design process, so do the fabrics in the 3D asset, and so does the data embedded in that asset.

Expand this concept to trims: instead of detailing 7 buttons to be used on the centre placket, 2 on each cuff and 1 spare on the BOM, and manually indicating where these buttons are purchased from, item numbers, costs etc., placements and quantities with all the associated embedded data should be clearly visible and accessed via the 3D asset itself.

I want to inspire a vision of what I believe is possible for DPC in apparel and footwear – not just a visual output, but a true representation of a given product that anyone can extract value from.

The vision here is not to weigh 3D styles down with unnecessary admin and information, but rather for the 3D styles to serve as their own product definitions – containing everything needed to bring that product to life, not just visually, but across materials, trims, cutting, sewing, and so on.

Let’s continue to expand this concept to 2D pattern data and sizing information. Again, to create the 3D asset an accurate 2D pattern is required, and from this 2D pattern key POMs (points of measure) can be obtained.

Size charts are currently a part of any tech pack, detailing those points of measure and what the finished garment should measure at these points. This is achieved via the 2D pattern, but again this data can embedded within the 3D asset.

Graded size information (the difference between each size) is also included in the size specification; this could easily be replaced by including a graded 2D pattern into the 3D asset and therefore creating graded 3D assets.

Fabric utilisation is another component of a BOM along with the costing process, however with a graded pattern, fabric information and simple integration to marker making software, creating multiple sized markers for costing purposes is possible.

Integrate again to an ERP system for accurate quantities by size / colour information, then the fabric utilisation and costs can reflect actual values, versus being based on average buy ratios.

What if repeat orders have a higher ratio of smaller sizes than the originally planned costed average cut ratio? Then the fabric utilisation is less. Typically fabric is at least 50% of the cost price of apparel. Understanding actuals vs averages can be possible via the data embedded 3D asset, and the value of doing things this way could be huge.

Another component of the cost of apparel, of course, is “make”: how much does the garment cost to put together? This process is just math, based on time and complexity of manufacturing operations. Time is calculated via seam type, length of seam and complexity / difficulty. Some operations are automated and time values are easily calculated, some operations are manual.

However the apparel industry has decades of time study data to understand time values of manual operations. SMVs (standard minute values) are created per garment: take how many minutes it takes to sew each seam and the garment in total, multiply the garment total minutes by the labor rate and this creates a “make” cost per garment.

Of course seam information, length of seam, even stitch density (critical for understanding thread usage) are all available via the 3D asset. And as a result, accurate costing and manufacturing instructions are both things that can be unlocked by treating the 3D asset as the product definition – not just a thing to look at and interpret, but as a direct driver for factory setup and line balancing.

What do I mean by those terms? Factory set up and line balancing, simply explained, are the order of operations for an assembly line and how many of each operation per production line are required. For example, if it takes twice as long to sew the underarm and side seam as it does to attach the sleeve to the body, then you would require 2 of these operations for every 1 sleeve attach on the production line to balance the flow of production through the line without inefficiency.

Once the 3D asset has progressed to this stage, one could argue this is more valuable than a physical sample. As a physical sample wouldn’t have all the associated data. And as samples evolve and change, understanding the impact of change on cost, both fabric / trim and make in real time, makes the 3D asset hugely valuable – much more so than a representation of a physical sample.

This would also bring the “creator” and the “manufacturer” closer together as the “creator” would essentially be creating a fully manufacturable digital twin.

When we progress to this stage of apparel DPC I think we will refer to the physical sample as the physical twin versus the current model of focusing on the physical with a digital “twin” recreation. This subtle but intentional switch puts the onus on the accuracy of the 3D asset, creating, manufacturing and costing in the digital environment with easy access to all the associated real time data.

If it’s painted right, a 3D picture has the potential to be worth at least a billion words!