The DPC Outlook with John Loonie of MESH01

Released in The Interline’s DPC Report 2024, this executive interview is one of a nine-part series that sees The Interline quiz executives from major DPC companies on the evolution of 3D and digital product creation tools and workflows, and ask their opinions on what the future holds for the the extended possibilities of digital assets.

For more on digital product creation in fashion, download the full DPC Report 2024 completely free of charge and ungated.

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In a difficult climate for fashion (and retail in general) new and ongoing investments in tech and talent-intensive initiatives like digital product creation can be harder to justify. Do you believe the business case for 3D / DPC is strong enough? And how is that business case evolving to meet a changing industry?

In challenging climates like we have today initiatives like DPC make more sense than ever. Leveraging DPC assets can significantly enhance product design and development and inform production and buys while decreasing inventory risk. Additionally, early consumer feedback aids in creating exceptional product experiences through an interactive, iterative design process.

Lowering the cost and effort to collect and analyze consumer feedback throughout the product lifecycle to better inform critical business decisions saves resources and helps avoid costly mistakes, all while decreasing time-to-market.

A lot of successful DPC roll-outs have incorporated internal product testing to some extent, with 3D being used to bring concepts, revisions, and ideas to life in a format that allows in-house audiences to make better-informed choices about what to adopt. Other DPC initiatives have focused on applying the same principle downstream, using 3D to sell-in to wholesale partners and end consumers with renders of finished products. It seems like we’re now converging on something in the middle: where 3D assets can be used to capture the voice of the real target consumer early in the product creation process. What does that look like in practice?

Brands don’t need to choose here.  Collecting product feedback using 3D assets should be leveraged at every point in the development process where feedback from the proper audience can provide value. This includes internal stakeholders, external partners, and of course end consumers. Further, the combination of physical product test feedback from the current or previous tests with similar products is uniquely powerful.

In practice, we’ve seen customers reap significant benefits by leveraging their assets externally with real customers to identify the best design direction, gauge receptiveness to aesthetics, home in on color and more. Weaving digital assets like 3D between internal and external reviews from early concept development through refinements and even sell-ins allows brands to essentially test the market and act on feedback without committing to manufacturing and buys. Example after example of this process takes place daily on MESH01, where brands can infuse their future products with the voice and guidance of their customers from the very beginning of product creation and continue through the launch of highly successful new styles and designs.

Taking that approach further, the key capability to build is the flexibility to actually incorporate data streams coming from consumer testing into a more agile model for future design, development, and all-round go-to-market workflows. How do you see those capabilities developing?

We completely agree.  All groups involved in the design, development, manufacturing, marketing, and selling of products should be able to leverage internal and external feedback in their processes and decision making. They should be able to share their findings with other groups and leverage the findings from other groups’ testing. The combined dataset, across different products, should be available for analysis to inform key decisions.

When we consider the move towards digital-native product creation, testing, and refinement, are there generic, industry-agnostic best practices that fashion and outdoor brands should be working to apply? Or are those industries going to be better-served by technology and service companies using retail and fashion-specific expertise to develop more tailored solutions?

While some aspects of DPC may be industry-agnostic, data collection and analysis methods that are context-aware will perform better. When collecting feedback, understanding the right questions to ask, and the most effective way to ask them, will result in more valuable feedback. On the analysis side, large language models that are specifically trained to recognize the product- or category-centric areas of interest, will provide better insights then generic models.

Not only can the use cases be unique, but tools built with the end-user in mind are much easier to adopt and will then bring greater value. Just as MESH01 lives to connect brands to customers so that the end products are exactly what their customers want and need, the tools that product creators use will benefit by the same approach. With accelerated product development calendars and go-to-market processes along with the volatility of the customer base and their shopping habits, the usability of DPC tools is critical. Specific to product feedback solutions, essential functionality built for product creators is key. The nature of a product test can be notably different from a concept test or market research survey. Similarly, the analysis and reporting needs may be quite different between a merchant and a marketing analyst.

Simply put, purpose-built consumer product solutions that understand the challenges specific to these areas will outperform generic solutions.

To really extend the value of digital representations of physical products, a key objective is to grow the community of users who interact with those assets – and with the data they incorporate and generate. This is true of 3D renders, but is it also true of the datapoints that are created when those assets are shared with potential consumers? How do you see the pool of users and creators for product testing tools expanding over time as DPC communities grow?

We see the community continuing to grow both internally and externally as the tools improve and the barrier to entry is reduced. We should strive to enable every team involved in the product lifecycle to easily gather feedback to any questions they have, whenever feedback is needed. Involving more consumers at key points should also be easy to accomplish. Through ML, the collective dataset can then be leveraged by product teams and leadership to make key tactical and strategic decisions.

Just as the deployment of concept testing and product testing can be done across the product life cycle and bring different value at different touchpoints, the distribution of findings can be leveraged to different ends by different roles. Where a product team may be within a period of development focused on the customers’ experience of a material or construction, a merchant may be leveraging an expanded dataset, looking across multiple styles, while working on a new collection. At the same time, trims may be identified across styles that need improvement or a new supplier while executives are looking cross-categorically at performance.

It’s within applications like these that machine learning and AI are being leveraged to make the breadth of data, through multiple streams and touchpoints, truly usable by product creators.

To realise the long-term vision for a complete “digital twin,” it should be possible for any decision across the extended product lifecycle to be made based on a digital representation of the physical asset, with total trust. Which of those decisions do you think meet that high bar today? And which do you believe has the furthest distance to travel?

While we’ve seen solutions drive consistent trusted decisions in areas such as fit and grading, the digital representation of physical materials and beyond, there will still be elements of the customer’s product experience that cannot be solved for digitally, like the hand-feel of a woven, stretch of a knit, finish of an overlock seam or durability of a bond. Our goal should be to continue to strive toward leveraging digital assets wherever possible and lowering the barrier of entry for including key voices in as many aspects of the process as possible.