The current dialogue in the fashion industry around innovation in sampling predominantly revolves around Digital Product Creation (DPC) – shifting from physical to digital samples to reduce logistical costs and environmental impact. Brands like Timberland, Macy's, Hugo Boss, Adidas, and Nike are at the forefront of this shift: Macy’s increased its virtual sample production to 61% in its 2022 development season, Hugo Boss aimed to create 90% of its samples digitally by 2023 and Adidas reported substantial sales from products created with 3D design. And when visiting a recent edition of the leading conference for fashion product development, PI Apparel, it only takes a couple of minutes to see that DPC is the red thread.
Despite the efficiency of 3D design and virtual sampling, there are additional, often overlooked, costs in the sampling process that extend beyond the tangible resources used. This conversation opens up the need to delve deeper into the less-discussed aspects of sampling in fashion, exploring how it impacts both the financial and creative sides of the industry.
As we delve deeper into fashion's sampling processes, we uncover significant, yet often overlooked, challenges. These go beyond just reduced logistical costs and environmental impact, revealing hidden layers of expense.
One such challenge is the high rate of sample rework or abandonment due to financial infeasibility. An executive from a billion-dollar apparel group shared with us that around 40% of samples per collection, after being approved for style after extensive development, are later redone or discarded as they fail to meet financial feasibility. This early-stage gap in profitability assessment leads to considerable waste in terms of time, effort, and resources, alongside the direct costs incurred through sampling. Such inefficiencies highlight the need for more accurate early-stage financial evaluations in product development.
An executive from a billion-dollar apparel group shared with us that around 40% of samples per collection, after being approved for style after extensive development, are later redone or discarded as they fail to meet financial feasibility
Another notable challenge is the absence of a systematic approach to track and analyse sample evaluations – understanding why some samples are chosen over others. A product developer who works for some of the largest Maisons in Paris shared her experience on this challenge, citing "multiple déjà vu's” when certain material choices were disregarded for style reasons and new samples had to be ordered. This results in the duplication of mistakes across collections, leading to considerable time loss and inefficiency – a clear indicator of the need for more strategic and data-driven approaches in the sampling process.
In the world of fashion product development, a critical challenge remains largely unaddressed: the scarcity of product margin and sustainability data during the crucial early stages of design. Data gaps are prevalent, ranging from the price, costs of fabrics, trims, production and transport for financial metrics; to component weights, material properties, supply chain specifics and production data vital for calculating environmental impacts. This lack of data, stemming from the fast-paced nature of the development process and the time-intensive task of data collection and organisation, poses a significant hurdle as outlined above.
In the world of fashion product development, a critical challenge remains largely unaddressed: the scarcity of product margin and sustainability data during the crucial early stages of design.
As highlighted in our article, "Beyond Midjourney: A Fresh Perspective on AI in Fashion Design," Machine Learning (ML) can be pivotal in addressing these pain points. ML algorithms can provide insights into the financial viability of designs from the outset. Additionally, ML algorithms can help in recording and analysing the decision-making process behind sample selections, ensuring valuable learnings are captured and utilised in future projects.
The true cost of traditional sampling in fashion design extends beyond the immediate resource impact.. It encompasses considerable waste in terms of time, effort, and resources. By synergizing virtual sampling with ML integration in the sampling process, the fashion industry can not only save time and resources but also gain deeper insights, leading to more informed decisions and a more efficient design process. This paradigm shift moves us from digitising the traditional process to fundamentally reimagining it, bringing both efficiency and strategic depth to the forefront.
Design meets data: athena studio is the first solution that integrates financial and environmental performance early in fashion product development, enabling product teams to design towards their targets. Our platform visualizes the impact of each design and generates recommendations, offering an effective tool to integrate targets into teams’ day-to-day and to save time on manual, post-mortem analyses.
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