The Missing Piece of the Sustainability Puzzle

Sustainability in Fashion: The Current State

In the rapidly evolving landscape of fashion and textiles, legislation is intensifying, especially within the EU. By late 2023, discussions on as many as 16 pieces of legislation were underway, signalling an end to the era of self-regulation in sustainability by 2024. 

Amidst this regulatory upheaval, companies grapple with securing sufficient data. While many brands have already made extensive ESG disclosures, these have been brand-specific and are not comparable. The forthcoming Corporate Sustainability Reporting Directive (CSRD) aims to standardise ESG reporting, while the Corporate Sustainability Due Diligence Directive calls for comprehensive due diligence across the value chain. These upcoming regulations, especially the CSRD, have stirred concerns in the industry about meeting the demanding requirements for data and analysis. For instance, a senior executive at Puma expressed challenges in fulfilling CSRD requirements despite two decades of reporting experience. This has led to an industry-wide scramble to adapt and brands’ sustainability teams, often staffed with mostly junior members, primarily spend their time on data consolidation and reporting. Luckily, recent years have seen a rise in tech platforms, like Fairly Made, Carbonfact, and Vaayu, aiming to streamline this reporting process.

However, there is one caveat: these tools cater to sustainability teams. Without solutions that are built for the operational teams like product development, that can blend in seamlessly in their workflows and bridge sustainability targets with other organisational objectives like financial performance, a crucial gap remains: the effective incorporation of sustainability in the day-to-day.

The Siloed Nature of Product Sustainability Impact Analysis 

Let’s double down on fashion product development. Today, sustainability impact analyses are almost always conducted post-collection sign-off, a point where over 80% of environmental impacts have been irreversibly decided. This delayed analysis results in a reactive, rather than proactive, approach. Insights on better material and supplier choices, vital for reducing environmental impact, are often recognised too late to influence the current collection, only potentially benefiting future lines.

Sustainability impact analyses are almost always conducted post-collection sign-off, a point where over 80% of environmental impacts have been irreversibly decided.

Effectively integrating the gathered insights in the form of design targets in the next line forms another challenge. Product developers, already navigating a fast-paced development cycle, find themselves with the additional challenge of incorporating these insights into their next designs but without the necessary tools to do so. Sounds overwhelming? Product developers can confirm.

The Cost of Financial Data’s Absence in Early Product Development

In fashion product development, a parallel issue to sustainability data is the unavailability of product margin data during crucial early design stages. At this point, the majority of gross margin is set in stone, driven by material and supplier choices, yet clear visibility on financial implications is often lacking.

A parallel issue to sustainability data is the unavailability of product margin data during crucial early design stages. At this point, the majority of gross margin is set in stone.

As highlighted in our article "Beyond DPC: The Hidden Cost of Sampling," this gap in financial data can have significant consequences. For instance, an executive from a billion-dollar apparel group confessed that up to 40% of samples that underwent the full development process are later discarded due to financial infeasibility, leading to a significant waste of time, effort, and resources, as well as direct sampling costs. This scenario underscores the need for integrating real-time financial data into the product development process, ensuring decisions are both financially viable and sustainable from the start.

The Complexity of Multi-Dimensional Optimisation

In the ideal scenario where all necessary data is at hand, fashion product developers still face the daunting task of optimising a sample across multiple dimensions. Gross margin is a relatively straightforward metric, but environmental impact is multifaceted, encompassing aspects like CO2 emissions, water usage, and circular design principles, which include design for recyclability as well as for durability and repair. Unsurprisingly, this complexity can be overwhelming for product developers. To tackle this, there are several strategies:

• Setting Company Targets: Establishing clear organisational goals can guide trade-offs in optimisation, serving as a compass for decision-making.

• Aligning with Regulations: Utilising methodologies like the PEF score, which consolidates environmental impact across dimensions into a single metric, can simplify the evaluation process

• Monetizing Environmental Impact: Assigning monetary value to resources and ecological footprints allows for comparison on an adjusted gross margin basis. An example is the Kering Group's annual EP&L (Environmental Profit & Loss), developed in collaboration with PWC, which accounts for environmental costs in financial terms.

These approaches, while not mutually exclusive, provide a framework to navigate the intricacies of multi-dimensional optimisation of products in fashion product development.

Gross margin is a relatively straightforward metric, but environmental impact is multifaceted, encompassing aspects like CO2 emissions, water usage, and circular design principles, which include design for recyclability as well as for durability and repair.

However, without specialised tools that seamlessly integrate these varied impact metrics into fashion product development teams’ workflows, the practical implementation of data-driven optimisation remains elusive. Advancements in Machine Learning (ML) offer a promising solution. As we explored in "Beyond Midjourney: A Fresh Perspective on the Role of AI in Fashion Design," ML can be instrumental in providing real-time, multi-dimensional optimisation. We encourage you to read the article, to explore ML's capacity to measure performance metrics in real-time, instantly optimise designs and even translate initial sketches into comprehensive Bills of Materials! This innovative approach is reshaping the landscape of fashion product development, making it an exciting area to watch.

Finalising the Puzzle 

For the fashion industry to truly realise sustainability objectives, and without compromising on financial targets, there needs to be a fundamental shift from traditional, siloed reactive reporting processes to proactive data-driven optimisation in the day-to-day of operational teams, like product development. Success hinges on the creation of solutions tailored for operational teams, which can seamlessly integrate into their workflows and align sustainability goals with broader organisational objectives, including financial performance. In addition to adopting new tools, this shift is about transforming the culture and processes of traditional product development in the fashion industry. With the existing sustainability teams, the first building blocks of sustainability transformation are firmly in place: it's now time for the industry to empower other departments, gearing them up to play a pivotal role in this sustainable revolution.