CAN AI MAKE FASHION MORE SUSTAINABLE?

How Artificial Intelligence can create a new industry model based on sustainability and profitability.

In an interview with Business of Fashion, H&M digital consultant (and Cambridge Analytica whistleblower) Christopher Wyle states: “Every piece of unsold clothing is a failure of matching supply and demand….you [fashion brands] have overproduced.” Overproduction is prevalent across the fashion industry. From luxury brands to fast fashion giants, there are simply too many clothes being produced that do not adequately match the demand. Fashion poses a serious threat to the planet, with global textile production more than doubling in the last 15 years and total greenhouse gas emissions from this at 1.2 billion tonnes annually. Fast fashion especially has become the figurehead of waste,  with the industry producing over $500 billion worth of waste annually. To make matters worse, it is no secret that luxury houses routinely burn millions worth of unsold stock.

As consumers, we’ve reached a deadlock. A report by Nielsen shows that almost 73% of consumers expect brands to be environmentally sustainable. Yet, businesses have long operated under the belief that social responsibility and profit generation cannot co-exist. With calls for environmentally friendly business growing by the minute, brands can no longer ignore demands for sustainability. The question now becomes: how can fashion brands ensure environmental as well as business growth?

AI has the potential to cause a seismic shift in fashion as it presents opportunities for profitability as well as sustainability. Implementation of Artificial Intelligence applications can help innovate every stage of the production cycle, from pre-design, to design, to logistics. This results in intelligent production; where AI in used at every step in a production cycle to increase performance and profitability. From trend forecasting to demand planning to AI stylists, AI led intelligence production creates a domino effects that can tackle overproduction and waste.

When the use of AI in fashion is discussed, the focus is often on the potential for trend prediction. H&M’s Head of AI and Global Analytics at Arti Zeighami addresses this dichotomy: “There is so much noise about AI-driven retail from the consumer side…..[AI] can help optimize the whole supply chain: forecasting trends, how to distribute, setting prices.” AI can do more than forecast trends to increase demand.  From the back end to the consumer, numerous AI applications can serve multiple purposes for garment production, inventory planning and trend detection.

In the garment production process

Rana Plaza Tower / Source: New York Times

Garment production has seen the brunt of public scrutiny over the last decade. The 2012 Rana Plaza tower incident brought to light the unpaid labour used to make our clothes. The disaster saw 1,134 workers die not from natural causes but as a result of a suboptimal conditions, confirming everything we didn’t want to be confronted with about how our clothes are made.

AI-based tools are being used to foster a culture of transparency by tracking suppliers operating at different production stages. Startups like Sourcemap and Trustrace, according to Huiertech,  work with apparel brands to create visualizations of supply chains  backed by “verified supplier data.” The combination of AI and blockchain can also help. By revealing the entire lifecycle of a specific garment, businesses can become more efficient and create sustainable supply chains.

In inventory

Inventory demand planning is shifting the traditional “push” model in fashion. Traditionally, brands “push” new collections and trends onto consumers. This is risky as companies can incur waste if inventory is not sold. By extracting information from mass amounts of data, AI can conduct a dynamic analysis about social media trends, demographics, body sizes and so on. This real time data allows brands to adjust stock availability depending on the needs of both its online stores and national/international storefronts. Retail giant Zara continues to invest in this AI application to help outperform its high street competition, increase customer-centricity and avoid excess inventory.

Trend forecasting

Wyle asserts that fashion companies must take a different outlook on the supply chain: they must see it less as one that “starts at production and ends with the consumer” to one that “starts and ends from the consumer.” Applying this customer-centric focus will help brands underpin what consumers want and don’t want to buy. AI and data are essential for this as they can enable retailers to better understand their customers’ shopping preferences. Trend detection with AI could minimize forecasting errors by 50% and reduce excess  inventory  by 20-50% , according to the 2018 McKinsey State Of Fashion report. By curbing unnecessary stock, this technology can optimize product development, design and sourcing and curb overproduction.


Only time will tell if the environmental impact of digital fashion innovation will pay off. In the age of artificial intelligence, retailers now have a visible strategy to harness data and artificial intelligence to produce less while increasing profits. H&M’s Zeighami famously declares:  “Stop guessing what you can calculate.” With AI led intelligent production, a new industry model marked by efficiency, transparency and reduced waste may soon become the new standard.