Fashion has an inventory management problem, and it’s only intensified in recent years. Solving these challenges has proven elusive, leaving the industry with billions in unsold stock annually and fuelling a system running on baked-in excess where margin-killing markdowns are the norm.
This is largely due to consumers demanding faster, trend-driven cycles, which reduce lead times and increase the need for brands to accurately forecast demand. Global disruptions, ranging from supply chain delays to unpredictable shifts in consumer behaviour, which stem from events like the pandemic, have also exacerbated forecasting challenges. Traditional demand patterns are no longer reliable, as external factors like unexpected lockdowns, fluctuating consumer spending and the climate crisis have injected further unpredictability. Seasonal volatility has also grown, with more extreme weather events impacting everything from materials sourcing to shipping timelines, making it harder to maintain accurate forecasts.
Can AI help?
A new crop of tech startups has emerged to answer that question, each with its sights set on evolving the Byzantine reality of fashion retail, where critical functions too often rely on error-prone manual data entry and unwieldy spreadsheets. Autone, an AI-powered platform, is one such startup, giving clients like Roberto Cavalli and Courrèges the tools to better forecast demand and optimise stock levels.
Fresh off a $17 million series A funding round led by General Catalyst — a backer of unicorns like Instacart and Airbnb — London-based Autone wants to redefine efficiency for forecasters and demand planners toiling inside fashion, beauty and accessories brands. Born from the first-hand experience of founders Adil Bouhdadi and Harry Glucksmann-Cheslaw during their time at Alexander McQueen, Autone addresses the pain points they encountered managing critical operational functions for the iconic fashion house, where the former — now the startup’s CEO — left as head of decision intelligence, and the latter — who now holds the chief technology officer title — was commercial insights manager.
“We realised that it’s not about creating the best forecast on the planet,” says Bouhdadi. “It’s about giving inventory allocators a platform that tells them which tasks they should execute to achieve their targets and explains why these tasks will accomplish these goals.”
Autone joins a growing number of tech-driven entrants hoping to bring order to fashion’s stock management and demand planning difficulties. Singuli, a notable name in the retail technology sector, similarly offers an AI-powered solution to the perennial challenge of inventory optimisation. Through machine learning, Singuli works with retail clients including Rhone, Cozy Earth and Harper Wilde to accurately forecast demand, streamline stock allocation across multiple channels and automate replenishment, ultimately minimising waste and maximising efficiency. Since it launched in February 2019 and raised a $3.7 million seed round two years later, the startup has been helping businesses manage their stock, which has led to reduced costs, increased sales and enhanced customer satisfaction.
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