In the highly competitive fashion industry, brands and retailers face an increasing challenge: accurately forecasting the styles, colors, and sizes that consumers will purchase. This problem becomes even more pronounced given the industry’s lengthy lead times, which can stretch from 37 to 45 weeks. According to Boston Consulting Group research, this means companies are often predicting consumer preferences up to ten months in advance. Such a scenario presents significant financial risk, evidenced by McKinsey’s finding that retailers in the United States were holding $740 billion in unsold inventory in 2023.
The implications of these inaccuracies are profound. Global retailers potentially lost a staggering $1.77 trillion in revenue due to inventory issues, according to IHL Group, taking into account both overstock situations and instances of stockouts. This losses not only impact the bottom line but also contribute to diminishing brand loyalty among consumers frustrated by unavailable items in their desired sizes.
To address this ongoing dilemma, Style Arcade—a retail analytics platform founded in 2018—aims to enhance assortment planning for both brands and retailers. Recognizing that fashion’s overstock issue leads to an annual loss of $163 billion, as reported by Bloomberg Intelligence, Style Arcade has developed an AI-powered buy and size calculator. This tool is designed to improve demand forecasting, enabling fashion companies to make data-driven decisions regarding inventory management.
Currently collaborating with over 120 brands such as Amiri, Aje, and Christopher Esber, Style Arcade reports a substantial increase in gross profit—averaging an additional 4.5 points—arising from improved buying and sizing practices within the first year of implementation. According to Michaela Wessels, the CEO and co-founder of Style Arcade and a former VP of merchandising, having the right inventory is crucial, stating, “If you are left with products that are not in high demand, or inaccurate sizes, then the best marketing in the world won’t save you from getting those two things wrong.”
Historically, retail buyers relied on aggregated data from generic business intelligence tools and complex spreadsheets to make purchase decisions. This often inefficient and time-consuming process overlooks crucial insights about demand, including what a retailer could have sold if not hampered by stock inaccuracies. Therefore, the introduction of AI into the merchandising process is critical. For instance, if a retailer intends to introduce a white, short-sleeved mini dress priced at $199 in April 2025, Style Arcade’s AI can deliver precise recommended order quantities by size and location, optimizing stock levels before they become an issue.
Yet, despite these advancements, effective assortment planning remains a complex puzzle. With countless variables—such as changing consumer preferences, seasonal patterns disrupted by unpredictable weather, and differing sales across geographical locations—manual calculation is often impractical. AI solutions are designed to bridge this performance gap while allowing the human aspect of trend analysis to remain relevant. Wessels emphasizes this human-AI synergy, stating, “AI helps them get to the quantification faster and takes the grind out of the analysis.”
Moreover, addressing sizing inaccuracies can significantly impact profitability. Wessels reveals that brands can lose up to 23% of their profit on a monthly basis due to errors in sizing decisions. Therefore, implementing smarter data analytics can directly contribute to closing significant profit gaps within the fashion industry.
Customer expectations in the fashion sector are evolving rapidly. Today’s shoppers expect not only the availability of their desired sizes but also faster turnaround times for products, including those that are in high demand. The common complaint echoed by consumers continues to be the inability to find their size in stock. This dissatisfaction can drive customers to competitors if their preferences are not met.
Wessels notes, “The number one shopper complaint is still: ‘My size is sold-out’—which indicates how critical this is not only for customer satisfaction and acquisition but also to reduce profit loss from inaccuracy.” As environmental sustainability emerges as a growing concern, companies must also be cognizant of how quickly they can bring products to market without compromising eco-friendliness.
Looking towards the future, the intersection of improved consumer expectation and advanced AI capabilities will shape the directions of the fashion industry. Retailers are being pushed towards hyper-personalization and service excellence, with AI becoming an essential tool in achieving these goals. Those who adapt to the demands of modern consumers will likely emerge stronger in this competitive landscape.
In summary, resolving fashion’s $163 billion buying and sizing inaccuracy problem is paramount for brands aiming to thrive in an increasingly challenging marketplace. By strategically leveraging data through AI tools, retailers can improve inventory accuracy, enhance consumer satisfaction, and ultimately boost their profitability.
FashionInnovationRetailManagementData
