How AI can help any fashion brand balance growth and profitability

Fashion brands face a growth conundrum. On the one hand, appetite for shopping is strong, with younger generations prioritizing it as a form of entertainment.

But demand alone is not enough to fuel brand growth and profitability. Geopolitical uncertainty and macroeconomic volatility are contributing to muted forecasts, with global top-line growth expected to remain between 2% and 4%.

And as for that entertainment? Digitally native competitors like Shein and Temu — brands that have mastered the gamification of shopping — are setting a blistering pace for US and EU market capture.

For brands prioritizing efficient and responsible expansion, strategies like cost cuts and price increases provide diminishing returns. So what’s left?

AI-supported inventory management offers brands a critical yet often overlooked lever for sustainable growth.

The Role of AI in Inventory Management

AI inventory management systems use machine learning (ML) models to modernize longstanding inventory processes. Models are crafted to solve the common issues brands face and are trained using fashion-specific data.

There are two critical components of AI inventory management.

AI demand forecasting helps planning teams identify precisely how much product is required to meet customers’ needs as part of pre-season planning or in-season OTB. These models augment historical sales and other internal data with external data like weather or trends, ultimately increasing accuracy and granularity.

Optimization algorithms enable teams to ensure supply meets demand at each door while adhering to brand-specific requirements like WOS, size curves, or target sell-through. These models complement unique human insight with intelligent, data-driven recommendations, nurturing an art + science approach to common allocation or rebalancing workflows.

Unlike traditional inventory management approaches, AI-powered solutions can adapt dynamically to evolving market trends and consumer preferences, enabling retailers to make data-driven decisions in real time.

Three AI-enabled Strategies for Fashion Brands to Grow Profitably

I. Drive High Growth in New Categories

The key: Rapidly scale based on early demand signals.

Brands often face the challenge of scaling their operations to meet demand for new product categories. AI-driven inventory management systems analyze early demand signals and optimize inventory allocation accordingly.

By identifying best-selling products and implementing postponement strategies, brands can minimize the risk of overstocking. In-the-moment demand signals can then trigger rapid rebalancing to the specific locations where interest is highest.

II. Optimize Replenishment in Mature Categories

The key: Maximize full-price sell-through (FPST) while minimizing stockouts.

For established product categories, maintaining optimal inventory levels is crucial. AI-powered forecasting models excel in this area because of their precision: as specific as the style x color x size x location level.

By leveraging predictive analytics, brands can accurately anticipate future demand fluctuations to avoid over or underbuying, and then adjust their replenishment strategies in real-time. This proactive approach not only improves inventory turnover and reduces carrying costs but also enhances customer satisfaction by ensuring product availability.

III. Create Efficiency in Turnaround Categories

The key: Free up working capital and unlock human efficiency.

In turnaround categories characterized by slow-moving inventory and excess stock, AI-driven inventory management can streamline operations and reduce wastage. Automation of rote tasks frees humans to focus on higher-value, more creative solutions.

By analyzing historical sales data and market trends, AI can identify opportunities to liquidate excess inventory through targeted promotions or markdowns. Optimization algorithms can help minimize overstocking by recommending optimal inventory levels based on demand forecasts and supply chain constraints. This data-driven approach not only improves inventory efficiency but also frees up working capital for investment in high-growth opportunities.

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By harnessing the capabilities of machine learning and predictive analytics, retailers can optimize their inventory processes, minimize stockouts, and maximize profitability. Adopting a proactive approach to inventory management can help any brand react with agility to changing market conditions, a recipe for long-term, healthy growth