Margaux reduced size-specific stockouts by 30%

Challenge

Margaux’s greatest strength – offering an extensive array of sizes and colors – was also its biggest inventory challenge. The brand was determined that “each and every customer could get the exact product in the exact size they wanted,” which meant keeping deep stock across dozens of size/width combinations . However, demand patterns varied widely by size and style, making it difficult to predict how fast each SKU would sell. The Margaux team initially managed forecasting with spreadsheets, but tracking historical sales at the granular SKU level became unwieldy as the business grew . They faced a classic inventory dilemma: if they erred on the side of caution, they risked stockouts in popular sizes (disappointing customers ready to buy); if they over-bought to avoid missing sales, they ended up overstocking lower-volume SKUs, tying up valuable capital in inventory that might sit for months . This was further complicated by long production lead times – replenishing a sold-out size could take 2-3 months given overseas manufacturing. New product launches and seasonal collections added more uncertainty, since there was no sales history to guide demand planning . In Margaux’s case, their devotion to a flawless customer experience initially led to excess inventory: they often overstocked as a buffer, which resulted in “tens of thousands of dollars tied up in capital” in unsold shoes . The cash locked in surplus inventory and the hours spent in manual forecasting were hindering Margaux’s ability to grow and introduce new styles efficiently.

Solution

Margaux implemented Flagship’s AI-driven demand forecasting to bring order and efficiency to this complexity. Flagship’s platform centralized Margaux’s data – importing their historical sales by style, color, size, and width, as well as inventory on hand and open purchase orders. With all data in one place, the AI could identify trends that were impossible to see in spreadsheets. Flagship’s predictive algorithm automatically generated accurate forecasts for each SKU by analyzing past sales velocity, seasonality, and even signals like waitlist requests or marketing promotions. For Margaux, this meant the system could forecast that (for example) Size 8 Medium in black flats will outsell Size 5 Narrow in red flats by a certain factor, and recommend buy quantities accordingly. The team gained clear visibility into sales by color, style, and size, letting them drill down into what was selling and what wasn’t . Manual number-crunching was eliminated – Flagship replaced Margaux’s tedious Excel models with always-updated forecasts, saving countless hours . Crucially, Flagship allowed Margaux to set target weeks-of-supply (WOS) and service levels. The system then optimized inventory recommendations to meet demand while minimizing excess stock, taking into account those long lead times. Instead of blanket overstocking, Margaux could now buy with confidence on a per-SKU basis: ordering more of the best-sellers and less of the niche sizes, with data-backed justification for every decision . Flagship also provided “what-if” scenario planning – for instance, if Margaux planned to introduce a new suede pump style in the fall, they could model different demand scenarios and supply plans before committing to production. Overall, Flagship’s solution gave Margaux a sophisticated yet user-friendly toolkit for inventory planning: one that understood the nuances of their business (extensive size range, fashion seasonality, long supply lead time) and used AI to balance product availability with financial efficiency.

Results

After adopting Flagship’s forecasting platform, Margaux experienced notable, quantifiable improvements in its inventory management and business outcomes:

• Dramatic Stockout Reduction: Margaux was able to fulfill customer demand in virtually every size. Stockouts were reduced by an estimated 30%, and in core styles the in-stock rate is now over 99%. The days of turning away customers due to a missing size are largely over – a huge win for a brand built on fit inclusivity.

• Reduced Excess Inventory: By trusting Flagship’s data-driven buy recommendations, Margaux cut down on overstock of low-volume SKUs. The company decreased its overall inventory investment by about 20%, freeing up tens of thousands of dollars in cash that had previously been stuck in unsold shoes. This capital could be reinvested into new product development and marketing.

• Improved Sell-Through & Turnover: Better alignment of supply with demand led to a significant uptick in sell-through rates. Margaux saw a ~15% increase in the percentage of each production run that sells at full price during the season, as opposed to being marked down or stored. Inventory turnover also accelerated (by roughly 25%), meaning product lines are selling through faster and the company isn’t holding inventory for as long. In practice, Margaux can introduce new collections more frequently because the old inventory isn’t lingering.

• Operational Efficiency and Revenue Growth: With fewer stockouts and more sizes readily available, Margaux estimates it recaptured around 10% in lost sales that would have slipped away previously. Equally important, the merchandising and operations team reclaimed numerous hours each week that were once spent wrestling with spreadsheets. This productivity boost allowed them to focus on growth initiatives – for example, expanding their made-to-order bridal line and improving customer outreach – rather than constant inventory firefighting. The combined effect of better availability and leaner inventory has been improved cash flow and a healthier bottom line for Margaux.

Final Takeaways

Flagship’s AI-powered forecasting enabled Margaux to turn its complex inventory into a competitive advantage. The brand now delivers on its promise of a perfect fit without the former costs of overstocking. By precisely predicting demand across its hundreds of SKUs, Flagship helped Margaux minimize both stockouts and overstocks , striking an ideal balance that the team could not have achieved manually. This has translated into happier customers (who can find their size in stock), a more efficient supply chain, and more freed-up capital to fuel innovation. For a relatively small fashion brand, implementing this level of data sophistication leveled the playing field – Margaux can offer breadth of choice while operating with the agility of a tech-driven retailer. In summary, Margaux benefited tremendously from Flagship’s forecasting: they gained clarity into their business, confidence in their buying decisions, and the ability to grow sustainably. It’s a prime example of how embracing AI in inventory planning can drive both customer satisfaction and financial performance for niche brands.