FLO reduces lost sales by 12% with invent.ai demand forecasting

FLO, one of Turkey's leading footwear retailers, operates a large multi-store retail network where demand patterns shift rapidly across fashion cycles, seasonal transitions, and promotional events. Traditional statistical forecasting methods could not capture store-level variability — each location carried distinct demand profiles shaped by local demographics, climate, and shopper behavior. The result was chronic inventory imbalance: high-demand SKUs stocked out while slower-moving styles accumulated markdown pressure. These stockouts translated directly into lost revenue, and the inability to act on granular, timely demand signals made network-wide inventory optimization structurally difficult to achieve with existing tools.

FLO partnered with invent.ai to deploy an autonomous demand forecasting platform built on time-series forecasting models operating at store-SKU granularity — the most actionable level for retail replenishment decisions. Rather than producing aggregate category-level projections, the system generates individual demand predictions for each SKU at each store, automatically incorporating seasonality patterns, promotional calendars, weather signals, and emerging consumer trend data. The platform integrated into FLO's existing retail operations without requiring manual data preparation or analyst override at scale. invent.ai's autonomous execution model means the system continuously retrains on fresh sales data, adjusting forecasts as demand conditions evolve. This eliminated the manual forecasting bottleneck and shortened the lag between observed demand shifts and updated inventory signals.

The primary outcome was a 12% reduction in lost sales, reflecting measurably improved in-store availability of the right products at the right locations across the retail network. Forecast accuracy at the store-SKU level enabled procurement and replenishment teams to act on more reliable demand signals, reducing both stockouts and excess inventory simultaneously. Key outcomes:

• 12% reduction in lost sales across the store network
• Store-SKU level forecast granularity achieved, enabling location-specific replenishment decisions
• Manual forecasting overhead eliminated, freeing planning teams for exception management
• Faster response to shifting consumer demand, reducing the gap between trend signals and inventory action

• Forecast granularity at the store-SKU level — not aggregate category — is what unlocks actionable replenishment decisions in multi-location retail.
• Automating signal ingestion (seasonality, promotions, weather) reduces planner burden while improving forecast responsiveness to real-world conditions.
• Track lost sales as the primary KPI for forecasting quality; stockout-driven revenue loss makes the business case concrete and measurable.
• In fashion retail, continuous model retraining on fresh sales data is non-negotiable — periodic forecast cycles cannot keep pace with trend-driven demand shifts.
• Eliminating manual forecasting workflows creates operational capacity, shifting planning teams toward higher-value exception management work.