Start with weekly sell-through by SKU for the last 24 months; if two years of data aren’t available, use comparable category products and channel-level proxies. Decompose the time series into trend, seasonal and residual components (e.g., STL decomposition). Measure seasonal indices (average week-of-year multiplier) to quantify spike magnitude versus baseline. Validate by calculating percent uplift for peak windows (e.g., Nov–Dec holidays, May–June prom/wedding season, Oct Halloween) and use those multipliers when projecting next-year demand for those weeks.

Combine internal POS/OMS sell-through, web traffic and conversion data, paid-media calendar and influencer posting schedules, and marketplace search intent (brand/category clicks). Supplement with macro retail indicators: Google Trends for style terms, fashion editorial calendars, and major retail promo dates (Black Friday, Singles Day, etc.). For B2B forecasting, add supplier lead-time logs and historical production yield to capture fulfillment constraints. Use ensemble signals—sales history as ground truth, traffic and search as leading indicators, and promo/influencer events as causal drivers.

Design models at the SKU-by-channel-by-week granularity. Start with baseline statistical models: exponential smoothing with seasonal components or SARIMA for stable series, and Facebook/Meta Prophet for irregular seasonality and holiday effects. For richer signals, build a gradient-boosted tree or light GBM that ingests lagged sales, promo flags, ad spend, search volume and calendar features. Train on rolling windows and measure MAPE or RMSE on holdout periods (use seasonal holdouts). Use model explainability (SHAP) to ensure promotions or influencer posts aren’t overstated.

Optimal lead time equals supplier production lead time plus transit and buffer for variability. Empirically, define two lead-time values: LT_base (average reorder-to-receive) and LT_sd (standard deviation). For seasonal peaks, add pre-production windows: plan production 6–10 weeks ahead for major holiday windows if shipping by sea; 2–4 weeks may suffice for air or local inventory. Compute safety stock = z * LT_sd * sqrt(LT_base) using a service-level z-value aligned to your target fill rate. Adjust LT assumptions for new SKUs or custom designs, which require longer sampling and tooling windows.

Capture promotion mechanics as explicit model features: discount percent, ad spend, placement channel, creative type, and influencer timing. Build lift curves from historical promo events by comparing treated weeks to baseline weeks (synthetic control or matched windows). Use these lift factors in forecasts rather than naive percent-of-baseline uplifts. For planned promotions, run scenario-based forecasts (baseline, conservative lift, aggressive lift) and size inventory for the highest acceptable risk scenario. Reserve a tactical promo buffer rather than increasing baseline stock permanently.

Monitor leading KPIs: week-over-week changes in search volume and category clicks, add-to-cart and checkout rates, and paid-traffic conversion rates. Track sell-through rate (units sold divided by beginning-of-period inventory) and weeks-of-supply to detect accelerating demand. Use MAPE on short rolling windows to detect model drift; rising forecast error often precedes a demand regime change. Additionally, monitor influencer engagement velocity (impressions, saves) during launches—large spikes often translate to sustained sell-through in 1–2 weeks for beauty accessories.